JournalTOCs

Discover Internet of Things
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This is an Open Access Journal

Open Access journal
ISSN (Online) 2730-7239
Published by Springer-Verlag

[2469 journals]

Evaluating student levelling based on machine learning model’s
performance
- Abstract: Abstract In this paper, a novel application of machine learning algorithms is presented for student levelling. In multicultural countries such as UAE, there are various education curriculums where the sector of private schools and quality assurance is supervising various private schools for many nationalities. As there are various education curriculums in United Arab Emirates, specifically Abu Dhabi, to meet expats’ needs, there are different requirements for registration and success. In addition, there are different age groups for starting education in each curriculum. Every curriculum follows different education methods such as assessment techniques, reassessment rules, and exam boards. Currently, students who transfer to other curriculums are not correctly placed to their appropriate year group as a result of the start and end dates of each academic year as well as due to their date of birth, in which students who are either younger or older for that year group can create gaps in their learning and performance. In addition, pupils’ academic journeys are not stored which create a gap for the schools to track their learning process. In this paper, we propose a computational framework applicable in multicultural countries such as United Arab Emirates in which multi-education systems are implemented. Machine Learning are used to provide the appropriate student’ level aiding schools to provide a smooth transition when assigning students to their year groups and provide levelling and differentiation information of pupils for a smooth transition between one education curriculums to another, in which retrieval of their progress is possible. For classification and discriminant analysis of pupils levelling, three machine learning classifiers are utilised including random forest classifier, Artificial Neural Network, and combined classifiers. The simulation results indicated that the proposed machine learning classifiers generated effective performance in terms of accuracy.
  PubDate: 2022-05-30
Computational intelligence based sustainable computing with classification
model for big data visualization on map reduce environment
- Abstract: Abstract In recent years, the researchers have perceived the modifications or transformations motivated by the presence of big data on the definition, complexity, and future direction of the real world optimization problems. Big Data visualization is mainly based on the efficient computer system for ingesting actual data and producing graphical representation for understanding large quantity of data in a fraction of seconds. At the same time, clustering is an effective data mining tool used to analyze big data and computational intelligence (CI) techniques can be employed to solve big data classification process. In this aspect, this study develops a novel Computational Intelligence based Clustering with Classification Model for Big Data Visualization on Map Reduce Environment, named CICC-BDVMR technique. The proposed CICC-BDVMR technique intends to perform effective BDV using the clustering and data classification processes on the Map Reduce environment. For clustering process, a grasshopper optimization algorithm (GOA) with kernelized fuzzy c-means (KFCM) technique is used to cluster the big data and the GOA is mainly utilized to determine the initial cluster centers of the KFCM technique. GOA is a recently proposed metaheuristic algorithm inspired by the swarming behaviour of grasshoppers. This algorithm has been shown to be efficient in tackling global unconstrained and constrained optimization problems. Based on the modified GOA, an effective kernel extreme learning machine model for financial stress prediction was created. Besides, big data classification process takes place using the Ridge Regression (RR) and the parameter optimization of the RR model is carried out via the Red Colobuses Monkey (RCM) algorithm. The design of GOA and RCM algorithms for parameter optimization processes for big data classification shows the novelty of the study. A wide ranging simulation analysis is carried out using benchmark big datasets and the comparative results reported the enhanced outcomes of the CICC-BDVMR technique over the recent state of art approaches. The broad comparison research illustrates the CICC-BDVMR approach’s promising performance against contemporary state-of-the-art techniques. As a result, the CICC-BDVMR technique has been demonstrated to be an effective technique for visualising and classifying large amounts of data.
  PubDate: 2022-05-09
Real-time instruction-level verification of remote IoT/CPS devices via
side channels
- Abstract: Abstract In recent years, with the rise of IoT technology, wireless Cyber-Physical Systems (CPS) have become widely deployed in critical infrastructure, including power generation, military systems, and autonomous and unmanned vehicles. The introduction of network connectivity for data transfer, cloud support, etc., into CPS, can lead to malware injection. Meanwhile, outsourcing of advanced technology node fabrication overseas makes it difficult to protect these devices from malicious modification and hardware Trojans. For solving these issues, traditional anomaly detection methods insert monitoring circuits or software into the target device but come with high overhead and power consumption. Alternative anomaly detection methods occur offline and use large equipment like oscilloscopes and PCs to collect and process side-channel traces. While they can achieve high accuracy in detecting various anomalies, they are difficult to use in practice due to their large, expensive setups. In this paper, we introduce a new instruction-level verification methodology that uses a low-cost, external add-on to monitor the power traces of a target device. This methodology possesses fine-grained granularity and could protect the target device from any malware or hardware Trojans that alter even a single instruction inside the target device. The hardware used is a tiny (20 \(\times \) 20 mm), custom-designed PCB called RASC that collects power traces, performs real-time malware detection, and transmits outcomes to security administrators via Bluetooth. The proposed methodology is demonstrated on 6 benchmarks with two types of malware on an Atmel AVR device, and the accuracy between offline and real-time malware detection is compared.
  PubDate: 2022-03-17
  DOI: 10.1007/s43926-022-00021-2
Exploring the benefits and challenges of Internet of Things (IoT) during
Covid-19: a case study of Bangladesh
- Abstract: Abstract The Internet of Things (IoT) is expected to have a huge impact, especially during the pandemic period. The study reveals that people are using the IoT mostly for education purposes (as students and educators), office work, banks and medical purposes during the pandemic. The topmost benefit of using IoT services experienced by people during pandemic situations is that it helps to strictly maintain physical distance. However, the greatest challenge faced by people is that the use of the IoT increases social distancing and reduces personal communication. Data were collected through a questionnaire distributed online and using a convenient random sampling method. A total of 260 participants’ properly completed responses were analyzed after conducting Three-fold validation. Research method was quantitative and empirical. Although, some studies have been found about IoT prospects in Bangladesh, no study has specifically explored the benefits and challenges of IoT services in diverse fields of Bangladesh during this new normal COVID-19 situation. The results can be beneficial to academic scholars, business professionals and organizations in different sectors and any other parties interested in determining the impact of IoT services on pandemic.
  PubDate: 2021-11-18
  DOI: 10.1007/s43926-021-00020-9
A novel IoT sensor authentication using HaLo extraction method and memory
chip variability
- Abstract: Abstract Since the inception of encrypted messages thousands of years ago, mathematicians and scientists have continued to improve encryption algorithms in order to create more secure means of communication. These improvements came by means of more complex encryption algorithms that have stronger security features such as larger keys and trusted third parties. While many new processors can handle these more complex encryption algorithms, IoT devices on the edge often struggle to keep up with resource intensive encryption standards. In order to meet this demand for lightweight, secure encryption on the edge, this paper proposes a novel solution, called the High and Low (HaLo) method, that generates Physical Unclonable Function (PUF) signatures based on process variations within flash memory. These PUF signatures can be used to uniquely identify and authenticate remote sensors, and help ensure that messages being sent from remote sensors are encrypted adequately without requiring computationally expensive methods. The HaLo method consumes 20x less power than conventional authentication schemes commonly used with IoT devