Subjects -> COMMUNICATIONS (Total: 518 journals)
    - COMMUNICATIONS (446 journals)
    - DIGITAL AND WIRELESS COMMUNICATION (31 journals)
    - HUMAN COMMUNICATION (19 journals)
    - MEETINGS AND CONGRESSES (7 journals)
    - RADIO, TELEVISION AND CABLE (15 journals)

DIGITAL AND WIRELESS COMMUNICATION (31 journals)

Showing 1 - 31 of 31 Journals sorted alphabetically
Ada : A Journal of Gender, New Media, and Technology     Open Access   (Followers: 22)
Advances in Image and Video Processing     Open Access   (Followers: 24)
Communications and Network     Open Access   (Followers: 13)
E-Health Telecommunication Systems and Networks     Open Access   (Followers: 3)
EURASIP Journal on Wireless Communications and Networking     Open Access   (Followers: 14)
Future Internet     Open Access   (Followers: 84)
Granular Computing     Hybrid Journal  
IEEE Transactions on Wireless Communications     Hybrid Journal   (Followers: 25)
IEEE Wireless Communications Letters     Hybrid Journal   (Followers: 41)
IET Wireless Sensor Systems     Open Access   (Followers: 17)
International Journal of Communications, Network and System Sciences     Open Access   (Followers: 9)
International Journal of Digital Earth     Hybrid Journal   (Followers: 14)
International Journal of Embedded and Real-Time Communication Systems     Full-text available via subscription   (Followers: 9)
International Journal of Interactive Communication Systems and Technologies     Full-text available via subscription   (Followers: 2)
International Journal of Machine Intelligence and Sensory Signal Processing     Hybrid Journal   (Followers: 3)
International Journal of Mobile Computing and Multimedia Communications     Full-text available via subscription   (Followers: 2)
International Journal of Satellite Communications and Networking     Hybrid Journal   (Followers: 40)
International Journal of Wireless and Mobile Computing     Hybrid Journal   (Followers: 8)
International Journal of Wireless Networks and Broadband Technologies     Full-text available via subscription   (Followers: 2)
International Journals Digital Communication and Analog Signals     Full-text available via subscription   (Followers: 2)
Journal of Digital Information     Open Access   (Followers: 164)
Journal of Interconnection Networks     Hybrid Journal   (Followers: 1)
Journal of the Southern Association for Information Systems     Open Access   (Followers: 2)
Mobile Media & Communication     Hybrid Journal   (Followers: 10)
Nano Communication Networks     Hybrid Journal   (Followers: 5)
Psychology of Popular Media Culture     Full-text available via subscription   (Followers: 2)
Signal, Image and Video Processing     Hybrid Journal   (Followers: 13)
Ukrainian Information Space     Open Access  
Vehicular Communications     Full-text available via subscription   (Followers: 4)
Vista     Open Access   (Followers: 2)
Wireless Personal Communications     Hybrid Journal   (Followers: 6)
Similar Journals
Journal Cover
Future Internet
Journal Prestige (SJR): 0.219
Citation Impact (citeScore): 1
Number of Followers: 84  

  This is an Open Access Journal Open Access journal
ISSN (Print) 1999-5903
Published by MDPI Homepage  [247 journals]
  • Future Internet, Vol. 15, Pages 40: Acknowledgment to the Reviewers of
           Future Internet in 2022

    • Authors: Future Internet Editorial Office
      First page: 40
      Abstract: High-quality academic publishing is built on rigorous peer review [...]
      Citation: Future Internet
      PubDate: 2023-01-19
      DOI: 10.3390/fi15020040
      Issue No: Vol. 15, No. 2 (2023)
       
  • Future Internet, Vol. 15, Pages 41: Redundancy Mitigation Mechanism for
           Collective Perception in Connected and Autonomous Vehicles

    • Authors: Wellington Lobato, Paulo Mendes, Denis Rosário, Eduardo Cerqueira, Leandro A. Villas
      First page: 41
      Abstract: Due to poor local range of the perception and object recognition mechanisms used by autonomous vehicles, incorrect decisions can be made, which can jeopardize a fully autonomous operation. A connected and autonomous vehicle should be able to combine its local perception with the perceptions of other vehicles to improve its capability to detect and predict obstacles. Such a collective perception system aims to expand the field of view of autonomous vehicles, augmenting their decision-making process, and as a consequence, increasing driving safety. Regardless of the benefits of a collective perception system, autonomous vehicles must intelligently select which data should be shared with who and when in order to conserve network resources and maintain the overall perception accuracy and time usefulness. In this context, the operational impact and benefits of a redundancy reduction mechanism for collective perception among connected autonomous vehicles are analyzed in this article. Therefore, we propose a reliable redundancy mitigation mechanism for collective perception services to reduce the transmission of inefficient messages, which is called VILE. Knowledge, selection, and perception are the three phases of the cooperative perception process developed in VILE. The results have shown that VILE is able to reduce it the absolute number of redundant objects of 75% and generated packets by up to 55%. Finally, we discuss possible research challenges and trends.
      Citation: Future Internet
      PubDate: 2023-01-22
      DOI: 10.3390/fi15020041
      Issue No: Vol. 15, No. 2 (2023)
       
  • Future Internet, Vol. 15, Pages 42: Application-Aware Network Traffic
           Management in MEC-Integrated Industrial Environments

    • Authors: Paolo Bellavista, Mattia Fogli, Carlo Giannelli, Cesare Stefanelli
      First page: 42
      Abstract: The industrial Internet of things (IIoT) has radically modified industrial environments, not only enabling novel industrial applications but also significantly increasing the amount of generated network traffic. Nowadays, a major concern is to support network-intensive industrial applications while ensuring the prompt and reliable delivery of mission-critical traffic flows concurrently traversing the industrial network. To this end, we propose application-aware network traffic management. The goal is to satisfy the requirements of industrial applications through a form of traffic management, the decision making of which is also based on what is carried within packet payloads (application data) in an efficient and flexible way. Our proposed solution targets multi-access edge computing (MEC)-integrated industrial environments, where on-premises and off-premises edge computing resources are used in a coordinated way, as it is expected to be in future Internet scenarios. The technical pillars of our solution are edge-powered in-network processing (eINP) and software-defined networking (SDN). The concept of eINP differs from INP because the latter is directly performed on network devices (NDs), whereas the former is performed on edge nodes connected via high-speed links to NDs. The rationale of eINP is to provide the network with additional capabilities for packet payload inspection and processing through edge computing, either on-premises or in the MEC-enabled cellular network. The reported in-the-field experimental results show the proposal feasibility and its primary tradeoffs in terms of performance and confidentiality.
      Citation: Future Internet
      PubDate: 2023-01-22
      DOI: 10.3390/fi15020042
      Issue No: Vol. 15, No. 2 (2023)
       
  • Future Internet, Vol. 15, Pages 43: Engineering Resource-Efficient Data
           Management for Smart Cities with Apache Kafka

    • Authors: Theofanis P. Raptis, Claudio Cicconetti, Manolis Falelakis, Grigorios Kalogiannis, Tassos Kanellos, Tomás Pariente Lobo
      First page: 43
      Abstract: In terms of the calibre and variety of services offered to end users, smart city management is undergoing a dramatic transformation. The parties involved in delivering pervasive applications can now solve key issues in the big data value chain, including data gathering, analysis, and processing, storage, curation, and real-world data visualisation. This trend is being driven by Industry 4.0, which calls for the servitisation of data and products across all industries, including the field of smart cities, where people, sensors, and technology work closely together. In order to implement reactive services such as situational awareness, video surveillance, and geo-localisation while constantly preserving the safety and privacy of affected persons, the data generated by omnipresent devices needs to be processed fast. This paper proposes a modular architecture to (i) leverage cutting-edge technologies for data acquisition, management, and distribution (such as Apache Kafka and Apache NiFi); (ii) develop a multi-layer engineering solution for revealing valuable and hidden societal knowledge in the context of smart cities processing multi-modal, real-time, and heterogeneous data flows; and (iii) address the key challenges in tasks involving complex data flows and offer general guidelines to solve them. In order to create an effective system for the monitoring and servitisation of smart city assets with a scalable platform that proves its usefulness in numerous smart city use cases with various needs, we deduced some guidelines from an experimental setting performed in collaboration with leading industrial technical departments. Ultimately, when deployed in production, the proposed data platform will contribute toward the goal of revealing valuable and hidden societal knowledge in the context of smart cities.
      Citation: Future Internet
      PubDate: 2023-01-22
      DOI: 10.3390/fi15020043
      Issue No: Vol. 15, No. 2 (2023)
       
  • Future Internet, Vol. 15, Pages 44: Attention-Enriched Mini-BERT Fake News
           Analyzer Using the Arabic Language

    • Authors: Husam M. Alawadh, Amerah Alabrah, Talha Meraj, Hafiz Tayyab Rauf
      First page: 44
      Abstract: Internet use resulted in people becoming more reliant on social media. Social media have become the main source of fake news or rumors. They spread uncertainty in each sector of the real world, whether in politics, sports, or celebrities’ lives—all are affected by the uncontrolled behavior of social media platforms. Intelligent methods used to control this fake news in various languages have already been much discussed and frequently proposed by researchers. However, Arabic grammar and language are a far more complex and crucial language to learn. Therefore, work on Arabic fake-news-based datasets and related studies is much needed to control the spread of fake news on social media and other Internet media. The current study uses a recently published dataset of Arabic fake news annotated by experts. Further, Arabic-language-based embeddings are given to machine learning (ML) classifiers, and the Arabic-language-based trained minibidirectional encoder representations from transformers (BERT) is used to obtain the sentiments of Arabic grammar and feed a deep learning (DL) classifier. The holdout validation schemes are applied to both ML classifiers and mini-BERT-based deep neural classifiers. The results show a consistent improvement in the performance of mini-BERT-based classifiers, which outperformed ML classifiers, by increasing the training data. A comparison with previous Arabic fake news detection studies is shown where results of the current study show greater improvement.
      Citation: Future Internet
      PubDate: 2023-01-22
      DOI: 10.3390/fi15020044
      Issue No: Vol. 15, No. 2 (2023)
       
  • Future Internet, Vol. 15, Pages 45: Digitalization of Distribution
           Transformer Failure Probability Using Weibull Approach towards Digital
           Transformation of Power Distribution Systems

    • Authors: A. M. Sakura R. H. Attanayake, R. M. Chandima Ratnayake
      First page: 45
      Abstract: Digitalization of the failure-probability modeling of crucial components in power-distribution systems is important for improving risk and reliability analysis for system-maintenance and asset-management practices. This paper aims to implement a Python programming-based Weibull approach for digitalization of distribution-transformer (DT) failures, considering a regional section of DTs in Sri Lanka as a case study. A comprehensive analysis for DT-failure data for six years has been utilized to derive a Weibull distribution analysis for DTs. The interpretation of the resulting beta and alpha parameters of the Weibull analysis for different categories of DTs in the selected region is also presented. The resulting data can be uploaded to computerized maintenance-management systems (CMMS), to adopt conclusions or resolutions reached by the asset and maintenance managers. Ultimately, failure-probability modeling is beneficial for decision-making processes for higher management aiming for the digital transformation of power-distribution systems.
      Citation: Future Internet
      PubDate: 2023-01-25
      DOI: 10.3390/fi15020045
      Issue No: Vol. 15, No. 2 (2023)
       
  • Future Internet, Vol. 15, Pages 46: Prediction of Energy Production Level
           in Large PV Plants through AUTO-Encoder Based Neural-Network (AUTO-NN)
           with Restricted Boltzmann Feature Extraction

    • Authors: Ramesh, Logeshwaran, Kiruthiga, Lloret
      First page: 46
      Abstract: In general, reliable PV generation prediction is required to increase complete control quality and avoid potential damage. Accurate forecasting of direct solar radiation trends in PV power production could limit the influence of uncertainties on photovoltaics, enhance organizational dependability, and maximize the utilization factor of the PV systems for something such as an energy management system (EMS) of microgrids. This paper proposes an intelligent prediction of energy production level in large PV plants through AUTO-encoder-based Neural-Network (AUTO-NN) with Restricted Boltzmann feature extraction. Here, the solar energy output may be projected using prior sun illumination and meteorological data. The feature selection and prediction modules use an AUTO encoder-based Neural Network to improve the process of energy prediction (AUTO-NN). Restricted Boltzmann Machines (RBM) can be used during a set of regulations for development-based feature extraction. The proposed model’s result is evaluated using various constraints. As a result, the proposed AUTO-NN achieved 58.72% of RMSE (Root Mean Square Error), 62.72% of n-RMSE (Normalized Root Mean Square Error), 48.04% of Max-AE (Maximum Absolute Error), 48.66% of (Mean Absolute Error), and 46.76% of (Mean Absolute Percentage Error).
      Citation: Future Internet
      PubDate: 2023-01-26
      DOI: 10.3390/fi15020046
      Issue No: Vol. 15, No. 2 (2023)
       
  • Future Internet, Vol. 15, Pages 47: Optimal Mobility-Aware Wireless Edge
           Cloud Support for the Metaverse

    • Authors: Zhaohui Huang, Vasilis Friderikos
      First page: 47
      Abstract: Mobile-augmented-reality (MAR) applications extended into the metaverse could provide mixed and immersive experiences by amalgamating the virtual and physical worlds. However, the consideration of joining MAR and the metaverse requires reliable and high-quality support for foreground interactions and rich background content from these applications, which intensifies their consumption of energy, caching and computing resources. To tackle these challenges, a more flexible request assignment and resource allocation framework with more efficient processing are proposed in this paper through anchoring decomposed metaverse AR services at different edge nodes and proactively caching background metaverse region models embedded with target augmented-reality objects (AROs). Advanced terminals are also considered to further reduce service delays at an acceptable energy-consumption cost. We, then, propose and solve a joint-optimization problem which explicitly considers the balance between service delay and energy consumption under the constraints of perceived user quality in a mobility event. By also explicitly taking into account the capabilities of user terminals, the proposed optimized scheme is compared to a terminal-oblivious scheme. According to a wide set of numerical investigations, the proposed scheme has wide-ranging advantages in service latency and energy efficiency over other nominal baseline schemes which neglect the capabilities of terminals, user physical mobility, service decomposition and the inherent multimodality of the metaverse MAR service.
      Citation: Future Internet
      PubDate: 2023-01-26
      DOI: 10.3390/fi15020047
      Issue No: Vol. 15, No. 2 (2023)
       
  • Future Internet, Vol. 15, Pages 48: A TOSCA-Based Conceptual Architecture
           to Support the Federation of Heterogeneous MSaaS Infrastructure

    • Authors: Paolo Bocciarelli, Andrea D’Ambrogio
      First page: 48
      Abstract: Modeling and simulation (M&S) techniques are effectively used in many application domains to support various operational tasks ranging from system analyses to innovative training activities. Any (M&S) effort might strongly benefit from the adoption of service orientation and cloud computing to ease the development and provision of M&S applications. Such an emerging paradigm is commonly referred to as M&S-as-a-Service (MSaaS). The need for orchestrating M&S services provided by different partners in a heterogeneous cloud infrastructure introduces new challenges. In this respect, the adoption of an effective architectural approach might significantly help the design and development of MSaaS infrastructure implementations that cooperate in a federated environment. In this context, this work introduces a MSaaS reference architecture (RA) that aims to investigate innovative approaches to ease the building of inter-cloud MSaaS applications. Moreover, this work presents ArTIC-MS, a conceptual architecture that refines the proposed RA for introducing the TOSCA (topology and orchestration specification for cloud applications) standard. ArTIC-MS’s main objective is to enable effective portability and interoperability among M&S services provided by different partners in heterogeneous federations of cloud-based MSaaS infrastructure. To show the validity of the proposed architectural approach, the results of concrete experimentation are provided.
      Citation: Future Internet
      PubDate: 2023-01-26
      DOI: 10.3390/fi15020048
      Issue No: Vol. 15, No. 2 (2023)
       
  • Future Internet, Vol. 15, Pages 25: A V2V Identity Authentication and Key
           Agreement Scheme Based on Identity-Based Cryptograph

    • Authors: Qiang Li
      First page: 25
      Abstract: Cellular vehicle to everything (C-V2X) is a technology to achieve vehicle networking, which can improve traffic efficiency and traffic safety. As a special network, the C-V2X system faces many security risks. The vehicle to vehicle (V2V) communication transmits traffic condition data, driving path data, user driving habits data, and so on. It is necessary to ensure the opposite equipment is registered C-V2X equipment (installed in the vehicle), and the data transmitted between the equipment is secure. This paper proposes a V2V identity authentication and key agreement scheme based on identity-based cryptograph (IBC). The C-V2X equipment use its vehicle identification (VID) as its public key. The key management center (KMC) generates a private key for the C-V2X equipment according to its VID. The C-V2X equipment transmit secret data encrypted with the opposite equipment public key to the other equipment, they authenticate each other through a challenge response protocol based on identity-based cryptography, and they negotiate the working key used to encrypt the communication data. The scheme can secure the V2V communication with low computational cost and simple architecture and meet the lightweight and efficient communication requirements of the C-V2X system.
      Citation: Future Internet
      PubDate: 2023-01-03
      DOI: 10.3390/fi15010025
      Issue No: Vol. 15, No. 1 (2023)
       
  • Future Internet, Vol. 15, Pages 26: Clinical Screening Prediction in the
           Portuguese National Health Service: Data Analysis, Machine Learning
           Models, Explainability and Meta-Evaluation

    • Authors: Teresa Gonçalves, Rute Veladas, Hua Yang, Renata Vieira, Paulo Quaresma, Paulo Infante, Cátia Sousa Pinto, João Oliveira, Maria Cortes Ferreira, Jéssica Morais, Ana Raquel Pereira, Nuno Fernandes, Carolina Gonçalves
      First page: 26
      Abstract: This paper presents an analysis of the calls made to the Portuguese National Health Contact Center (SNS24) during a three years period. The final goal was to develop a system to help nurse attendants select the appropriate clinical pathway (from 59 options) for each call. It examines several aspects of the calls distribution like age and gender of the user, date and time of the call and final referral, among others and presents comparative results for alternative classification models (SVM and CNN) and different data samples (three months, one and two years data models). For the task of selecting the appropriate pathway, the models, learned on the basis of the available data, achieved F1 values that range between 0.642 (3 months CNN model) and 0.783 (2 years CNN model), with SVM having a more stable performance (between 0.743 and 0.768 for the corresponding data samples). These results are discussed regarding error analysis and possibilities for explaining the system decisions. A final meta evaluation, based on a clinical expert overview, compares the different choices: the nurse attendants (reference ground truth), the expert and the automatic decisions (2 models), revealing a higher agreement between the ML models, followed by their agreement with the clinical expert, and minor agreement with the reference.
      Citation: Future Internet
      PubDate: 2023-01-03
      DOI: 10.3390/fi15010026
      Issue No: Vol. 15, No. 1 (2023)
       
  • Future Internet, Vol. 15, Pages 27: Transfer Functions and Linear
           Distortions in Ultra-Wideband Channels Faded by Rain in GeoSurf Satellite
           Constellations

    • Authors: Emilio Matricciani, Carlo Riva
      First page: 27
      Abstract: Because of rain attenuation, the equivalent baseband transfer function of large bandwidth radio-links will not be ideal. We report the results concerning radio links to/from satellites orbiting in GeoSurf satellite constellations located at Spino d’Adda, Prague, Madrid, and Tampa, which are all sites in different climatic regions. By calculating rain attenuation and phase delay with the Synthetic Storm Technique, we have found that in a 10-GHz bandwidth centered at 80 GHz (W-Band)—to which we refer to as “ultra-wideband-, both direct and orthogonal channels will introduce significant amplitude and phase distortions, which increase with rain attenuation. Only “narrow-band” channels (100~200 MHz) will not be affected. The ratio between the probability of bit error with rain attenuation and the probability of bit error with no rain attenuation increases with rain attenuation. The estimated loss in the signal-to-noise ratio can reach 3~4 dB. All results depend on the site, Tampa being the worst. To confirm these findings, future work will need a full Monte Carlo digital simulation.
      Citation: Future Internet
      PubDate: 2023-01-03
      DOI: 10.3390/fi15010027
      Issue No: Vol. 15, No. 1 (2023)
       
  • Future Internet, Vol. 15, Pages 28: Adapting Recommendations on
           Environmental Education Programs

    • Authors: Katerina Kabassi, Anastasia Papadaki, Athanasios Botonis
      First page: 28
      Abstract: Stakeholders in Environmental Education (EE) often face difficulties identifying and selecting programs that best suit their needs. This is due, in part, to the lack of expertise in evaluation knowledge and practice, as well as to the absence of a unified database of Environmental Education Programs (EEPs) with a defined structure. This article presents the design and development of a web application for evaluating and selecting EEPs. The certified users of the application can insert, view, and evaluate the registered EEPs. At the same time, the application creates and maintains for each user an individual and dynamic user model reflecting their personal preferences. Finally, using all the above information and applying a combination of Multi-Criteria Decision-Making Methods (MCDM), the application provides a comparative and adaptive evaluation in order to help each user to select the EEPs that best suit his/her needs. The personalized recommendations are based on the information about the user stored in the user model and the results of the EEPs evaluations by the users that have applied them. As a case study, we used the EEPs from the Greek Educational System.
      Citation: Future Internet
      PubDate: 2023-01-04
      DOI: 10.3390/fi15010028
      Issue No: Vol. 15, No. 1 (2023)
       
  • Future Internet, Vol. 15, Pages 29: Role of Attention and Design Cues for
           Influencing Cyber-Sextortion Using Social Engineering and Phishing Attacks
           

    • Authors: Brent Pethers, Abubakar Bello
      First page: 29
      Abstract: Cyber sextortion attacks are security and privacy threats delivered to victims online, to distribute sexual material in order to force the victim to act against their will. This continues to be an under-addressed concern in society. This study investigated social engineering and phishing email design and influence techniques in susceptibility to cyber sextortion attacks. Using a quantitative methodology, a survey measured susceptibility to cyber sextortion with a focus on four different email design cues. One-way repeated measures ANOVA, post hoc comparison tests, Friedman nonparametric test, and Spearman correlation tests were conducted with results indicating that attention to email source and title/subject line significantly increased individuals’ susceptibility, while attention to grammar and spelling, and urgency cues, had lesser influence. As such, the influence of these message-related factors should be considered when implementing effective security controls to mitigate the risks and vulnerabilities to cyber sextortion attacks.
      Citation: Future Internet
      PubDate: 2023-01-07
      DOI: 10.3390/fi15010029
      Issue No: Vol. 15, No. 1 (2023)
       
  • Future Internet, Vol. 15, Pages 30: Time Segmentation-Based Hybrid Caching
           in 5G-ICN Bearer Network

    • Authors: Ke Zhao, Rui Han, Xu Wang
      First page: 30
      Abstract: The fifth-generation communication technology (5G) and information-centric networks (ICNs) are acquiring more and more attention. Cache plays a significant part in the 5G-ICN architecture that the industry has suggested. 5G mobile terminals switch between different base stations quickly, creating a significant amount of traffic and a significant amount of network latency. This brings great challenges to 5G-ICN mobile cache. It appears urgent to improve the cache placement strategy. This paper suggests a hybrid caching strategy called time segmentation-based hybrid caching (TSBC) strategy, based on the 5G-ICN bearer network infrastructure. A base station’s access frequency can change throughout the course of the day due to the “tidal phenomena” of mobile networks. To distinguish the access frequency, we split each day into periods of high and low liquidity. To maintain the diversity of cache copies during periods of high liquidity, we replace the path’s least-used cache copy. We determine the cache value of each node in the path and make caching decisions during periods of low liquidity to make sure users can access the content they are most interested in quickly. The simulation results demonstrate that the proposed strategy has a positive impact on both latency and the cache hit ratio.
      Citation: Future Internet
      PubDate: 2023-01-07
      DOI: 10.3390/fi15010030
      Issue No: Vol. 15, No. 1 (2023)
       
  • Future Internet, Vol. 15, Pages 31: Product Evaluation Prediction Model
           Based on Multi-Level Deep Feature Fusion

    • Authors: Qingyan Zhou, Hao Li, Youhua Zhang, Junhong Zheng
      First page: 31
      Abstract: Traditional product evaluation research is to collect data through questionnaires or interviews to optimize product design, but the whole process takes a long time to deploy and cannot fully reflect the market situation. Aiming at this problem, we propose a product evaluation prediction model based on multi-level deep feature fusion of online reviews. It mines product satisfaction from the massive reviews published by users on e-commerce websites, and uses this model to analyze the relationship between design attributes and customer satisfaction, design products based on customer satisfaction. Our proposed model can be divided into the following four parts: First, the DSCNN (Depthwise Separable Convolutions) layer and pooling layer are used to combine extracting shallow features from the primordial data. Secondly, CBAM (Convolutional Block Attention Module) is used to realize the dimension separation of features, enhance the expressive ability of key features in the two dimensions of space and channel, and suppress the influence of redundant information. Thirdly, BiLSTM (Bidirectional Long Short-Term Memory) is used to overcome the complexity and nonlinearity of product evaluation prediction, output the predicted result through the fully connected layer. Finally, using the global optimization capability of the genetic algorithm, the hyperparameter optimization of the model constructed above is carried out. The final forecasting model consists of a series of decision rules that avoid model redundancy and achieve the best forecasting effect. It has been verified that the method proposed in this paper is better than the above-mentioned models in five evaluation indicators such as MSE, MAE, RMSE, MAPE and SMAPE, compared with Support Vector Regression (SVR), DSCNN, BiLSTM and DSCNN-BiLSTM. By predicting customer emotional satisfaction, it can provide accurate decision-making suggestions for enterprises to design new products.
      Citation: Future Internet
      PubDate: 2023-01-09
      DOI: 10.3390/fi15010031
      Issue No: Vol. 15, No. 1 (2023)
       
  • Future Internet, Vol. 15, Pages 32: Image of a City through Big Data
           Analytics: Colombo from the Lens of Geo-Coded Social Media Data

    • Authors: Sandulika Abesinghe, Nayomi Kankanamge, Tan Yigitcanlar, Surabhi Pancholi
      First page: 32
      Abstract: The image of a city represents the sum of beliefs, ideas, and impressions that people have of that city. Mostly, city images are assessed through direct or indirect interviews and cognitive mapping exercises. Such methods consume more time and effort and are limited to a small number of people. However, recently, people tend to use social media to express their thoughts and experiences of a place. Taking this into consideration, this paper attempts to explore city images through social media big data, considering Colombo, Sri Lanka, as the testbed. The aim of the study is to examine the image of a city through Lynchian elements—i.e., landmarks, paths, nodes, edges, and districts—by using community sentiments expressed and images posted on social media platforms. For that, this study conducted various analyses—i.e., descriptive, image processing, sentiment, popularity, and geo-coded social media analyses. The study findings revealed that: (a) the community sentiments toward the same landmarks, paths, nodes, edges, and districts change over time; (b) decisions related to locating landmarks, paths, nodes, edges, and districts have a significant impact on community cognition in perceiving cities; and (c) geo-coded social media data analytics is an invaluable approach to capture the image of a city. The study informs urban authorities in their placemaking efforts by introducing a novel methodological approach to capture an image of a city.
      Citation: Future Internet
      PubDate: 2023-01-09
      DOI: 10.3390/fi15010032
      Issue No: Vol. 15, No. 1 (2023)
       
  • Future Internet, Vol. 15, Pages 33: Abstracting Data in Distributed Ledger
           Systems for Higher Level Analytics and Visualizations

    • Authors: Leny Vinceslas, Safak Dogan, Srikumar Sundareshwar, Ahmet M. Kondoz
      First page: 33
      Abstract: By design, distributed ledger technologies persist low-level data, which makes conducting complex business analysis of the recorded operations challenging. Existing blockchain visualization and analytics tools such as block explorers tend to rely on this low-level data and complex interfacing to provide an enriched level of analytics. The ability to derive richer analytics could be improved through the availability of a higher level abstraction of the data. This article proposes an abstraction layer architecture that enables the design of high-level analytics of distributed ledger systems and the decentralized applications that run on top. Based on the analysis of existing initiatives and identification of the relevant user requirements, this work aims to establish key insights and specifications to improve the auditability and intuitiveness of distributed ledger systems by leveraging the development of future user interfaces. To illustrate the benefits offered by the proposed abstraction layer architecture, a regulated sector use case is explored.
      Citation: Future Internet
      PubDate: 2023-01-11
      DOI: 10.3390/fi15010033
      Issue No: Vol. 15, No. 1 (2023)
       
  • Future Internet, Vol. 15, Pages 34: Deep Reinforcement Learning Evolution
           Algorithm for Dynamic Antenna Control in Multi-Cell Configuration HAPS
           System

    • Authors: Siyuan Yang, Mondher Bouazizi, Tomoaki Ohtsuki, Yohei Shibata, Wataru Takabatake, Kenji Hoshino, Atsushi Nagate
      First page: 34
      Abstract: In this paper, we propose a novel Deep Reinforcement Learning Evolution Algorithm (DRLEA) method to control the antenna parameters of the High-Altitude Platform Station (HAPS) mobile to reduce the number of low-throughput users. Considering the random movement of the HAPS caused by the winds, the throughput of the users might decrease. Therefore, we propose a method that can dynamically adjust the antenna parameters based on the throughput of the users in the coverage area to reduce the number of low-throughput users by improving the users’ throughput. Different from other model-based reinforcement learning methods, such as the Deep Q Network (DQN), the proposed method combines the Evolution Algorithm (EA) with Reinforcement Learning (RL) to avoid the sub-optimal solutions in each state. Moreover, we consider non-uniform user distribution scenarios, which are common in the real world, rather than ideal uniform user distribution scenarios. To evaluate the proposed method, we do the simulations under four different real user distribution scenarios and compare the proposed method with the conventional EA and RL methods. The simulation results show that the proposed method effectively reduces the number of low throughput users after the HAPS moves.
      Citation: Future Internet
      PubDate: 2023-01-12
      DOI: 10.3390/fi15010034
      Issue No: Vol. 15, No. 1 (2023)
       
  • Future Internet, Vol. 15, Pages 35: Redactable Blockchain: Comprehensive
           Review, Mechanisms, Challenges, Open Issues and Future Research Directions
           

    • Authors: Shams Mhmood Abd Ali, Mohd Najwadi Yusoff, Hasan Falah Hasan
      First page: 35
      Abstract: The continuous advancements of blockchain applications impose constant improvements on their technical features. Particularly immutability, a highly secure blockchain attribute forbidding unauthorized or illicit data editing or deletion, which functions as crucial blockchain security. Nonetheless, the security function is currently being challenged due to improper data stored, such as child pornography, copyright violation, and lately the enaction of the “Right to be Forgotten (RtbF)” principle disseminated by the General Data Protection Regulation (GDPR), where it requires blockchain data to be redacted to suit current applications’ urgent demands, and even compliance with the regulation is a challenge and an unfeasible practice for various blockchain technology providers owing to the immutability characteristic. To overcome this challenge, mutable blockchain is highly demanded to solve previously mentioned issues, where controlled and supervised amendments to certain content within constrained privileges granted are suggested by several researchers through numerous blockchain redaction mechanisms using chameleon and non-chameleon hashing function approaches, and methods were proposed to achieve reasonable policies while ensuring high blockchain security levels. Accordingly, the current study seeks to thoroughly define redaction implementation challenges and security properties criteria. The analysis performed has mapped these criteria with chameleon-based research methodologies, technical approaches, and the latest cryptographic techniques implemented to resolve the challenge posed by the policy in which comparisons paved current open issues, leading to shaping future research directions in the scoped field.
      Citation: Future Internet
      PubDate: 2023-01-12
      DOI: 10.3390/fi15010035
      Issue No: Vol. 15, No. 1 (2023)
       
  • Future Internet, Vol. 15, Pages 36: A Multi-Agent Approach to Binary
           Classification Using Swarm Intelligence

    • Authors: Sean Grimes, David E. Breen
      First page: 36
      Abstract: Wisdom-of-Crowds-Bots (WoC-Bots) are simple, modular agents working together in a multi-agent environment to collectively make binary predictions. The agents represent a knowledge-diverse crowd, with each agent trained on a subset of available information. A honey-bee-derived swarm aggregation mechanism is used to elicit a collective prediction with an associated confidence value from the agents. Due to their multi-agent design, WoC-Bots can be distributed across multiple hardware nodes, include new features without re-training existing agents, and the aggregation mechanism can be used to incorporate predictions from other sources, thus improving overall predictive accuracy of the system. In addition to these advantages, we demonstrate that WoC-Bots are competitive with other top classification methods on three datasets and apply our system to a real-world sports betting problem, producing a consistent return on investment from 1 January 2021 through 15 November 2022 on most major sports.
      Citation: Future Internet
      PubDate: 2023-01-12
      DOI: 10.3390/fi15010036
      Issue No: Vol. 15, No. 1 (2023)
       
  • Future Internet, Vol. 15, Pages 37: Cost-Profiling Microservice
           Applications Using an APM Stack

    • Authors: Sjouke de Vries, Frank Blaauw, Vasilios Andrikopoulos
      First page: 37
      Abstract: Understanding how the different parts of a cloud-native application contribute to its operating expenses is an important step towards optimizing this cost. However, with the adoption and rollout of microservice architectures, the gathering of the necessary data becomes much more involved and nuanced due to the distributed and heterogeneous nature of these architectures. Existing solutions for this purpose are either closed-source and proprietary or focus only on the infrastructural footprint of the applications. In response to that, in this work, we present a cost-profiling solution aimed at Kubernetes-based microservice applications, building on a popular open-source application performance monitoring (APM) stack. By means of a case study with a data engineering company, we demonstrate how our proposed solution can provide deeper insights into the cost profile of the various application components and drive informed decision-making in managing the deployment of the application.
      Citation: Future Internet
      PubDate: 2023-01-13
      DOI: 10.3390/fi15010037
      Issue No: Vol. 15, No. 1 (2023)
       
  • Future Internet, Vol. 15, Pages 38: Blockchain, Quo Vadis' Recent
           Changes in Perspectives on the Application of Technology in Agribusiness

    • Authors: Geneci da Silva Ribeiro Rocha, Diego Durante Mühl, Hermenegildo Almeida Chingamba, Letícia de Oliveira, Edson Talamini
      First page: 38
      Abstract: Information technologies such as blockchain are developing fast, overcoming bottlenecks, and quickly taking advantage of their application. The present study analyzes recent changes concerning the benefits, disadvantages, challenges, and opportunities of blockchain applications in agribusiness. Interviews were conducted with and a questionnaire was applied to professionals working in the development and application of blockchain technology in agribusiness, to compare their perception of the recent advances. The results showed that the importance of blockchain technology to improve governance and information flow along supply chains has increased, and this is the main perceived benefit. The main disadvantages were removing intermediaries and the high cost of implementing the technology. The absence of a widely accepted platform in blockchain operations is the leading and growing challenge, while patterns for blockchain technology seem to be being overcome. The integration of blockchain with new technologies, and the competitiveness provided by the technology, are seen as the main and growing opportunities. Despite the study limitations, we conclude that the benefits and opportunities associated with blockchain application in agribusiness outweigh the challenges and disadvantages in number and importance, and are becoming more relevant.
      Citation: Future Internet
      PubDate: 2023-01-16
      DOI: 10.3390/fi15010038
      Issue No: Vol. 15, No. 1 (2023)
       
  • Future Internet, Vol. 15, Pages 39: Using Metaheuristics (SA-MCSDN)
           Optimized for Multi-Controller Placement in Software-Defined Networking

    • Authors: Neamah S. Radam, Sufyan T. Faraj Al-Janabi, Khalid Sh. Jasim
      First page: 39
      Abstract: The multi-controller placement problem (MCPP) represents one of the most challenging issues in software-defined networks (SDNs). High-efficiency and scalable optimized solutions can be achieved for a given position in such networks, thereby enhancing various aspects of programmability, configuration, and construction. In this paper, we propose a model called simulated annealing for multi-controllers in SDN (SA-MCSDN) to solve the problem of placing multiple controllers in appropriate locations by considering estimated distances and distribution times among the controllers, as well as between controllers and switches (C2S). We simulated the proposed mathematical model using Network Simulator NS3 in the Linux Ubuntu environment to extract the performance results. We then compared the results of this single-solution algorithm with those obtained by our previously proposed multi-solution harmony search particle swarm optimization (HS-PSO) algorithm. The results reveal interesting aspects of each type of solution. We found that the proposed model works better than previously proposed models, according to some of the metrics upon which the network relies to achieve optimal performance. The metrics considered in this work are propagation delay, round-trip time (RTT), matrix of time session (TS), average delay, reliability, throughput, cost, and fitness value. The simulation results presented herein reveal that the proposed model achieves high reliability and satisfactory throughput with a short access time standard, addressing the issues of scalability and flexibility and achieving high performance to support network efficiency.
      Citation: Future Internet
      PubDate: 2023-01-16
      DOI: 10.3390/fi15010039
      Issue No: Vol. 15, No. 1 (2023)
       
  • Future Internet, Vol. 15, Pages 1: Machine Learning Failure-Aware Scheme
           for Profit Maximization in the Cloud Market

    • Authors: Bashar Igried, Atalla Fahed Al-Serhan, Ayoub Alsarhan, Mohammad Aljaidi, Amjad Aldweesh
      First page: 1
      Abstract: A successful cloud trading system requires suitable financial incentives for all parties involved. Cloud providers in the cloud market provide computing services to clients in order to perform their tasks and earn extra money. Unfortunately, the applications in the cloud are prone to failure for several reasons. Cloud service providers are responsible for managing the availability of scheduled computing tasks in order to provide high-level quality of service for their customers. However, the cloud market is extremely heterogeneous and distributed, making resource management a challenging problem. Protecting tasks against failure is a challenging and non-trivial mission due to the dynamic, heterogeneous, and largely distributed structure of the cloud environment. The existing works in the literature focus on task failure prediction and neglect the remedial (post) actions. To address these challenges, this paper suggests a fault-tolerant resource management scheme for the cloud computing market in which the optimal amount of computing resources is extracted at each system epoch to replace failed machines. When a cloud service provider detects a malfunctioning machine, they transfer the associated work to new machinery.
      Citation: Future Internet
      PubDate: 2022-12-20
      DOI: 10.3390/fi15010001
      Issue No: Vol. 15, No. 1 (2022)
       
  • Future Internet, Vol. 15, Pages 2: Utility of Sharing Economy Principles
           in the Development of Applications Dedicated to Construction Cost
           Estimation

    • Authors: Adrian Sfichi, Eduard Zadobrischi, Narcisa Sfichi, Marian Bădeliță, Mihai Medrihan
      First page: 2
      Abstract: This research aims to highlight the importance and notoriety that an application based on heuristic algorithms can have in the field of e-commerce in the construction niche, guiding us on participatory economy principles. The expansion of e-commerce has shaped a new directive and increased the complexity of logistics, being a topical and critical issue. Users want the goods to be delivered in a timely manner to the specified address and to benefit from the fastest services. These aspects are challenging to achieve given that most operations fall within the remit of specialized staff within an e-commerce company. In this context, a service-type software application dedicated to the construction field was created to increase productivity, applying the principles of the sharing economy and developing intelligent algorithms. Coestim is a cloud-based SaaS solution for construction work estimations and a marketplace for construction-market-related products. Equipment rental, specialists, tracking the traceability process, generating a quote, and increasing productivity are essential components of the developed application.
      Citation: Future Internet
      PubDate: 2022-12-21
      DOI: 10.3390/fi15010002
      Issue No: Vol. 15, No. 1 (2022)
       
  • Future Internet, Vol. 15, Pages 3: Ethereum-Based Information System for
           Digital Higher Education Registry and Verification of Student Achievement
           Documents

    • Authors: Yerlan Kistaubayev, Galimkair Mutanov, Madina Mansurova, Zhanna Saxenbayeva, Yassynzhan Shakan
      First page: 3
      Abstract: Blockchain is a new and modern technology that is gradually being used in various fields due to its ability to decentralize and organize secure and reliable data exchange and storage. One of the related research areas generating increasing interest is the field of education, with particular focus on the digitization and automation of educational management processes and the ability to store and verify digital documents about student progress. The main goal of this study is to develop a platform that creates a unified digital register of students’ educational achievements, which is one of the most pressing issues in the field of education, based on the Ethereum blockchain architecture. Blockchain is expensive; therefore, there is a need to consider performance criteria when evaluating any decision made about the technology, especially the most important aspects such as predicting traffic behavior, estimating transaction costs and providing the necessary indicators of system quality and functionality. However, most research ignores the evaluation of performance indicators, such as throughput, the speed of transactions and the amount of data stored in the Ethereum blockchain database, which are the main evaluation criteria. This paper aims to eliminate this gap by evaluating the performance of the developed platform and by discussing the obtained experimental results. Thus, the main results of this work are the design and deployment of a blockchain platform and the analysis of its transaction costs. We conclude that the proposed blockchain solution is applicable as a system for the accounting and verification of loans and student academic achievements.
      Citation: Future Internet
      PubDate: 2022-12-22
      DOI: 10.3390/fi15010003
      Issue No: Vol. 15, No. 1 (2022)
       
  • Future Internet, Vol. 15, Pages 4: An Analysis of ML-Based Outlier
           Detection from Mobile Phone Trajectories

    • Authors: Francisco Melo Pereira, Rute C. Sofia
      First page: 4
      Abstract: This paper provides an analysis of two machine learning algorithms, density-based spatial clustering of applications with noise (DBSCAN) and the local outlier factor (LOF), applied in the detection of outliers in the context of a continuous framework for the detection of points of interest (PoI). This framework has as input mobile trajectories of users that are continuously fed to the framework in close to real time. Such frameworks are today still in their infancy and highly required in large-scale sensing deployments, e.g., Smart City planning deployments, where individual anonymous trajectories of mobile users can be useful to better develop urban planning. The paper’s contributions are twofold. Firstly, the paper provides the functional design for the overall PoI detection framework. Secondly, the paper analyses the performance of DBSCAN and LOF for outlier detection considering two different datasets, a dense and large dataset with over 170 mobile phone-based trajectories and a smaller and sparser dataset, involving 3 users and 36 trajectories. Results achieved show that LOF exhibits the best performance across the different datasets, thus showing better suitability for outlier detection in the context of frameworks that perform PoI detection in close to real time.
      Citation: Future Internet
      PubDate: 2022-12-23
      DOI: 10.3390/fi15010004
      Issue No: Vol. 15, No. 1 (2022)
       
  • Future Internet, Vol. 15, Pages 5: Using Social Media & Sentiment
           Analysis to Make Investment Decisions

    • Authors: Ben Hasselgren, Christos Chrysoulas, Nikolaos Pitropakis, William J. Buchanan
      First page: 5
      Abstract: Making investment decisions by utilizing sentiment data from social media (SM) is starting to become a more tangible concept. There has been a broad investigation into this field of study over the last decade, and many of the findings have promising results. However, there is still an opportunity for continued research, firstly, in finding the most effective way to obtain relevant sentiment data from SM, then building a system to measure the sentiment, and finally visualizing it to help users make investment decisions. Furthermore, much of the existing work fails to factor SM metrics into the sentiment score effectively. This paper presents a novel prototype as a contribution to the field of study. In our work, a detailed overview of the topic is given in the form of a literature and technical review. Next, a prototype is designed and developed using the findings from the previous analysis. On top of that, a novel approach to factor SM metrics into the sentiment score is presented, with the goal of measuring the collective sentiment of the data effectively. To test the proposed approach, we only used popular stocks from the S&P500 to ensure large volumes of SM sentiment was available, adding further insight into findings, which we then discuss in our evaluation.
      Citation: Future Internet
      PubDate: 2022-12-23
      DOI: 10.3390/fi15010005
      Issue No: Vol. 15, No. 1 (2022)
       
  • Future Internet, Vol. 15, Pages 6: Statistical Model of Accurately
           Estimating Service Delay Behavior in Saturated IEEE 802.11 Networks Based
           on 2-D Markov Chain

    • Authors: Qian Yang, Suoping Li, Hongli Li, Weiru Chen
      First page: 6
      Abstract: To accurately estimate the service delay behavior of IEEE 802.11 networks, this paper comprehensively considers four main factors that affect the performance of IEEE 802.11 networks and establishes a service delay model with statistical characteristics. We analyzed the operation mechanism of 802.11 DCF, using the backoff stage and the backoff counter to portray the dynamic change characteristics of the system regarding the data frame transmission states. Afterward, we calculated the one-step transition probability of these states, establishing a 2-D Markov model, including the ICS procedure and the backoff procedure. Based on this model, we constructed steady-state equations to derive a relationship between the transmission probability and collision probability for each node transmission queue. By analyzing the ICS delay and the backoff delay, we obtained the probability generating function (PGF) of the average idle time. The analytical expressions of other service delays, such as the successful transmission time and collided transmission time, were derived to obtain the PGF of the total service delay. In the numerical simulation, we compared the first two statistical moments of the PGF with the Nav model, and it was found that our delay evaluation results were significantly better than the traditional evaluation results. The average service delay of the Nav model in all the scenarios was larger than that of the proposed model due to the lack of the ICS procedure in the Nav model. Since a DIFS duration is generally much shorter than a random backoff duration, our model saves the bandwidth and improves transmission efficiency.
      Citation: Future Internet
      PubDate: 2022-12-25
      DOI: 10.3390/fi15010006
      Issue No: Vol. 15, No. 1 (2022)
       
  • Future Internet, Vol. 15, Pages 7: SmartGroup: A Tool for Small-Group
           Learning Activities

    • Authors: Haining Zhu, Na Li, Nitish Kumar Rai, John M. Carroll
      First page: 7
      Abstract: Small-group learning activities (SGLAs) offer varied active learning opportunities and student benefits, but higher education instructors do not universally adopt SGLAs, in part owing to management burdens. We designed and deployed the SmartGroup system, a tool-based approach to minimize instructor burdens while facilitating SGLAs and associated benefits by managing peer group formation and peer group work assessment. SmartGroup was deployed in one course over 10 weeks; iterations of SmartGroup were provided continuously to meet the instructor’s needs. After deployment, the instructor and teaching assistant were interviewed, and 20 anonymous post-study survey responses were collected. The system exposed students to new perspectives, fostered meta-cognitive opportunities, and improved weaker students’ performances while being predominantly well-received in terms of usability and satisfaction. Our work contributes to the literature an exploration of tool-assisted peer group work assessment in higher education and how to promote wider SGLA adoption.
      Citation: Future Internet
      PubDate: 2022-12-26
      DOI: 10.3390/fi15010007
      Issue No: Vol. 15, No. 1 (2022)
       
  • Future Internet, Vol. 15, Pages 8: Evaluating the Perceived Quality of
           Mobile Banking Applications in Croatia: An Empirical Study

    • Authors: Tihomir Orehovački, Luka Blašković, Matej Kurevija
      First page: 8
      Abstract: Mobile banking is nowadays a standard service provided by banks worldwide because it adds convenience for people. There is no more rushing to a bank or waiting in lines for a simple transaction that can be conducted from anywhere and at any time in the blink of an eye. To be consumed by a respective amount of bank clients regularly, mobile banking applications are required to be continuously improved and updated, be in line with recent security standards, and meet quality requirements. This paper tackles the perceived quality of mobile banking applications that are most commonly used in Croatia and has three objectives in that respect. The first one is to identify the extent to which pragmatic and hedonic dimensions of quality contribute to customers’ satisfaction and their behavioral intentions related to the continuous use of mobile banking applications. The second one is to determine if there are significant differences in the perceived quality between users of diverse mobile banking applications as well as between users who belong to different age groups. The last one is to uncover the advantages and disadvantages of evaluated mobile banking applications. For this purpose, an empirical study was carried out, during which data were collected with an online questionnaire. The sample was composed of 130 participants who are representative and regular users of mobile banking applications. The psychometric features of the proposed research model, which represents an interplay of perceived quality attributes, were tested using the partial least squares structural equation modeling (PLS-SEM) method. Differences in the perceived quality among different mobile banking applications and customers of various age groups were explored with Kruskal–Wallis tests. Pros and cons of mobile banking applications were identified with the help of descriptive statistics. Study findings indicate that, in the context of mobile banking applications used in Croatia, feedback quality and responsiveness contribute to the ease of use, usefulness is affected by both ease of use and efficiency, responsiveness has a significant impact on efficiency while ease of use, usefulness, and security of personal data are predictors of customers’ satisfaction which in turn influences their behavioral intentions. While no significant difference exists in the perceived quality of four examined mobile banking applications, we found a significant difference in the perceived quality among three age groups of users of mobile banking applications. The most commonly reported advantages of mobile banking applications were related to facets of their efficiency and usefulness, whereas their main drawback appeared to be the lack of features dealing with the personalization of offered services. The reported and discussed results of an empirical study can be used as a set of guidelines for future advances in the evaluation and design of mobile banking applications.
      Citation: Future Internet
      PubDate: 2022-12-26
      DOI: 10.3390/fi15010008
      Issue No: Vol. 15, No. 1 (2022)
       
  • Future Internet, Vol. 15, Pages 9: HH-NIDS: Heterogeneous Hardware-Based
           Network Intrusion Detection Framework for IoT Security

    • Authors: Duc-Minh Ngo, Dominic Lightbody, Andriy Temko, Cuong Pham-Quoc, Ngoc-Thinh Tran, Colin C. Murphy, Emanuel Popovici
      First page: 9
      Abstract: This study proposes a heterogeneous hardware-based framework for network intrusion detection using lightweight artificial neural network models. With the increase in the volume of exchanged data, IoT networks’ security has become a crucial issue. Anomaly-based intrusion detection systems (IDS) using machine learning have recently gained increased popularity due to their generation’s ability to detect unseen attacks. However, the deployment of anomaly-based AI-assisted IDS for IoT devices is computationally expensive. A high-performance and ultra-low power consumption anomaly-based IDS framework is proposed and evaluated in this paper. The framework has achieved the highest accuracy of 98.57% and 99.66% on the UNSW-NB15 and IoT-23 datasets, respectively. The inference engine on the MAX78000EVKIT AI-microcontroller is 11.3 times faster than the Intel Core i7-9750H 2.6 GHz and 21.3 times faster than NVIDIA GeForce GTX 1650 graphics cards, when the power drawn was 18mW. In addition, the pipelined design on the PYNQ-Z2 SoC FPGA board with the Xilinx Zynq xc7z020-1clg400c device is optimised to run at the on-chip frequency (100 MHz), which shows a speedup of 53.5 times compared to the MAX78000EVKIT.
      Citation: Future Internet
      PubDate: 2022-12-26
      DOI: 10.3390/fi15010009
      Issue No: Vol. 15, No. 1 (2022)
       
  • Future Internet, Vol. 15, Pages 10: NewSQL Databases Assessment:
           CockroachDB, MariaDB Xpand, and VoltDB

    • Authors: Eduardo Pina, Filipe Sá, Jorge Bernardino
      First page: 10
      Abstract: Background: Relational databases have been a prevalent technology for decades, using SQL (Structured Query Language) to manage data. However, the emergence of new technologies, such as the web and the cloud, has brought the requirement to handle more complex data. NewSQL is the latest technology that incorporates the ability to scale and ensures the availability of NoSQL (Not Only SQL) without losing the ACID properties (Atomicity, Consistency, Isolation, Durability) associated with relational databases. Methods: We evaluated CockroachDB, MariaDB Xpand, and VoltDB with OSSpal methodology and experimentally using the Star Schema Benchmark (SSB). The scalability and performance capabilities of each database were assessed. Results: Applying the OSSpal methodology, the results showed that MariaDB Xpand outperformed CockroachDB and VoltDB. On the other hand, we concluded that with Star Schema Benchmark, CockroachDB had better scalability, while VoltDB had a faster query execution time. Conclusions: CockroachDB and VoltDB are the best performing databases in terms of scalability and performance.
      Citation: Future Internet
      PubDate: 2022-12-26
      DOI: 10.3390/fi15010010
      Issue No: Vol. 15, No. 1 (2022)
       
  • Future Internet, Vol. 15, Pages 11: Review on Semantic Modeling and
           Simulation of Cybersecurity and Interoperability on the Internet of
           Underwater Things

    • Authors: Kotis, Stavrinos, Kalloniatis
      First page: 11
      Abstract: As maritime and military missions become more and more complex and multifactorial over the years, there has been a high interest in the research and development of (autonomous) unmanned underwater vehicles (UUVs). Latest efforts concern the modeling and simulation of UUVs’ collaboration in swarm formations, towards obtaining deeper insights related to the critical issues of cybersecurity and interoperability. The research topics, which are constantly emerging in this domain, are closely related to the communication, interoperability, and secure operation of UUVs, as well as to the volume, velocity, variety, and veracity of data transmitted in low bit-rate due to the medium, i.e., the water. This paper reports on specific research topics in the domain of UUVs, emphasizing interoperability and cybersecurity in swarms of UUVs in a military/search-and-rescue setting. The goal of this work is two-fold: a) to review existing methods and tools of semantic modeling and simulation for cybersecurity and interoperability on the Internet of Underwater Things (IoUT), b) to highlight open issues and challenges, towards developing a novel simulation approach to effectively support critical and life-saving decision-making of commanders of military and search-and-rescue operations.
      Citation: Future Internet
      PubDate: 2022-12-26
      DOI: 10.3390/fi15010011
      Issue No: Vol. 15, No. 1 (2022)
       
  • Future Internet, Vol. 15, Pages 12: Pedestrian Simulation with
           Reinforcement Learning: A Curriculum-Based Approach

    • Authors: Giuseppe Vizzari, Thomas Cecconello
      First page: 12
      Abstract: Pedestrian simulation is a consolidated but still lively area of research. State of the art models mostly take an agent-based perspective, in which pedestrian decisions are made according to a manually defined model. Reinforcement learning (RL), on the other hand, is used to train an agent situated in an environment how to act so as to maximize an accumulated numerical reward signal (a feedback provided by the environment to every chosen action). We explored the possibility of applying RL to pedestrian simulation. We carefully defined a reward function combining elements related to goal orientation, basic proxemics, and basic way-finding considerations. The proposed approach employs a particular training curriculum, a set of scenarios growing in difficulty supporting an incremental acquisition of general movement competences such as orientation, walking, and pedestrian interaction. The learned pedestrian behavioral model is applicable to situations not presented to the agents in the training phase, and seems therefore reasonably general. This paper describes the basic elements of the approach, the training procedure, and an experimentation within a software framework employing Unity and ML-Agents.
      Citation: Future Internet
      PubDate: 2022-12-27
      DOI: 10.3390/fi15010012
      Issue No: Vol. 15, No. 1 (2022)
       
  • Future Internet, Vol. 15, Pages 13: A Cross-Platform Personalized
           Recommender System for Connecting E-Commerce and Social Network

    • Authors: Jiaxu Zhao, Binting Su, Xuli Rao, Zhide Chen
      First page: 13
      Abstract: In this paper, we build a recommender system for a new study area: social commerce, which combines rich information about social network users and products on an e-commerce platform. The idea behind this recommender system is that a social network contains abundant information about its users which could be exploited to create profiles of the users. For social commerce, the quality of the profiles of potential consumers determines whether the recommender system is a success or a failure. In our work, not only the user’s textual information but also the tags and the relationships between users have been considered in the process of building user profiling model. A topic model has been adopted in our system, and a feedback mechanism also been design in this paper. Then, we apply a collative filtering method and a clustering algorithm in order to obtain a high recommendation accuracy. We do an empirical analysis based on real data collected on a social network and an e-commerce platform. We find that the social network has an impact on e-commerce, so social commerce could be realized. Simulations show that our topic model has a better performance in topic finding, meaning that our profile-building model is suitable for a social commerce recommender system.
      Citation: Future Internet
      PubDate: 2022-12-27
      DOI: 10.3390/fi15010013
      Issue No: Vol. 15, No. 1 (2022)
       
  • Future Internet, Vol. 15, Pages 14: Human–Machine Interaction
           through Advanced Haptic Sensors: A Piezoelectric Sensory Glove with Edge
           Machine Learning for Gesture and Object Recognition

    • Authors: Roberto De Fazio, Vincenzo Mariano Mastronardi, Matteo Petruzzi, Massimo De Vittorio, Paolo Visconti
      First page: 14
      Abstract: Human–machine interaction (HMI) refers to systems enabling communication between machines and humans. Systems for human–machine interfaces have advanced significantly in terms of materials, device design, and production methods. Energy supply units, logic circuits, sensors, and data storage units must be flexible, stretchable, undetectable, biocompatible, and self-healing to act as human–machine interfaces. This paper discusses the technologies for providing different haptic feedback of different natures. Notably, the physiological mechanisms behind touch perception are reported, along with a classification of the main haptic interfaces. Afterward, a comprehensive overview of wearable haptic interfaces is presented, comparing them in terms of cost, the number of integrated actuators and sensors, their main haptic feedback typology, and their future application. Additionally, a review of sensing systems that use haptic feedback technologies—specifically, smart gloves—is given by going through their fundamental technological specifications and key design requirements. Furthermore, useful insights related to the design of the next-generation HMI devices are reported. Lastly, a novel smart glove based on thin and conformable AlN (aluminum nitride) piezoelectric sensors is demonstrated. Specifically, the device acquires and processes the signal from the piezo sensors to classify performed gestures through an onboard machine learning (ML) algorithm. Then, the design and testing of the electronic conditioning section of AlN-based sensors integrated into the smart glove are shown. Finally, the architecture of a wearable visual-tactile recognition system is presented, combining visual data acquired by a micro-camera mounted on the user’s glass with the haptic ones provided by the piezoelectric sensors.
      Citation: Future Internet
      PubDate: 2022-12-27
      DOI: 10.3390/fi15010014
      Issue No: Vol. 15, No. 1 (2022)
       
  • Future Internet, Vol. 15, Pages 15: BART-IT: An Efficient
           Sequence-to-Sequence Model for Italian Text Summarization

    • Authors: Moreno La Quatra, Luca Cagliero
      First page: 15
      Abstract: The emergence of attention-based architectures has led to significant improvements in the performance of neural sequence-to-sequence models for text summarization. Although these models have proved to be effective in summarizing English-written documents, their portability to other languages is limited thus leaving plenty of room for improvement. In this paper, we present BART-IT, a sequence-to-sequence model, based on the BART architecture that is specifically tailored to the Italian language. The model is pre-trained on a large corpus of Italian-written pieces of text to learn language-specific features and then fine-tuned on several benchmark datasets established for abstractive summarization. The experimental results show that BART-IT outperforms other state-of-the-art models in terms of ROUGE scores in spite of a significantly smaller number of parameters. The use of BART-IT can foster the development of interesting NLP applications for the Italian language. Beyond releasing the model to the research community to foster further research and applications, we also discuss the ethical implications behind the use of abstractive summarization models.
      Citation: Future Internet
      PubDate: 2022-12-27
      DOI: 10.3390/fi15010015
      Issue No: Vol. 15, No. 1 (2022)
       
  • Future Internet, Vol. 15, Pages 16: Narrowband Internet-of-Things to
           Enhance the Vehicular Communications Performance

    • Authors: Qadri Hamarsheh, Omar Daoud, Mohammed Baniyounis, Ahlam Damati
      First page: 16
      Abstract: The interest in vehicle-to-vehicle communication has gained a high demand in the last decade. This is due to the need for safe and robust smart communication, while this type of communication is vulnerable to latency and power. Therefore, this work proposes the Narrowband Internet-of-Things to enhance the robustness of the vehicular communication system. Accordingly, the system’s QoS is enhanced. This enhancement is based on proposing two parts to cover the latency and the harmonics issues, in addition to proposing a distributed antenna configuration for the moving vehicles under a machine learning benchmark, which uses the across-entropy algorithm. The proposed environment has been simulated and compared to the state-of-the-art work performance. The simulation results verify the proposed work performance based on three different parameters; namely the latency, the mean squared error rate, and the transmitted signal block error rate. From these results, the proposed work outperforms the literature; at the probability of 10−3, the proposed work reduces the peak power deficiency by almost 49%, an extra 23.5% enhancement has been attained from the self-interference cancellation side, and a bit error rate enhancement by a ratio of 31%.
      Citation: Future Internet
      PubDate: 2022-12-28
      DOI: 10.3390/fi15010016
      Issue No: Vol. 15, No. 1 (2022)
       
  • Future Internet, Vol. 15, Pages 17: Drifting Streaming
           Peaks-Over-Threshold-Enhanced Self-Evolving Neural Networks for Short-Term
           Wind Farm Generation Forecast

    • Authors: Yunchuan Liu, Amir Ghasemkhani, Lei Yang
      First page: 17
      Abstract: This paper investigates the short-term wind farm generation forecast. It is observed from the real wind farm generation measurements that wind farm generation exhibits distinct features, such as the non-stationarity and the heterogeneous dynamics of ramp and non-ramp events across different classes of wind turbines. To account for the distinct features of wind farm generation, we propose a Drifting Streaming Peaks-over-Threshold (DSPOT)-enhanced self-evolving neural networks-based short-term wind farm generation forecast. Using DSPOT, the proposed method first classifies the wind farm generation data into ramp and non-ramp datasets, where time-varying dynamics are taken into account by utilizing dynamic ramp thresholds to separate the ramp and non-ramp events. We then train different neural networks based on each dataset to learn the different dynamics of wind farm generation by the NeuroEvolution of Augmenting Topologies (NEAT), which can obtain the best network topology and weighting parameters. As the efficacy of the neural networks relies on the quality of the training datasets (i.e., the classification accuracy of the ramp and non-ramp events), a Bayesian optimization-based approach is developed to optimize the parameters of DSPOT to enhance the quality of the training datasets and the corresponding performance of the neural networks. Based on the developed self-evolving neural networks, both distributional and point forecasts are developed. The experimental results show that compared with other forecast approaches, the proposed forecast approach can substantially improve the forecast accuracy, especially for ramp events. The experiment results indicate that the accuracy improvement in a 60 min horizon forecast in terms of the mean absolute error (MAE) is at least 33.6% for the whole year data and at least 37% for the ramp events. Moreover, the distributional forecast in terms of the continuous rank probability score (CRPS) is improved by at least 35.8% for the whole year data and at least 35.2% for the ramp events.
      Citation: Future Internet
      PubDate: 2022-12-28
      DOI: 10.3390/fi15010017
      Issue No: Vol. 15, No. 1 (2022)
       
  • Future Internet, Vol. 15, Pages 18: VR, AR, and 3-D User Interfaces for
           Measurement and Control

    • Authors: Annalisa Liccardo, Francesco Bonavolontà
      First page: 18
      Abstract: The topics of virtual, mixed, and extended reality have now become key areas in various fields of scientific and industrial applications, and the interest in them is made tangible by the numerous papers available in the scientific literature. In this regard, the Special Issue “VR, AR, and 3-D User Interfaces for Measurement and Control” received a fair number of varied contributions that analyzed different aspects of the implementation of virtual, mixed, and extended reality systems and approaches in the real world. They range from investigating the requirements of new potential technologies to the prediction verification of the effectiveness and benefits of their use, the analysis of the difficulties of interaction with graphical interfaces to the possibility of performing complex and risky tasks (such as surgical operations) using mixed reality viewers. All contributions were of a high standard and mainly highlight that measurement and control applications based on the new models of interaction with reality are by now increasingly ready to leave laboratory spaces and become objects and features of common life. The significant benefits of this technology will radically change the way we live and interact with information and the reality around us, and it will surely be worthy of further exploration, maybe even in a new Special Issue of Future Internet.
      Citation: Future Internet
      PubDate: 2022-12-29
      DOI: 10.3390/fi15010018
      Issue No: Vol. 15, No. 1 (2022)
       
  • Future Internet, Vol. 15, Pages 19: Logically-Centralized SDN-Based NDN
           Strategies for Wireless Mesh Smart-City Networks

    • Authors: Sarantis Kalafatidis, Sotiris Skaperas, Vassilis Demiroglou, Lefteris Mamatas, Vassilis Tsaoussidis
      First page: 19
      Abstract: The Internet of Things (IoT) is a key technology for smart community networks, such as smart-city environments, and its evolution calls for stringent performance requirements (e.g., low delay) to support efficient communication among a wide range of objects, including people, sensors, vehicles, etc. At the same time, these ecosystems usually adopt wireless mesh technology to extend their communication range in large-scale IoT deployments. However, due to the high range of coverage, the smart-city WMNs may face different network challenges according to the network characteristic, for example, (i) areas that include a significant number of wireless nodes or (ii) areas with frequent dynamic changes such as link failures due to unstable topologies. Named-Data Networking (NDN) can enhance WMNs to meet such IoT requirements, thanks to the content naming scheme and in-network caching, but it necessitates adaptability to the challenging conditions of WMNs. In this work, we aim at efficient end-to-end NDN communication in terms of performance (i.e., delay), performing extended experimentation over a real WMN, evaluating and discussing the benefits provided by two SDN-based NDN strategies: (1) a dynamic SDN-based solution that integrates the NDN operation with the routing decisions of a WMN routing protocol; (2) a static one which based on SDN-based clustering and real WMN performance measurements. Our key contributions include (i) the implementation of two types of NDN path selection strategies; (ii) experimentation and data collection over the w-iLab.t Fed4FIRE+ testbed with real WMN conditions; (ii) real measurements released as open-data, related to the performance of the wireless links in terms of RSSI, delay, and packet loss among the wireless nodes of the corresponding testbed.
      Citation: Future Internet
      PubDate: 2022-12-29
      DOI: 10.3390/fi15010019
      Issue No: Vol. 15, No. 1 (2022)
       
  • Future Internet, Vol. 15, Pages 20: Teachers’ Views on Integrating
           Augmented Reality in Education: Needs, Opportunities, Challenges and
           Recommendations

    • Authors: Perifanou, Economides, Nikou
      First page: 20
      Abstract: The integration of augmented reality (AR) in education is promising since it enhances teaching and offers more engaging and appealing learning experiences. Teachers can have a catalytic role towards the adoption of AR in education; therefore, their perspectives with regard to AR in teaching and learning are very important. The current study explores teachers’ views on the integration of AR in education through an open-ended questionnaire that has been answered by 93 educators worldwide. A set of digital skills that can support student-centered pedagogies in an appropriate infrastructure are the main requirement for effective teaching with AR. Among the perceived benefits and opportunities are interactive teaching and learning, increased interest and engagement, better understanding of complex concepts. As barriers, participants reported the lack of AR educational applications, the cost of buying and maintaining AR equipment and resources, the lack of teachers’ and students’ digital skills, classroom management issues, and security and ethical issues. Moreover, survey participants highlighted the need for raising teachers’ awareness for the added value of AR in education and the need for teachers’ continuous professional development. Implications and future research recommendations on the integration of AR in education are discussed.
      Citation: Future Internet
      PubDate: 2022-12-29
      DOI: 10.3390/fi15010020
      Issue No: Vol. 15, No. 1 (2022)
       
  • Future Internet, Vol. 15, Pages 21: The Emerging Technologies of Digital
           Payments and Associated Challenges: A Systematic Literature Review

    • Authors: Khando Khando, M. Sirajul Islam, Shang Gao
      First page: 21
      Abstract: The interplay between finance and technology with the use of the internet triggered the emergence of digital payment technologies. Such technological innovation in the payment industry is the foundation for financial inclusion. However, despite the continuous progress and potential of moving the payment landscape towards digital payments and connecting the population to the ubiquitous digital environment, some critical issues need to be addressed to achieve a more harmonious inclusive and sustainable cashless society. The study aims to provide a comprehensive literature review on the emerging digital payment technologies and associated challenges. By systematically reviewing existing empirical studies, this study puts forward the state-of-the-art classification of digital payment technologies and presents four categories of digital payment technologies: card payment, e-payment,mobile payment and cryptocurrencies. Subsequently, the paper presents the key challenges in digital payment technologies categorized into broad themes: social, economic, technical, awareness and legal. The classification and categorization of payment technologies and associated challenges can be useful to both researchers and practitioners to understand, elucidate and develop a coherent digital payment strategy.
      Citation: Future Internet
      PubDate: 2022-12-30
      DOI: 10.3390/fi15010021
      Issue No: Vol. 15, No. 1 (2022)
       
  • Future Internet, Vol. 15, Pages 22: A Novel NODE Approach Combined with
           LSTM for Short-Term Electricity Load Forecasting

    • Authors: Songtao Huang, Jun Shen, Qingquan Lv, Qingguo Zhou, Binbin Yong
      First page: 22
      Abstract: Electricity load forecasting has seen increasing importance recently, especially with the effectiveness of deep learning methods growing. Improving the accuracy of electricity load forecasting is vital for public resources management departments. Traditional neural network methods such as long short-term memory (LSTM) and bidirectional LSTM (BiLSTM) have been widely used in electricity load forecasting. However, LSTM and its variants are not sensitive to the dynamic change of inputs and miss the internal nonperiodic rules of series, due to their discrete observation interval. In this paper, a novel neural ordinary differential equation (NODE) method, which can be seen as a continuous version of residual network (ResNet), is applied to electricity load forecasting to learn dynamics of time series. We design three groups of models based on LSTM and BiLSTM and compare the accuracy between models using NODE and without NODE. The experimental results show that NODE can improve the prediction accuracy of LSTM and BiLSTM. It indicates that NODE is an effective approach to improving the accuracy of electricity load forecasting.
      Citation: Future Internet
      PubDate: 2022-12-30
      DOI: 10.3390/fi15010022
      Issue No: Vol. 15, No. 1 (2022)
       
  • Future Internet, Vol. 15, Pages 23: A GIS-Based Hot and Cold Spots
           Detection Method by Extracting Emotions from Social Streams

    • Authors: Cardone, Di Martino, Miraglia
      First page: 23
      Abstract: Hot and cold spot identification is a spatial analysis technique used in various issues to identify regions where a specific phenomenon is either strongly or poorly concentrated or sensed. Many hot/cold spot detection techniques are proposed in literature; clustering methods are generally applied in order to extract hot and cold spots as polygons on the maps; the more precise the determination of the area of the hot (cold) spots, the greater the computational complexity of the clustering algorithm. Furthermore, these methods do not take into account the hidden information provided by users through social networks, which is significant for detecting the presence of hot/cold spots based on the emotional reactions of citizens. To overcome these critical points, we propose a GIS-based hot and cold spot detection framework encapsulating a classification model of emotion categories of documents extracted from social streams connected to the investigated phenomenon is implemented. The study area is split into subzones; residents’ postings during a predetermined time period are retrieved and analyzed for each subzone. The proposed model measures for each subzone the prevalence of pleasant and unpleasant emotional categories in different time frames; with the aid of a fuzzy-based emotion classification approach, subzones in which unpleasant/pleasant emotions prevail over the analyzed time period are labeled as hot/cold spots. A strength of the proposed framework is to significantly reduce the CPU time of cluster-based hot and cold spot detection methods as it does not require detecting the exact geometric shape of the spot. Our framework was tested to detect hot and cold spots related to citizens’ discomfort due to heatwaves in the study area made up of the municipalities of the northeastern area of the province of Naples (Italy). The results show that the hot spots, where the greatest discomfort is felt, correspond to areas with a high population/building density. On the contrary, cold spots cover urban areas having a lower population density.
      Citation: Future Internet
      PubDate: 2022-12-30
      DOI: 10.3390/fi15010023
      Issue No: Vol. 15, No. 1 (2022)
       
  • Future Internet, Vol. 15, Pages 24: Formal Safety Assessment and
           Improvement of DDS Protocol for Industrial Data Distribution Service

    • Authors: Jinze Du, Chengtai Gao, Tao Feng
      First page: 24
      Abstract: The Data Distribution Service (DDS) for real-time systems is an industrial Internet communication protocol. Due to its distributed high reliability and the ability to transmit device data communication in real-time, it has been widely used in industry, medical care, transportation, and national defense. With the wide application of various protocols, protocol security has become a top priority. There are many studies on protocol security, but these studies lack a formal security assessment of protocols. Based on the above status, this paper evaluates and improves the security of the DDS protocol using a model detection method combining the Dolev–Yao attack model and the Coloring Petri Net (CPN) theory. Because of the security loopholes in the original protocol, a timestamp was introduced into the original protocol, and the shared key establishment process in the original protocol lacked fairness and consistency. We adopted a new establishment method to establish the shared secret and re-verified its security. The results show that the overall security of the protocol has been improved by 16.7% while effectively preventing current replay attack.
      Citation: Future Internet
      PubDate: 2022-12-31
      DOI: 10.3390/fi15010024
      Issue No: Vol. 15, No. 1 (2022)
       
  • Future Internet, Vol. 14, Pages 346: Cloud-Native Applications and
           Services

    • Authors: Nane Kratzke
      First page: 346
      Abstract: This Special Issue presents some of the most recent innovations in cloud-native software and system [...]
      Citation: Future Internet
      PubDate: 2022-11-22
      DOI: 10.3390/fi14120346
      Issue No: Vol. 14, No. 12 (2022)
       
  • Future Internet, Vol. 14, Pages 347: Evolutionary Computation for Sparse
           Synthesis Optimization of CCAAs: An Enhanced Whale Optimization Algorithm
           Method

    • Authors: Bohao Tang, Lihua Cai, Shuai Yang, Jiaxing Xu, Yi Yu
      First page: 347
      Abstract: Concentric circular antenna arrays (CCAAs) can obtain better performance than other antenna arrays. However, high overhead and excessive sidelobes still make its application difficult. In this paper, we consider the sparse synthesis optimization of CCAAs. Specifically, we aim to turn off a specific number of antennas while reducing the sidelobe of CCAAs. First, we formulate an optimization problem and present the solution space. Then, we propose a novel evolutionary method for solving the optimization problem. Our proposed method introduces hybrid solution initialization, hybrid crossover method, and hybrid update methods. Simulation results show the effectiveness of the proposed algorithm and the proposed improvement factors.
      Citation: Future Internet
      PubDate: 2022-11-22
      DOI: 10.3390/fi14120347
      Issue No: Vol. 14, No. 12 (2022)
       
  • Future Internet, Vol. 14, Pages 348: rl4dtn: Q-Learning for Opportunistic
           Networks

    • Authors: Jorge Visca, Javier Baliosian
      First page: 348
      Abstract: Opportunistic networks are highly stochastic networks supported by sporadic encounters between mobile devices. To route data efficiently, opportunistic-routing algorithms must capitalize on devices’ movement and data transmission patterns. This work proposes a routing method based on reinforcement learning, specifically Q-learning. As usual in routing algorithms, the objective is to select the best candidate devices to put forward once an encounter occurs. However, there is also the possibility of not forwarding if we know that a better candidate might be encountered in the future. This decision is not usually considered in learning schemes because there is no obvious way to represent the temporal evolution of the network. We propose a novel, distributed, and online method that allows learning both the network’s connectivity and its temporal evolution with the help of a temporal graph. This algorithm allows learning to skip forwarding opportunities to capitalize on future encounters. We show that explicitly representing the action for deferring forwarding increases the algorithm’s performance. The algorithm’s scalability is discussed and shown to perform well in a network of considerable size.
      Citation: Future Internet
      PubDate: 2022-11-23
      DOI: 10.3390/fi14120348
      Issue No: Vol. 14, No. 12 (2022)
       
  • Future Internet, Vol. 14, Pages 349: YOLO-DFAN: Effective High-Altitude
           Safety Belt Detection Network

    • Authors: Wendou Yan, Xiuying Wang, Shoubiao Tan
      First page: 349
      Abstract: This paper proposes the You Only Look Once (YOLO) dependency fusing attention network (DFAN) detection algorithm, improved based on the lightweight network YOLOv4-tiny. It combines the advantages of fast speed of traditional lightweight networks and high precision of traditional heavyweight networks, so it is very suitable for the real-time detection of high-altitude safety belts in embedded equipment. In response to the difficulty of extracting the features of an object with a low effective pixel ratio—which is an object with a low ratio of actual area to detection anchor area in the YOLOv4-tiny network—we make three major improvements to the baseline network: The first improvement is introducing the atrous spatial pyramid pooling network after CSPDarkNet-tiny extracts features. The second is to propose the DFAN, while the third is to introduce the path aggregation network (PANet) to replace the feature pyramid network (FPN) of the original network and fuse it with the DFAN. According to the experimental results in the high-altitude safety belt dataset, YOLO-DFAN improves the accuracy by 5.13% compared with the original network, and its detection speed meets the real-time demand. The algorithm also exhibits a good improvement on the Pascal voc07+12 dataset.
      Citation: Future Internet
      PubDate: 2022-11-23
      DOI: 10.3390/fi14120349
      Issue No: Vol. 14, No. 12 (2022)
       
  • Future Internet, Vol. 14, Pages 350: COVID-Related Misinformation
           Migration to BitChute and Odysee

    • Authors: Olga Papadopoulou, Evangelia Kartsounidou, Symeon Papadopoulos
      First page: 350
      Abstract: The overwhelming amount of information and misinformation on social media platforms has created a new role that these platforms are inclined to take on, that of the Internet custodian. Mainstream platforms, such as Facebook, Twitter and YouTube, are under tremendous public and political pressure to combat disinformation and remove harmful content. Meanwhile, smaller platforms, such as BitChute and Odysee, have emerged and provide fertile ground for disinformation as a result of their low content-moderation policy. In this study, we analyze the phenomenon of removed content migration from YouTube to BitChute and Odysee. In particular, starting from a list of COVID-related videos removed from YouTube due to violating its misinformation policy, we find that ∼15% (1114 videos) of them migrated to the two low content-moderation platforms under study. This amounts to 4096 videos on BitChute and 1810 on Odysee. We present an analysis of this video dataset, revealing characteristics of misinformation dissemination similar to those on YouTube and other mainstream social media platforms. The BitChute–Odysee COVID-related dataset is publicly available for research purposes on misinformation analysis.
      Citation: Future Internet
      PubDate: 2022-11-23
      DOI: 10.3390/fi14120350
      Issue No: Vol. 14, No. 12 (2022)
       
  • Future Internet, Vol. 14, Pages 351: A Systematic Literature Review on
           Applications of GAN-Synthesized Images for Brain MRI

    • Authors: Sampada Tavse, Vijayakumar Varadarajan, Mrinal Bachute, Shilpa Gite, Ketan Kotecha
      First page: 351
      Abstract: With the advances in brain imaging, magnetic resonance imaging (MRI) is evolving as a popular radiological tool in clinical diagnosis. Deep learning (DL) methods can detect abnormalities in brain images without an extensive manual feature extraction process. Generative adversarial network (GAN)-synthesized images have many applications in this field besides augmentation, such as image translation, registration, super-resolution, denoising, motion correction, segmentation, reconstruction, and contrast enhancement. The existing literature was reviewed systematically to understand the role of GAN-synthesized dummy images in brain disease diagnosis. Web of Science and Scopus databases were extensively searched to find relevant studies from the last 6 years to write this systematic literature review (SLR). Predefined inclusion and exclusion criteria helped in filtering the search results. Data extraction is based on related research questions (RQ). This SLR identifies various loss functions used in the above applications and software to process brain MRIs. A comparative study of existing evaluation metrics for GAN-synthesized images helps choose the proper metric for an application. GAN-synthesized images will have a crucial role in the clinical sector in the coming years, and this paper gives a baseline for other researchers in the field.
      Citation: Future Internet
      PubDate: 2022-11-25
      DOI: 10.3390/fi14120351
      Issue No: Vol. 14, No. 12 (2022)
       
  • Future Internet, Vol. 14, Pages 352: Recursive Feature Elimination for
           Improving Learning Points on Hand-Sign Recognition

    • Authors: Rung-Ching Chen, William Eric Manongga, Christine Dewi
      First page: 352
      Abstract: Hand gestures and poses allow us to perform non-verbal communication. Sign language is becoming more important with the increase in the number of deaf and hard-of-hearing communities. However, learning to understand sign language is very difficult and also time consuming. Researchers are still trying to find a better way to understand sign language using the help of technology. The accuracy of most hand-sign detection methods still needs to be improved for real-life usage. In this study, Mediapipe is used for hand feature extraction. Mediapipe can extract 21 hand landmarks from a hand image. Hand-pose detection using hand landmarks is chosen since it reduces the interference from the image background and uses fewer parameters compared to traditional hand-sign classification using pixel-based features and CNN. The Recursive Feature Elimination (RFE) method, using a novel distance from the hand landmark to the palm centroid, is proposed for feature selection to improve the accuracy of digit hand-sign detection. We used three different datasets in this research to train models with a different number of features, including the original 21 features, 15 features, and 10 features. A fourth dataset was used to evaluate the performance of these trained models. The fourth dataset is not used to train any model. The result of this study shows that removing the non-essential hand landmarks can improve the accuracy of the models in detecting digit hand signs. Models trained using fewer features have higher accuracy than models trained using the original 21 features. The model trained with 10 features also shows better accuracy than other models trained using 21 features and 15 features.
      Citation: Future Internet
      PubDate: 2022-11-26
      DOI: 10.3390/fi14120352
      Issue No: Vol. 14, No. 12 (2022)
       
  • Future Internet, Vol. 14, Pages 353: Analysis and Prediction of the IPv6
           Traffic over Campus Networks in Shanghai

    • Authors: Zhiyang Sun, Hui Ruan, Yixin Cao, Yang Chen, Xin Wang
      First page: 353
      Abstract: With the exhaustion of IPv4 addresses, research on the adoption, deployment, and prediction of IPv6 networks becomes more and more significant. This paper analyzes the IPv6 traffic of two campus networks in Shanghai, China. We first conduct a series of analyses for the traffic patterns and uncover weekday/weekend patterns, the self-similarity phenomenon, and the correlation between IPv6 and IPv4 traffic. On weekends, traffic usage is smaller than on weekdays, but the distribution does not change much. We find that the self-similarity of IPv4 traffic is close to that of IPv6 traffic, and there is a strong positive correlation between IPv6 traffic and IPv4 traffic. Based on our findings on traffic patterns, we propose a new IPv6 traffic prediction model by combining the advantages of the statistical and deep learning models. In addition, our model would extract useful information from the corresponding IPv4 traffic to enhance the prediction. Based on two real-world datasets, it is shown that the proposed model outperforms eight baselines with a lower prediction error. In conclusion, our approach is helpful for network resource allocation and network management.
      Citation: Future Internet
      PubDate: 2022-11-27
      DOI: 10.3390/fi14120353
      Issue No: Vol. 14, No. 12 (2022)
       
  • Future Internet, Vol. 14, Pages 354: SAUSA: Securing Access, Usage, and
           Storage of 3D Point CloudData by a Blockchain-Based Authentication Network
           

    • Authors: Ronghua Xu, Yu Chen, Genshe Chen, Erik Blasch
      First page: 354
      Abstract: The rapid development of three-dimensional (3D) acquisition technology based on 3D sensors provides a large volume of data, which are often represented in the form of point clouds. Point cloud representation can preserve the original geometric information along with associated attributes in a 3D space. Therefore, it has been widely adopted in many scene-understanding-related applications such as virtual reality (VR) and autonomous driving. However, the massive amount of point cloud data aggregated from distributed 3D sensors also poses challenges for secure data collection, management, storage, and sharing. Thanks to the characteristics of decentralization and security, Blockchain has great potential to improve point cloud services and enhance security and privacy preservation. Inspired by the rationales behind the software-defined network (SDN) technology, this paper envisions SAUSA, a Blockchain-based authentication network that is capable of recording, tracking, and auditing the access, usage, and storage of 3D point cloud datasets in their life-cycle in a decentralized manner. SAUSA adopts an SDN-inspired point cloud service architecture, which allows for efficient data processing and delivery to satisfy diverse quality-of-service (QoS) requirements. A Blockchain-based authentication framework is proposed to ensure security and privacy preservation in point cloud data acquisition, storage, and analytics. Leveraging smart contracts for digitizing access control policies and point cloud data on the Blockchain, data owners have full control of their 3D sensors and point clouds. In addition, anyone can verify the authenticity and integrity of point clouds in use without relying on a third party. Moreover, SAUSA integrates a decentralized storage platform to store encrypted point clouds while recording references of raw data on the distributed ledger. Such a hybrid on-chain and off-chain storage strategy not only improves robustness and availability, but also ensures privacy preservation for sensitive information in point cloud applications. A proof-of-concept prototype is implemented and tested on a physical network. The experimental evaluation validates the feasibility and effectiveness of the proposed SAUSA solution.
      Citation: Future Internet
      PubDate: 2022-11-28
      DOI: 10.3390/fi14120354
      Issue No: Vol. 14, No. 12 (2022)
       
  • Future Internet, Vol. 14, Pages 355: NextDet: Efficient Sparse-to-Dense
           Object Detection with Attentive Feature Aggregation

    • Authors: Priyank Kalgaonkar, Mohamed El-Sharkawy
      First page: 355
      Abstract: Object detection is a computer vision task of detecting instances of objects of a certain class, identifying types of objects, determining its location, and accurately labelling them in an input image or a video. The scope of the work presented within this paper proposes a modern object detection network called NextDet to efficiently detect objects of multiple classes which utilizes CondenseNeXt, an award-winning lightweight image classification convolutional neural network algorithm with reduced number of FLOPs and parameters as the backbone, to efficiently extract and aggregate image features at different granularities in addition to other novel and modified strategies such as attentive feature aggregation in the head, to perform object detection and draw bounding boxes around the detected objects. Extensive experiments and ablation tests, as outlined in this paper, are performed on Argoverse-HD and COCO datasets, which provide numerous temporarily sparse to dense annotated images, demonstrate that the proposed object detection algorithm with CondenseNeXt as the backbone result in an increase in mean Average Precision (mAP) performance and interpretability on Argoverse-HD’s monocular ego-vehicle camera captured scenarios by up to 17.39% as well as COCO’s large set of images of everyday scenes of real-world common objects by up to 14.62%.
      Citation: Future Internet
      PubDate: 2022-11-28
      DOI: 10.3390/fi14120355
      Issue No: Vol. 14, No. 12 (2022)
       
  • Future Internet, Vol. 14, Pages 356: The Application of Artificial
           Intelligence in Magnetic Hyperthermia Based Research

    • Authors: Magdalena Osial, Agnieszka Pregowska
      First page: 356
      Abstract: The development of nanomedicine involves complex nanomaterial research involving magnetic nanomaterials and their use in magnetic hyperthermia. The selection of the optimal treatment strategies is time-consuming, expensive, unpredictable, and not consistently effective. Delivering personalized therapy that obtains maximal efficiency and minimal side effects is highly important. Thus, Artificial Intelligence (AI) based algorithms provide the opportunity to overcome these crucial issues. In this paper, we briefly overview the significance of the combination of AI-based methods, particularly the Machine Learning (ML) technique, with magnetic hyperthermia. We considered recent publications, reports, protocols, and review papers from Scopus and Web of Science Core Collection databases, considering the PRISMA-S review methodology on applying magnetic nanocarriers in magnetic hyperthermia. An algorithmic performance comparison in terms of their types and accuracy, data availability taking into account their amount, types, and quality was also carried out. Literature shows AI support of these studies from the physicochemical evaluation of nanocarriers, drug development and release, resistance prediction, dosing optimization, the combination of drug selection, pharmacokinetic profile characterization, and outcome prediction to the heat generation estimation. The papers reviewed here clearly illustrate that AI-based solutions can be considered as an effective supporting tool in drug delivery, including optimization and behavior of nanocarriers, both in vitro and in vivo, as well as the delivery process. Moreover, the direction of future research, including the prediction of optimal experiments and data curation initiatives has been indicated.
      Citation: Future Internet
      PubDate: 2022-11-28
      DOI: 10.3390/fi14120356
      Issue No: Vol. 14, No. 12 (2022)
       
  • Future Internet, Vol. 14, Pages 357: Integrating ISA and Part-of Domain
           Knowledge into Process Model Discovery

    • Authors: Alessio Bottrighi, Marco Guazzone, Giorgio Leonardi, Stefania Montani, Manuel Striani, Paolo Terenziani
      First page: 357
      Abstract: The traces of process executions are a strategic source of information, from which a model of the process can be mined. In our recent work, we have proposed SIM (semantic interactive miner), an innovative process mining tool to discover the process model incrementally: it supports the interaction with domain experts, who can selectively merge parts of the model to achieve compactness, generalization, and reduced redundancy. We now propose a substantial extension of SIM, making it able to exploit (both automatically and interactively) pre-encoded taxonomic knowledge about the refinement (ISA relations) and composition (part-of relations) of process activities, as is available in many domains. The extended approach allows analysts to move from a process description where activities are reported at the ground level to more user-interpretable/compact descriptions, in which sets of such activities are abstracted into the “macro-activities” subsuming them or constituted by them. An experimental evaluation based on a real-world setting (stroke management) illustrates the advantages of our approach.
      Citation: Future Internet
      PubDate: 2022-11-28
      DOI: 10.3390/fi14120357
      Issue No: Vol. 14, No. 12 (2022)
       
  • Future Internet, Vol. 14, Pages 358: Detection of Malicious Websites Using
           Symbolic Classifier

    • Authors: Nikola Anđelić, Sandi Baressi Šegota, Ivan Lorencin, Matko Glučina
      First page: 358
      Abstract: Malicious websites are web locations that attempt to install malware, which is the general term for anything that will cause problems in computer operation, gather confidential information, or gain total control over the computer. In this paper, a novel approach is proposed which consists of the implementation of the genetic programming symbolic classifier (GPSC) algorithm on a publicly available dataset to obtain a simple symbolic expression (mathematical equation) which could detect malicious websites with high classification accuracy. Due to a large imbalance of classes in the initial dataset, several data sampling methods (random undersampling/oversampling, ADASYN, SMOTE, BorderlineSMOTE, and KmeansSMOTE) were used to balance the dataset classes. For this investigation, the hyperparameter search method was developed to find the combination of GPSC hyperparameters with which high classification accuracy could be achieved. The first investigation was conducted using GPSC with a random hyperparameter search method and each dataset variation was divided on a train and test dataset in a ratio of 70:30. To evaluate each symbolic expression, the performance of each symbolic expression was measured on the train and test dataset and the mean and standard deviation values of accuracy (ACC), AUC, precision, recall and f1-score were obtained. The second investigation was also conducted using GPSC with the random hyperparameter search method; however, 70%, i.e., the train dataset, was used to perform 5-fold cross-validation. If the mean accuracy, AUC, precision, recall, and f1-score values were above 0.97 then final training and testing (train/test 70:30) were performed with GPSC with the same randomly chosen hyperparameters used in a 5-fold cross-validation process and the final mean and standard deviation values of the aforementioned evaluation methods were obtained. In both investigations, the best symbolic expression was obtained in the case where the dataset balanced with the KMeansSMOTE method was used for training and testing. The best symbolic expression obtained using GPSC with the random hyperparameter search method and classic train–test procedure (70:30) on a dataset balanced with the KMeansSMOTE method achieved values of ACC¯, AUC¯, Precsion¯, Recall¯ and F1-score¯ (with standard deviation) 0.9992±2.249×10−5, 0.9995±9.945×10−6, 0.9995±1.09×10−5, 0.999±5.17×10−5, 0.9992±5.17×10−6, respectively. The best symbolic expression obtained using GPSC with a random hyperparameter search method and 5-fold cross-validation on a dataset balanced with the KMeansSMOTE method achieved values of ACC¯, AUC¯, Precsion¯, Recall¯ and F1-score¯ (with standard deviation) 0.9994±1.13×10−5, 0.9994±1.2×10−5, 1.0±0, 0.9988±2.4×10−5, and 0.9994±1.2×10−5, respectively.
      Citation: Future Internet
      PubDate: 2022-11-29
      DOI: 10.3390/fi14120358
      Issue No: Vol. 14, No. 12 (2022)
       
  • Future Internet, Vol. 14, Pages 359: Cumulative Impact of Testing Factors
           in Usability Tests for Human-Centered Web Design

    • Authors: Alexander V. Yakunin, Svetlana S. Bodrunova
      First page: 359
      Abstract: The study examines the cumulative impact of factors that affect usability testing for user-centered web design, namely the so-called ‘contextual fidelity model’ factors that include product properties, task features, user traits, and environment/context factors. Today, the design, user experience and usability (DUXU) research experiences a lack of studies that would assess combinatorial, rather than individual, effects of these factors upon user performance. We address this gap by seeing both independent factors and the resulting user states as complex and dynamic, and testing the combined impact of aesthetic quality of websites, user traits, and individual/group experiment settings upon formation of two dysfunctional user states that critically affect user performance, namely monotony and anxiety. We develop a research design that allows for assessing the combinatorial effects in formation of user dysfunctionality. For that, we conduct a study with 80 assessors of Russian/European and Chinese origin in individual/group setting, employing two types of tasks and websites of high/low aesthetic quality. As the results of our experiment show, group task solving enhances the synchronous impact of website aesthetics and task features upon user states. Interaction of high-quality design, group environment, and monotonous tasks provides for an antagonistic effect when aesthetic layout in a group environment significantly reduces the fatigue rate. Low aesthetic quality in a group environment leads to cumulative enhancing of dysfunctionality for both monotony and anxiety. We conclude by setting questions and prospects for further research.
      Citation: Future Internet
      PubDate: 2022-11-30
      DOI: 10.3390/fi14120359
      Issue No: Vol. 14, No. 12 (2022)
       
  • Future Internet, Vol. 14, Pages 360: QuickFaaS: Providing Portability and
           Interoperability between FaaS Platforms

    • Authors: Pedro Rodrigues, Filipe Freitas, José Simão
      First page: 360
      Abstract: Serverless computing hides infrastructure management from developers and runs code on-demand automatically scaled and billed during the code’s execution time. One of the most popular serverless backend services is called Function-as-a-Service (FaaS), in which developers are often confronted with cloud-specific requirements. Function signature requirements, and the usage of custom libraries that are unique to cloud providers, were identified as the two main reasons for portability issues in FaaS applications, leading to various vendor lock-in problems. In this work, we define three cloud-agnostic models that compose FaaS platforms. Based on these models, we developed QuickFaaS, a multi-cloud interoperability desktop tool targeting cloud-agnostic functions and FaaS deployments. The proposed cloud-agnostic approach enables developers to reuse their serverless functions in different cloud providers with no need to change code or install extra software. We also provide an evaluation that validates the proposed solution by measuring the impact of a cloud-agnostic approach on the function’s performance, when compared to a cloud-non-agnostic one. The study shows that a cloud-agnostic approach does not significantly impact the function’s performance.
      Citation: Future Internet
      PubDate: 2022-11-30
      DOI: 10.3390/fi14120360
      Issue No: Vol. 14, No. 12 (2022)
       
  • Future Internet, Vol. 14, Pages 361: A Novel Strategy for VNF Placement in
           Edge Computing Environments

    • Authors: Anselmo Luiz Éden Battisti, Evandro Luiz Cardoso Macedo, Marina Ivanov Pereira Josué, Hugo Barbalho, Flávia C. Delicato, Débora Christina Muchaluat-Saade, Paulo F. Pires, Douglas Paulo de Mattos, Ana Cristina Bernardo de Oliveira
      First page: 361
      Abstract: Network function virtualization (NFV) is a novel technology that virtualizes computing, network, and storage resources to decouple the network functions from the underlying hardware, thus allowing the software implementation of such functions to run on commodity hardware. By doing this, NFV provides the necessary flexibility to enable agile, cost-effective, and on-demand service delivery models combined with automated management. Different management and orchestration challenges arise in such virtualized and distributed environments. A major challenge in the selection of the most suitable edge nodes is that of deploying virtual network functions (VNFs) to meet requests from multiple users. This article addresses the VNF placement problem by providing a novel integer linear programming (ILP) optimization model and a novel VNF placement algorithm. In our definition, the multi-objective optimization problem aims to (i) minimize the energy consumption in the edge nodes; (ii) minimize the total latency; and (iii) reducing the total cost of the infrastructure. Our new solution formulates the VNF placement problem by taking these three objectives into account simultaneously. In addition, the novel VNF placement algorithm leverages VNF sharing, which reuses VNF instances already placed to potentially reduce computational resource usage. Such a feature is still little explored in the community. Through simulation, numerical results show that our approach can perform better than other approaches found in the literature regarding resource consumption and the number of SFC requests met.
      Citation: Future Internet
      PubDate: 2022-11-30
      DOI: 10.3390/fi14120361
      Issue No: Vol. 14, No. 12 (2022)
       
  • Future Internet, Vol. 14, Pages 362: A Game-Theoretic Approach for Network
           Security Using Honeypots

    • Authors: Răzvan Florea, Mitică Craus
      First page: 362
      Abstract: Cybersecurity plays an increasing role in today’s digital space, and its methods must keep pace with the changes. Both public and private sector researchers have put efforts into strengthening the security of networks by proposing new approaches. This paper presents a method to solve a game theory model by defining the contents of the game payoff matrix and incorporating honeypots in the defense strategy. Using a probabilistic approach we propose the course-of-action Stackelberg game (CoASG), where every path of the graph leads to an undesirable state based on security issues found in every host. The reality of the system is represented by a cost function which helps us to define a payoff matrix and find the best possible combination of the strategies once the game is run. The results show the benefits of using this model in the early prevention stages for detecting cyberattack patterns.
      Citation: Future Internet
      PubDate: 2022-11-30
      DOI: 10.3390/fi14120362
      Issue No: Vol. 14, No. 12 (2022)
       
  • Future Internet, Vol. 14, Pages 363: TinyML for Ultra-Low Power AI and
           Large Scale IoT Deployments: A Systematic Review

    • Authors: Nikolaos Schizas, Aristeidis Karras, Christos Karras, Spyros Sioutas
      First page: 363
      Abstract: The rapid emergence of low-power embedded devices and modern machine learning (ML) algorithms has created a new Internet of Things (IoT) era where lightweight ML frameworks such as TinyML have created new opportunities for ML algorithms running within edge devices. In particular, the TinyML framework in such devices aims to deliver reduced latency, efficient bandwidth consumption, improved data security, increased privacy, lower costs and overall network cost reduction in cloud environments. Its ability to enable IoT devices to work effectively without constant connectivity to cloud services, while nevertheless providing accurate ML services, offers a viable alternative for IoT applications seeking cost-effective solutions. TinyML intends to deliver on-premises analytics that bring significant value to IoT services, particularly in environments with limited connection. This review article defines TinyML, presents an overview of its benefits and uses and provides background information based on up-to-date literature. Then, we demonstrate the TensorFlow Lite framework which supports TinyML along with analytical steps for an ML model creation. In addition, we explore the integration of TinyML with network technologies such as 5G and LPWAN. Ultimately, we anticipate that this analysis will serve as an informational pillar for the IoT/Cloud research community and pave the way for future studies.
      Citation: Future Internet
      PubDate: 2022-12-06
      DOI: 10.3390/fi14120363
      Issue No: Vol. 14, No. 12 (2022)
       
  • Future Internet, Vol. 14, Pages 364: Internet Video Delivery Improved by
           Super-Resolution with GAN

    • Authors: Joao da Mata Liborio, Cesar Melo, Marcos Silva
      First page: 364
      Abstract: In recent years, image and video super-resolution have gained attention outside the computer vision community due to the outstanding results produced by applying deep-learning models to solve the super-resolution problem. These models have been used to improve the quality of videos and images. In the last decade, video-streaming applications have also become popular. Consequently, they have generated traffic with an increasing quantity of data in network infrastructures, which continues to grow, e.g., global video traffic is forecast to increase from 75% in 2017 to 82% in 2022. In this paper, we leverage the power of deep-learning-based super-resolution methods and implement a model for video super-resolution, which we call VSRGAN+. We train our model with a dataset proposed to teach systems for high-level visual comprehension tasks. We also test it on a large-scale JND-based coded video quality dataset containing 220 video clips with four different resolutions. Additionally, we propose a cloud video-delivery framework that uses video super-resolution. According to our findings, the VSRGAN+ model can reconstruct videos without perceptual distinction of the ground truth. Using this model with added compression can decrease the quantity of data delivered to surrogate servers in a cloud video-delivery framework. The traffic decrease reaches 98.42% in total.
      Citation: Future Internet
      PubDate: 2022-12-06
      DOI: 10.3390/fi14120364
      Issue No: Vol. 14, No. 12 (2022)
       
  • Future Internet, Vol. 14, Pages 365: Enhancing the Lifetime and Energy
           Efficiency of Wireless Sensor Networks Using Aquila Optimizer Algorithm

    • Authors: Ashraf A. Taha, Hagar O. Abouroumia, Shimaa A. Mohamed, Lamiaa A. Amar
      First page: 365
      Abstract: As sensors are distributed among wireless sensor networks (WSNs), ensuring that the batteries and processing power last for a long time, to improve energy consumption and extend the lifetime of the WSN, is a significant challenge in the design of network clustering techniques. The sensor nodes are divided in these techniques into clusters with different cluster heads (CHs). Recently, certain considerations such as less energy consumption and high reliability have become necessary for selecting the optimal CH nodes in clustering-based metaheuristic techniques. This paper introduces a novel enhancement algorithm using Aquila Optimizer (AO), which enhances the energy balancing in clusters across sensor nodes during network communications to extend the network lifetime and reduce power consumption. Lifetime and energy-efficiency clustering algorithms, namely the low-energy adaptive clustering hierarchy (LEACH) protocol as a traditional protocol, genetic algorithm (GA), Coyote Optimization Algorithm (COY), Aquila Optimizer (AO), and Harris Hawks Optimization (HHO), are evaluated in a wireless sensor network. The paper concludes that the proposed AO algorithm outperforms other algorithms in terms of alive nodes analysis and energy consumption.
      Citation: Future Internet
      PubDate: 2022-12-07
      DOI: 10.3390/fi14120365
      Issue No: Vol. 14, No. 12 (2022)
       
  • Future Internet, Vol. 14, Pages 366: A Low-Cost Open-Source Architecture
           for a Digital Signage Emergency Evacuation System for Cruise Ships, Based
           on IoT and LTE/4G Technologies

    • Authors: Vasileios Cheimaras, Athanasios Trigkas, Panagiotis Papageorgas, Dimitrios Piromalis, Emmanouil Sofianopoulos
      First page: 366
      Abstract: During a ship evacuation, many people panic as they do not know the direction that leads to the emergency muster station. Moreover, sometimes passengers get crowded in corridors or stairs, so they cannot save their lives. This paper proposes an IoT-enabled architecture for digital signage systems that directs passengers to the muster stations of a cruise ship by following the less dangerous route. Thus, crews’ and passengers’ safety risks during a ship evacuation can be low, and human health hazards may be limited. The system is based on a low-cost and open-source architecture that can also be used in a variety of fields in industrial IoT applications. The proposed modular digital signage architecture utilizes Light Emitting Diode (LED) strips that are remotely managed through a private Long-Term Evolution (LTE)/Fourth Generation (4G) cellular network. Publish–subscribe communication protocols were used for the control of the digital strips and particularly through a Message Queuing Telemetry Transport (MQTT) broker who publishes/subscribes every message to specific topics of the realized IoT platform, while the overall digital signage system centralization was implemented with an appropriate dashboard supported from an open-source RESTful API.
      Citation: Future Internet
      PubDate: 2022-12-07
      DOI: 10.3390/fi14120366
      Issue No: Vol. 14, No. 12 (2022)
       
  • Future Internet, Vol. 14, Pages 367: Professionals as Change Agents or
           Instruments of Reproduction' Medical Residents’ Reasoning for
           Not Sharing the Electronic Health Record Screen with Patients

    • Authors: Celeste Campos-Castillo, Noelle Chesley, Onur Asan
      First page: 367
      Abstract: The stability of physicians’ authority over patients despite decades of changes in medicine conflicts with newer institutionalist accounts of professionals as change agents rather than instruments of reproduction. We analyzed whether the cultural scripts that twenty-one residents used to justify their approach to a new change, the electronic health record (EHR), signaled a leveling of the patient-physician hierarchy. Residents are intriguing because their position makes them open to change. Indeed, residents justified using the EHR in ways that level the patient-physician hierarchy, but also offered rationales that sustain it. For the latter, residents described using the EHR to substantiate their expertise, situate themselves as brokers between patients and the technology, and preserve the autonomy of clinicians. Our findings highlight how professionals with little direct experience before a change can selectively apply incumbent scripts to sustain extant structures, while informing newer institutionalist accounts of professionals and the design of EHR systems.
      Citation: Future Internet
      PubDate: 2022-12-07
      DOI: 10.3390/fi14120367
      Issue No: Vol. 14, No. 12 (2022)
       
  • Future Internet, Vol. 14, Pages 368: Holistic Utility Satisfaction in
           Cloud Data CentreNetwork Using Reinforcement Learning

    • Authors: Pejman Goudarzi, Mehdi Hosseinpour, Roham Goudarzi, Jaime Lloret
      First page: 368
      Abstract: Cloud computing leads to efficient resource allocation for network users. In order to achieve efficient allocation, many research activities have been conducted so far. Some researchers focus on classical optimisation theory techniques (such as multi-objective optimisation, evolutionary optimisation, game theory, etc.) to satisfy network providers and network users’ service-level agreement (SLA) requirements. Normally, in a cloud data centre network (CDCN), it is difficult to jointly satisfy both the cloud provider and cloud customer’ utilities, and this leads to complex combinatorial problems, which are usually NP-hard. Recently, machine learning and artificial intelligence techniques have received much attention from the networking community because of their capability to solve complicated networking problems. In the current work, at first, the holistic utility satisfaction for the cloud data centre provider and customers is formulated as a reinforcement learning (RL) problem with a specific reward function, which is a convex summation of users’ utility functions and cloud provider’s utility. The user utility functions are modelled as a function of cloud virtualised resources (such as storage, CPU, RAM), connection bandwidth, and also, the network-based expected packet loss and round-trip time factors associated with the cloud users. The cloud provider utility function is modelled as a function of resource prices and energy dissipation costs. Afterwards, a Q-learning implementation of the mentioned RL algorithm is introduced, which is able to converge to the optimal solution in an online and fast manner. The simulation results exhibit the enhanced convergence speed and computational complexity properties of the proposed method in comparison with similar approaches from the joint cloud customer/provider utility satisfaction perspective. To evaluate the scalability property of the proposed method, the results are also repeated for different cloud user population scenarios (small, medium, and large).
      Citation: Future Internet
      PubDate: 2022-12-08
      DOI: 10.3390/fi14120368
      Issue No: Vol. 14, No. 12 (2022)
       
  • Future Internet, Vol. 14, Pages 369: LSSDNF: A Lightweight Secure Software
           Defined Network Framework for Future Internet in 5G–6G

    • Authors: Surjit Singh, Vivek Mehla, Srete Nikolovski
      First page: 369
      Abstract: As information technology advances quickly, so does the 5G–6G network management system, which is moving toward greater integration, decentralization, diversity, and intelligence. As flexibility is a crucial criterion for 5G–6G network architecture, we use the Software Defined Network (SDN) paradigm to make the programmability more flexible. Due to their ability to replace the current TCP/IP architecture with one that separates the control plane and data plane, software-defined networks have gained much popularity. However, they are susceptible to routing attacks. Therefore, this work proposes Lightweight Security Framework that combines blockchain technology with Software-Defined Networking (LSSDNF) to address this problem. The proposed framework adds the routing data that the controller withheld to the multichain blockchain. Here, a mininet network simulator is used to model the proposed framework. The data transfer rate or network throughput, bandwidth variation, and jitter have all been used to assess the performance of single-controller-SDN networks and multi-controller-SDN networks. The results demonstrate that the proposed framework performs better than the conventional single-controller-SDN architecture in terms of throughput, bandwidth fluctuation, and jitter.
      Citation: Future Internet
      PubDate: 2022-12-08
      DOI: 10.3390/fi14120369
      Issue No: Vol. 14, No. 12 (2022)
       
  • Future Internet, Vol. 14, Pages 370: Smart Objects and Technologies for
           Social Good

    • Authors: Ivan Miguel Pires
      First page: 370
      Abstract: Social goods are commodities and services that for-profit businesses, government agencies, or private enterprises may offer [...]
      Citation: Future Internet
      PubDate: 2022-12-09
      DOI: 10.3390/fi14120370
      Issue No: Vol. 14, No. 12 (2022)
       
  • Future Internet, Vol. 14, Pages 371: Implementation of the Canny Edge
           Detector Using a Spiking Neural Network

    • Authors: Krishnamurthy V. Vemuru
      First page: 371
      Abstract: Edge detectors are widely used in computer vision applications to locate sharp intensity changes and find object boundaries in an image. The Canny edge detector is the most popular edge detector, and it uses a multi-step process, including the first step of noise reduction using a Gaussian kernel and a final step to remove the weak edges by the hysteresis threshold. In this work, a spike-based computing algorithm is presented as a neuromorphic analogue of the Canny edge detector, where the five steps of the conventional algorithm are processed using spikes. A spiking neural network layer consisting of a simplified version of a conductance-based Hodgkin–Huxley neuron as a building block is used to calculate the gradients. The effectiveness of the spiking neural-network-based algorithm is demonstrated on a variety of images, showing its successful adaptation of the principle of the Canny edge detector. These results demonstrate that the proposed algorithm performs as a complete spike domain implementation of the Canny edge detector.
      Citation: Future Internet
      PubDate: 2022-12-11
      DOI: 10.3390/fi14120371
      Issue No: Vol. 14, No. 12 (2022)
       
  • Future Internet, Vol. 14, Pages 372: Information and Future Internet
           Security, Trust and Privacy

    • Authors: Weizhi Meng, Thanassis Giannetsos, Christian D. Jensen
      First page: 372
      Abstract: The Internet has rapidly grown into a distributed and collaborative network with over one billion users, e.g., the Internet of Things (IoT). The future Internet will become the core of the next information infrastructure in regard to computation and communication, being capable of extensibility, survivability, mobility, and adaptability. However, with the increasing complexity of the future Internet and boost in information sharing, there is a threat to such infrastructure in the aspects of security, trust, and privacy. This editorial discusses the state-of-the-art advancements in information and the future internet.
      Citation: Future Internet
      PubDate: 2022-12-12
      DOI: 10.3390/fi14120372
      Issue No: Vol. 14, No. 12 (2022)
       
  • Future Internet, Vol. 14, Pages 373: Joint Random Forest and Particle
           Swarm Optimization for Predictive Pathloss Modeling of Wireless Signals
           from Cellular Networks

    • Authors: Okiemute Roberts Omasheye, Samuel Azi, Joseph Isabona, Agbotiname Lucky Imoize, Chun-Ta Li, Cheng-Chi Lee
      First page: 373
      Abstract: The accurate and reliable predictive estimation of signal attenuation loss is of prime importance in radio resource management. During wireless network design and planning, a reliable path loss model is required for optimal predictive estimation of the received signal strength, coverage, quality, and signal interference-to-noise ratio. A set of trees (100) on the target measured data was employed to determine the most informative and important subset of features, which were in turn employed as input data to the Particle Swarm (PS) model for predictive path loss analysis. The proposed Random Forest (RF-PS) based model exhibited optimal precision performance in the real-time prognostic analysis of measured path loss over operational 4G LTE networks in Nigeria. The relative performance of the proposed RF-PS model was compared to the standard PS and hybrid radial basis function-particle swarm optimization (RBF-PS) algorithm for benchmarking. Generally, results indicate that the proposed RF-PS model gave better prediction accuracy than the standard PS and RBF-PS models across the investigated environments. The projected hybrid model would find useful applications in path loss modeling in related wireless propagation environments.
      Citation: Future Internet
      PubDate: 2022-12-12
      DOI: 10.3390/fi14120373
      Issue No: Vol. 14, No. 12 (2022)
       
  • Future Internet, Vol. 14, Pages 374: Gamification with Scratch or App
           Inventor in Higher Education: A Systematic Review

    • Authors: David Pérez-Jorge, María Carmen Martínez-Murciano
      First page: 374
      Abstract: Programming skills should be taught and developed; Scratch and App Inventor are two tools that can contribute significantly to developing this competence in university students. This study aims to investigate the use and effect of the programming language Scratch and App Inventor on the development of skills and competencies for learning (autonomy, attention, motivation, critical thinking, creative thinking, computational thinking, communication, problem solving and social interaction) in higher education. To achieve this goal, a systematic review of articles in English and Spanish was carried out using the PRISMA statement (research publication guidelines designed to improve the integrity of systematic review and meta-analysis reports). A search for studies was conducted in the Web of Science (WOS), Dialnet, and SCOPUS. A total of 405 papers were analyzed, of which 11 were finally selected. The results showed that both Scratch and App Inventor favor the development of skills and competencies for learning in the context of higher education, despite being underutilized strategies that all knowledge disciplines should promote.
      Citation: Future Internet
      PubDate: 2022-12-13
      DOI: 10.3390/fi14120374
      Issue No: Vol. 14, No. 12 (2022)
       
  • Future Internet, Vol. 14, Pages 375: Examining Gender Bias of
           Convolutional Neural Networks via Facial Recognition

    • Authors: Tony Gwyn, Kaushik Roy
      First page: 375
      Abstract: Image recognition technology systems have existed in the realm of computer security since nearly the inception of electronics, and have seen vast improvements in recent years. Currently implemented facial detection systems regularly achieve accuracy rates close to 100 percent. This includes even challenging environments, such as with low light or skewed images. Despite this near perfect performance, the problem of gender bias with respect to accuracy is still inherent in many current facial recognition algorithms. This bias needs to be addressed to make facial recognition a more complete and useful system. In particular, current image recognition system tend to have poor accuracy concerning underrepresented groups, including minorities and female individuals. The goal of this research is to increase the awareness of this bias issue, as well as to create a new model for image recognition that is gender independent. To achieve this goal, a variety of Convolutional Neural Networks (CNNs) will be tested for accuracy as it pertains to gender bias. In the future, the most accurate CNNs will then be implemented into a new network with the goal of creating a program which is better able to distinguish individuals with a high accuracy, but without gender bias. At present, our research has identified two specific CNNs, VGG-16 and ResNet50, which we believe will be ideal for the creation of this new CNN algorithm.
      Citation: Future Internet
      PubDate: 2022-12-13
      DOI: 10.3390/fi14120375
      Issue No: Vol. 14, No. 12 (2022)
       
  • Future Internet, Vol. 14, Pages 376: ERGCN: Enhanced Relational Graph
           Convolution Network, an Optimization for Entity Prediction Tasks on
           Temporal Knowledge Graphs

    • Authors: Yinglin Wang, Xinyu Xu
      First page: 376
      Abstract: Reasoning on temporal knowledge graphs, which aims to infer new facts from existing knowledge, has attracted extensive attention and in-depth research recently. One of the important tasks of reasoning on temporal knowledge graphs is entity prediction, which focuses on predicting the missing objects in facts at current time step when relevant histories are known. The problem is that, for entity prediction task on temporal knowledge graphs, most previous studies pay attention to aggregating various semantic information from entities but ignore the impact of semantic information from relation types. We believe that relation types is a good supplement for our task and making full use of semantic information of facts can promote the results. Therefore, a framework of Enhanced Relational Graph Convolution Network (ERGCN) is put forward in this paper. Rather than only considering representations of entities, the context semantic information of both relations and entities is considered and merged together in this framework. Experimental results show that the proposed approach outperforms the state-of-the-art methods.
      Citation: Future Internet
      PubDate: 2022-12-13
      DOI: 10.3390/fi14120376
      Issue No: Vol. 14, No. 12 (2022)
       
  • Future Internet, Vol. 14, Pages 377: FedCO: Communication-Efficient
           Federated Learning via Clustering Optimization

    • Authors: Ahmed A. Al-Saedi, Veselka Boeva, Emiliano Casalicchio
      First page: 377
      Abstract: Federated Learning (FL) provides a promising solution for preserving privacy in learning shared models on distributed devices without sharing local data on a central server. However, most existing work shows that FL incurs high communication costs. To address this challenge, we propose a clustering-based federated solution, entitled Federated Learning via Clustering Optimization (FedCO), which optimizes model aggregation and reduces communication costs. In order to reduce the communication costs, we first divide the participating workers into groups based on the similarity of their model parameters and then select only one representative, the best performing worker, from each group to communicate with the central server. Then, in each successive round, we apply the Silhouette validation technique to check whether each representative is still made tight with its current cluster. If not, the representative is either moved into a more appropriate cluster or forms a cluster singleton. Finally, we use split optimization to update and improve the whole clustering solution. The updated clustering is used to select new cluster representatives. In that way, the proposed FedCO approach updates clusters by repeatedly evaluating and splitting clusters if doing so is necessary to improve the workers’ partitioning. The potential of the proposed method is demonstrated on publicly available datasets and LEAF datasets under the IID and Non-IID data distribution settings. The experimental results indicate that our proposed FedCO approach is superior to the state-of-the-art FL approaches, i.e., FedAvg, FedProx, and CMFL, in reducing communication costs and achieving a better accuracy in both the IID and Non-IID cases.
      Citation: Future Internet
      PubDate: 2022-12-13
      DOI: 10.3390/fi14120377
      Issue No: Vol. 14, No. 12 (2022)
       
  • Future Internet, Vol. 14, Pages 378: Integrated SDN-NFV 5G Network
           Performance and Management-Complexity Evaluation

    • Authors: Nico Surantha, Noffal A. Putra
      First page: 378
      Abstract: Digitalization is one of the factors that affects the acceleration of the application of telecommunications technologies such as 5G. The 5G technology that has been developed today does not yet meet different performance and manageability standards, particularly for data center networks as a supportive technology. Software-defined networking (SDN) and network function virtualization (NFV) are two complementary technologies that are currently used by almost all data centers in the telecommunications industry to rectify performance and manageability issues. In this study, we deliver an integrated SDN-NFV architecture to simplify network management activities in telecommunication companies. To improve network performance at the computing level, we performed a modification of a networking system at the computing level, underlying NFV devices by replacing the default virtual switch with a data plane development kit (DPDK) and single root I/O virtualization (SR-IOV). This study evaluated the proposed architecture design in terms of network performance and manageability. Based on 30 days of observation in prime time, the proposed solution increased throughput up to 200 Mbps for the server leaf and 1.6 Gbps for the border leaf compared to the legacy architecture. Meanwhile, the latency decreased to 12 ms for the server leaf and 17 ms for the border leaf. For manageability, we tested three different scenarios and achieved savings of 13 min for Scenario 1, 22 min for Scenario 2 and 9 min for Scenario 3.
      Citation: Future Internet
      PubDate: 2022-12-14
      DOI: 10.3390/fi14120378
      Issue No: Vol. 14, No. 12 (2022)
       
  • Future Internet, Vol. 14, Pages 379: 6G Wireless Communication Systems:
           Applications, Opportunities and Challenges

    • Authors: Kelvin Anoh, Chan Hwang See, Yousef Dama, Raed A. Abd-Alhameed, Simeon Keates
      First page: 379
      Abstract: As the technical specifications of the 5th Generation (5G) wireless communication standard are being wrapped up, there are growing efforts amongst researchers, industrialists, and standardisation bodies on the enabling technologies of a 6G standard or the so-called Beyond 5G (B5G) one. Although the 5G standard has presented several benefits, there are still some limitations within it. Such limitations have motivated the setting up of study groups to determine suitable technologies that should operate in the year 2030 and beyond, i.e., after 5G. Consequently, this Special Issue of Future Internet concerning what possibilities lie ahead for a 6G wireless network includes four high-quality research papers (three of which are review papers with over 412 referred sources and one regular research). This editorial piece summarises the major contributions of the articles and the Special Issue, outlining future directions for new research.
      Citation: Future Internet
      PubDate: 2022-12-15
      DOI: 10.3390/fi14120379
      Issue No: Vol. 14, No. 12 (2022)
       
  • Future Internet, Vol. 14, Pages 380: Comparative Analysis of
           Skeleton-Based Human Pose Estimation

    • Authors: Jen-Li Chung, Lee-Yeng Ong, Meng-Chew Leow
      First page: 380
      Abstract: Human pose estimation (HPE) has become a prevalent research topic in computer vision. The technology can be applied in many areas, such as video surveillance, medical assistance, and sport motion analysis. Due to higher demand for HPE, many HPE libraries have been developed in the last 20 years. In the last 5 years, more and more skeleton-based HPE algorithms have been developed and packaged into libraries to provide ease of use for researchers. Hence, the performance of these libraries is important when researchers intend to integrate them into real-world applications for video surveillance, medical assistance, and sport motion analysis. However, a comprehensive performance comparison of these libraries has yet to be conducted. Therefore, this paper aims to investigate the strengths and weaknesses of four popular state-of-the-art skeleton-based HPE libraries for human pose detection, including OpenPose, PoseNet, MoveNet, and MediaPipe Pose. A comparative analysis of these libraries based on images and videos is presented in this paper. The percentage of detected joints (PDJ) was used as the evaluation metric in all comparative experiments to reveal the performance of the HPE libraries. MoveNet showed the best performance for detecting different human poses in static images and videos.
      Citation: Future Internet
      PubDate: 2022-12-15
      DOI: 10.3390/fi14120380
      Issue No: Vol. 14, No. 12 (2022)
       
  • Future Internet, Vol. 14, Pages 381: A Multi-Sensory In-Store Virtual
           Reality Customer Journey for Retailing: A Field Study in a Furniture
           Flagship Store

    • Authors: Michele Fiorentino, Marina Ricci, Alessandro Evangelista, Vito Modesto Manghisi, Antonio Emmanuele Uva
      First page: 381
      Abstract: The choice of furniture in a retail store is usually based on a product catalog and simplistic product renderings with different configurations. We present a preliminary field study that tests a Multi-Sensory In-Store Virtual Reality Customer Journey (MSISVRCJ) through a virtual catalog and a product configurator to support furnishings sales. The system allows customers to stay immersed in the virtual environment (VE) while the sales expert changes the colors, textures, and finishes of the furniture, also exploring different VEs. In addition, customers can experience realistic tactile feedback with in-store samples of furniture they can test. The journey is implemented for a furniture manufacturer and tested in a flagship store. Fifty real customers show positive feedback in terms of general satisfaction, perceived realism, and acceptance. This method can increase purchase confidence, reduce entrepreneurial costs, and leverage in-store versus online shopping.
      Citation: Future Internet
      PubDate: 2022-12-16
      DOI: 10.3390/fi14120381
      Issue No: Vol. 14, No. 12 (2022)
       
  • Future Internet, Vol. 14, Pages 382: Cyber-Physical Systems: Prospects,
           Challenges and Role in Software-Defined Networking and Blockchains

    • Authors: Uttam Ghosh, Deepak Tosh, Nawab Muhammad Faseeh Qureshi, Ali Kashif Bashir, Al-Sakib Khan Pathan, Zhaolong Ning
      First page: 382
      Abstract: In recent years, cyber-physical systems (CPSs) have gained a lot of attention from academia, industry and government agencies, considered to be the world’s third wave of information technology, following computers and the internet [...]
      Citation: Future Internet
      PubDate: 2022-12-18
      DOI: 10.3390/fi14120382
      Issue No: Vol. 14, No. 12 (2022)
       
  • Future Internet, Vol. 14, Pages 383: Graph-Based Taxonomic Semantic Class
           Labeling

    • Authors: Tajana Ban Kirigin, Sanda Bujačić Babić, Benedikt Perak
      First page: 383
      Abstract: We present a graph-based method for the lexical task of labeling senses of polysemous lexemes. The labeling task aims at generalizing sense features of a lexical item in a corpus using more abstract concepts. In this method, a coordination dependency-based lexical graph is first constructed with clusters of conceptually associated lexemes representing related senses and conceptual domains of a source lexeme. The label abstraction is based on the syntactic patterns of the x is_a y dependency relation. For each sense cluster, an additional lexical graph is constructed by extracting label candidates from a corpus and selecting the most prominent is_a collocates in the constructed label graph. The obtained label lexemes represent the sense abstraction of the cluster of conceptually associated lexemes. In a similar graph-based procedure, the semantic class representation is validated by constructing a WordNet hypernym relation graph. These additional labels indicate the most appropriate hypernym category of a lexical sense community. The proposed labeling method extracts hierarchically abstract conceptual content and the sense semantic features of the polysemous source lexeme, which can facilitate lexical understanding and build corpus-based taxonomies.
      Citation: Future Internet
      PubDate: 2022-12-19
      DOI: 10.3390/fi14120383
      Issue No: Vol. 14, No. 12 (2022)
       
  • Future Internet, Vol. 14, Pages 384: Single-Shot Global and Local Context
           Refinement Neural Network for Head Detection

    • Authors: Jingyuan Hu, Zhouwang Yang
      First page: 384
      Abstract: Head detection is a fundamental task, and it plays an important role in many head-related problems. The difficulty in creating the local and global context in the face of significant lighting, orientation, and occlusion uncertainty, among other factors, still makes this task a remarkable challenge. To tackle these problems, this paper proposes an effective detector, the Context Refinement Network (CRN), that captures not only the refined global context but also the enhanced local context. We use simplified non-local (SNL) blocks at hierarchical features, which can successfully establish long-range dependencies between heads to improve the capability of building the global context. We suggest a multi-scale dilated convolutional module for the local context surrounding heads that extracts local context from various head characteristics. In comparison to other models, our method outperforms them on the Brainwash and the HollywoodHeads datasets.
      Citation: Future Internet
      PubDate: 2022-12-19
      DOI: 10.3390/fi14120384
      Issue No: Vol. 14, No. 12 (2022)
       
  • Future Internet, Vol. 14, Pages 333: A Cost-Aware Framework for QoS-Based
           and Energy-Efficient Scheduling in Cloud–Fog Computing

    • Authors: Husam Suleiman
      First page: 333
      Abstract: Cloud–fog computing is a large-scale service environment developed to deliver fast, scalable services to clients. The fog nodes of such environments are distributed in diverse places and operate independently by deciding on which data to process locally and which data to send remotely to the cloud for further analysis, in which a Service-Level Agreement (SLA) is employed to govern Quality of Service (QoS) requirements of the cloud provider to such nodes. The provider experiences varying incoming workloads that come from heterogeneous fog and Internet of Things (IoT) devices, each of which submits jobs that entail various service characteristics and QoS requirements. To execute fog workloads and meet their SLA obligations, the provider allocates appropriate resources and utilizes load scheduling strategies that effectively manage the executions of fog jobs on cloud resources. Failing to fulfill such demands causes extra network bottlenecks, service delays, and energy constraints that are difficult to maintain at run-time. This paper proposes a joint energy- and QoS-optimized performance framework that tolerates delay and energy risks on the cost performance of the cloud provider. The framework employs scheduling mechanisms that consider the SLA penalty and energy impacts of data communication, service, and waiting performance metrics on cost reduction. The findings prove the framework’s effectiveness in mitigating energy consumption due to QoS penalties and therefore reducing the gross scheduling cost.
      Citation: Future Internet
      PubDate: 2022-11-14
      DOI: 10.3390/fi14110333
      Issue No: Vol. 14, No. 11 (2022)
       
  • Future Internet, Vol. 14, Pages 334: Editorial for the Special Issue
           “Selected Papers from the 9th Annual Conference ‘Comparative
           Media Studies in Today’s World’ (CMSTW’2021)”

    • Authors: Svetlana S. Bodrunova
      First page: 334
      Abstract: This Special Issue of Future Internet features the best papers from the 9th annual conference “Comparative Media Studies in Today’s World (CMSTW’2021)”, which was held between 20 and 21 April 2021, in St [...]
      Citation: Future Internet
      PubDate: 2022-11-16
      DOI: 10.3390/fi14110334
      Issue No: Vol. 14, No. 11 (2022)
       
  • Future Internet, Vol. 14, Pages 335: The Future of Cybersecurity in the
           Age of Quantum Computers

    • Authors: Fazal Raheman
      First page: 335
      Abstract: The first week of August 2022 saw the world’s cryptographers grapple with the second shocker of the year. Another one of the four post-quantum cryptography (PQC) algorithms selected by the NIST (National Institute of Standards and Technology) in a rigorous 5-year process was cracked by a team from Belgium. They took just 62 min and a standard laptop to break the PQC algorithm to win a USD 50,000 bounty from Microsoft. The first shocker came 6 months earlier, when another of the NIST finalists (Rainbow) was taken down. Unfortunately, both failed PQC algorithms are commercially available to consumers. With 80 of the 82 PQC candidates failing the NIST standardization process, the future of the remaining two PQC algorithms is, at best, questionable, placing the rigorous 5-year NIST exercise to build a quantum-safe encryption standard in jeopardy. Meanwhile, there is no respite from the quantum threat that looms large. It is time we take a step back and review the etiology of the problem de novo. Although state-of-the-art computer security heavily relies on cryptography, it can indeed transcend beyond encryption. This paper analyzes an encryption-agnostic approach that can potentially render computers quantum-resistant. Zero-vulnerability computing (ZVC) secures computers by banning all third-party permissions, a root cause of most vulnerabilities. ZVC eliminates the complexities of the multi-layered architecture of legacy computers and builds a minimalist, compact solid-state software on a chip (3SoC) that is robust, energy-efficient, and potentially resistant to malware as well as quantum threats.
      Citation: Future Internet
      PubDate: 2022-11-16
      DOI: 10.3390/fi14110335
      Issue No: Vol. 14, No. 11 (2022)
       
  • Future Internet, Vol. 14, Pages 336: Comparison of Distributed
           Tamper-Proof Storage Methods for Public Key Infrastructures

    • Authors: Fabian Honecker, Julian Dreyer, Ralf Tönjes
      First page: 336
      Abstract: Modern Public Key Infrastructures (PKIs) allow users to create and maintain centrally stored cryptographic certificates. These infrastructures use a so-called certificate chain. At the root of the chain, a root Certification Authority (CA) is responsible for issuing the base certificate. Every verification and certification step within the chain is based upon the security of said root CA. Thus, its operation security is of great concern. Since the root certificates are stored locally on the root CA, any Denial of Service (DoS) attack may render the whole certificate chain, which is based on of the attacked root CA, inoperable. Therefore, this article evaluates different approaches to a decentralized data storage system that is based on the Distributed Ledger Technology (DLT). To show the real-world potential of the proposed approaches, we also evaluate the different technologies using a novel PKI mechanism called Near Field Communication Key Exchange (NFC-KE). The results indicate that modern distributed data storage solutions such as Interplanetary Filesystem (IPFS) and SIA can have significant performance and decentralization benefits in comparison to purely Blockchain-based technologies like Hyperledger Fabric. However, they lack any Smart Contract functionality, which requires a software developer to implement verification mechanisms in centralized software solutions.
      Citation: Future Internet
      PubDate: 2022-11-18
      DOI: 10.3390/fi14110336
      Issue No: Vol. 14, No. 11 (2022)
       
  • Future Internet, Vol. 14, Pages 337: Internet of Things and
           Cyber–Physical Systems

    • Authors: Grobelna
      First page: 337
      Abstract: The area of the Internet of Things (IoT) and cyber–physical systems (CPS) has created a great opportunity for interdisciplinary research concerning both fundamental theoretical studies as well as their application in practice [...]
      Citation: Future Internet
      PubDate: 2022-11-18
      DOI: 10.3390/fi14110337
      Issue No: Vol. 14, No. 11 (2022)
       
  • Future Internet, Vol. 14, Pages 338: SHFL: K-Anonymity-Based Secure
           Hierarchical Federated Learning Framework for Smart Healthcare Systems

    • Authors: Muhammad Asad, Muhammad Aslam, Syeda Fizzah Jilani, Saima Shaukat, Manabu Tsukada
      First page: 338
      Abstract: Dynamic and smart Internet of Things (IoT) infrastructures allow the development of smart healthcare systems, which are equipped with mobile health and embedded healthcare sensors to enable a broad range of healthcare applications. These IoT applications provide access to the clients’ health information. However, the rapid increase in the number of mobile devices and social networks has generated concerns regarding the secure sharing of a client’s location. In this regard, federated learning (FL) is an emerging paradigm of decentralized machine learning that guarantees the training of a shared global model without compromising the data privacy of the client. To this end, we propose a K-anonymity-based secure hierarchical federated learning (SHFL) framework for smart healthcare systems. In the proposed hierarchical FL approach, a centralized server communicates hierarchically with multiple directly and indirectly connected devices. In particular, the proposed SHFL formulates the hierarchical clusters of location-based services to achieve distributed FL. In addition, the proposed SHFL utilizes the K-anonymity method to hide the location of the cluster devices. Finally, we evaluated the performance of the proposed SHFL by configuring different hierarchical networks with multiple model architectures and datasets. The experiments validated that the proposed SHFL provides adequate generalization to enable network scalability of accurate healthcare systems without compromising the data and location privacy.
      Citation: Future Internet
      PubDate: 2022-11-18
      DOI: 10.3390/fi14110338
      Issue No: Vol. 14, No. 11 (2022)
       
  • Future Internet, Vol. 14, Pages 339: A Dynamic Federated Identity
           Management Using OpenID Connect

    • Authors: Ahmad Alsadeh, Nasri Yatim, Yousef Hassouneh
      First page: 339
      Abstract: Identity federation allows one to link a user’s digital identities across several identity management systems. Federated identity management (FIM) ensures that users have easy access to the available resources. However, scaling FIM to numerous partners is a challenging process due to the interoperability issue between different federation architectures. This study proposes a dynamic identity federation model to eliminate the manual configuration steps needed to establish an organizational identity federation by utilizing the OpenID Connect (OIDC) framework. The proposed model consists of three major steps to establish dynamic FIM: first, the discovery of the OpenID service provider, which indicates the location of the partner organization; second, the registration of the OpenID relying party, which allows the organization and its partner to negotiate information for establishing the federation; finally, establishing the dynamic trust federation. The proposed dynamic FIM model allows applications to provide services to end-users coming from various domains while maintaining a trust between clients and service providers. Through our proposed dynamic identity federation model, organizations can save hundreds of hours by achieving dynamic federation in runtime and serving a large number of end-users.
      Citation: Future Internet
      PubDate: 2022-11-21
      DOI: 10.3390/fi14110339
      Issue No: Vol. 14, No. 11 (2022)
       
  • Future Internet, Vol. 14, Pages 340: Multimodel Phishing URL Detection
           Using LSTM, Bidirectional LSTM, and GRU Models

    • Authors: Sanjiban Sekhar Roy, Ali Ismail Awad, Lamesgen Adugnaw Amare, Mabrie Tesfaye Erkihun, Mohd Anas
      First page: 340
      Abstract: In today’s world, phishing attacks are gradually increasing, resulting in individuals losing valuables, assets, personal information, etc., to unauthorized parties. In phishing, attackers craft malicious websites disguised as well-known, legitimate sites and send them to individuals to steal personal information and other related private details. Therefore, an efficient and accurate method is required to determine whether a website is malicious. Numerous methods have been proposed for detecting malicious uniform resource locators (URLs) using deep learning, machine learning, and other approaches. In this study, we have used malicious and benign URLs datasets and have proposed a detection mechanism for detecting malicious URLs using recurrent neural network models such as long short-term memory (LSTM), bidirectional long short-term memory (Bi-LSTM), and the gated recurrent unit (GRU). Experimental results have shown that the proposed mechanism achieved an accuracy of 97.0% for LSTM, 99.0% for Bi-LSTM, and 97.5% for GRU, respectively.
      Citation: Future Internet
      PubDate: 2022-11-21
      DOI: 10.3390/fi14110340
      Issue No: Vol. 14, No. 11 (2022)
       
  • Future Internet, Vol. 14, Pages 341: Blockchain Technology: Benefits,
           Challenges, Applications, and Integration of Blockchain Technology with
           Cloud Computing

    • Authors: Gousia Habib, Sparsh Sharma, Sara Ibrahim, Imtiaz Ahmad, Shaima Qureshi, Malik Ishfaq
      First page: 341
      Abstract: The real-world use cases of blockchain technology, such as faster cross-border payments, identity management, smart contracts, cryptocurrencies, and supply chain–blockchain technology are here to stay and have become the next innovation, just like the Internet. There have been attempts to formulate digital money, but they have not been successful due to security and trust issues. However, blockchain needs no central authority, and its operations are controlled by the people who use it. Furthermore, it cannot be altered or forged, resulting in massive market hype and demand. Blockchain has moved past cryptocurrency and discovered implementations in other real-life applications; this is where we can expect blockchain technology to be simplified and not remain a complex concept. Blockchain technology’s desirable characteristics are decentralization, integrity, immutability, verification, fault tolerance, anonymity, audibility, and transparency. We first conduct a thorough analysis of blockchain technology in this paper, paying particular attention to its evolution, applications and benefits, the specifics of cryptography in terms of public key cryptography, and the challenges of blockchain in distributed transaction ledgers, as well as the extensive list of blockchain applications in the financial transaction system. This paper presents a detailed review of blockchain technology, the critical challenges faced, and its applications in different fields. Blockchain in the transaction system is explained in detail with a summary of different cryptocurrencies. Some of the suggested solutions are given in the overall study of the paper.
      Citation: Future Internet
      PubDate: 2022-11-21
      DOI: 10.3390/fi14110341
      Issue No: Vol. 14, No. 11 (2022)
       
  • Future Internet, Vol. 14, Pages 342: Editorial for the Special Issue on 5G
           Enabling Technologies and Wireless Networking

    • Authors: Michael Mackay
      First page: 342
      Abstract: The ongoing deployment of 5G networks is seen as a key enabler for realizing upcoming interconnected services at scale, including the massive deployment of the Internet of Things, providing V2X communications to support autonomous vehicles, and the increase in smart homes, smart cities, and Industry 4 [...]
      Citation: Future Internet
      PubDate: 2022-11-21
      DOI: 10.3390/fi14110342
      Issue No: Vol. 14, No. 11 (2022)
       
  • Future Internet, Vol. 14, Pages 343: Integrating Chatbot Media Automations
           in Professional Journalism: An Evaluation Framework

    • Authors: Efthimis Kotenidis, Nikolaos Vryzas, Andreas Veglis, Charalampos Dimoulas
      First page: 343
      Abstract: Interactivity has been a very sought-after feature in professional journalism ever since the media industry transitioned from print into the online space. Within this context, chatbots started to infiltrate the media sphere and provide news organizations with new and innovative ways to create and share their content, with an even larger emphasis on back-and-forth communication and news reporting personalization. The present research highlights two important factors that can determine the efficient integration of chatbots in professional journalism: the feasibility of chatbot programming by journalists without a background in computer science using coding-free platforms and the usability of the created chatbot agents for news reporting to the audience. This paper aims to review some of the most popular, coding-free chatbot creation platforms that are available to journalists today. To that end, a three-phase evaluation framework is introduced. First off, the interactivity features that they offer to media industry workers are evaluated using an appropriate metrics framework. Secondly, a two- part workshop is conducted where journalists use the aforementioned platforms to create their own chatbot news reporting agents with minimum training, and lastly, the created chatbots are evaluated by a larger audience concerning the usability and overall user experience.
      Citation: Future Internet
      PubDate: 2022-11-21
      DOI: 10.3390/fi14110343
      Issue No: Vol. 14, No. 11 (2022)
       
  • Future Internet, Vol. 14, Pages 344: Data Synchronization: A Complete
           Theoretical Solution for Filesystems

    • Authors: Elod P. Csirmaz, Laszlo Csirmaz
      First page: 344
      Abstract: Data reconciliation in general, and filesystem synchronization in particular, lacks rigorous theoretical foundation. This paper presents, for the first time, a complete analysis of synchronization for two replicas of a theoretical filesystem. Synchronization has two main stages: identifying the conflicts, and resolving them. All existing (both theoretical and practical) synchronizers are operation-based: they define, using some rationale or heuristics, how conflicts are to be resolved without considering the effect of the resolution on subsequent conflicts. Instead, our approach is declaration-based: we define what constitutes the resolution of all conflicts, and for each possible scenario we prove the existence of sequences of operations/commands which convert the replicas into a common synchronized state. These sequences consist of operations rolling back some local changes, followed by operations performed on the other replica. The set of rolled-back operations provides the user with clear and intuitive information on the proposed changes, so she can easily decide whether to accept them or ask for other alternatives. All possible synchronized states are described by specifying a set of conflicts, a partial order on the conflicts describing the order in which they need to be resolved, as well as the effect of each decision on subsequent conflicts. Using this classification, the outcomes of different conflict resolution policies can be investigated easily.
      Citation: Future Internet
      PubDate: 2022-11-21
      DOI: 10.3390/fi14110344
      Issue No: Vol. 14, No. 11 (2022)
       
  • Future Internet, Vol. 14, Pages 345: Assessing Latency of Packet Delivery
           in the 5G 3GPP Integrated Access and Backhaul Architecture with
           Half-Duplex Constraints

    • Authors: Nikita Polyakov, Anna Platonova
      First page: 345
      Abstract: Integrated Access and Backhaul (IAB) is an enabling technology for efficient 5G millimeter wave (mmWave) New Radio (NR) deployment. The key feature of IAB is multi-hop wireless backhauling, allowing utilizing relaying IAB-nodes to provide cost-efficient access network densification and alleviate the problem of blockages. One of the critical performance measures in such systems is the latency of packet delivery over the multi-hop paths. The paper aims at assessing the impact of multi-hop transmission on the end-to-end delay in an IAB radio access network, taking into account the half-duplex constraint. We build a detailed queuing theory model for latency assessment in time-division-multiplexing (TDM)-based IAB deployments and evaluate the delay due to queuing in the network nodes for several cell topologies and under different time allocation strategies between access and backhaul. The paper considers a practical Manhattan-style urban deployment, which is characteristically impaired by the blockage of buildings. The numerical results show that balancing the access and backhaul micro phases is crucial for reducing the end-to-end packet delay, at least in the uplink, while increasing the number of network hops yields a linear increase in the total packet delay for both the uplink and downlink. The numerical results were obtained via simulation using the open-source software OMNeT++.
      Citation: Future Internet
      PubDate: 2022-11-21
      DOI: 10.3390/fi14110345
      Issue No: Vol. 14, No. 11 (2022)
       
 
JournalTOCs
School of Mathematical and Computer Sciences
Heriot-Watt University
Edinburgh, EH14 4AS, UK
Email: journaltocs@hw.ac.uk
Tel: +00 44 (0)131 4513762
 


Your IP address: 35.173.35.14
 
Home (Search)
API
About JournalTOCs
News (blog, publications)
JournalTOCs on Twitter   JournalTOCs on Facebook

JournalTOCs © 2009-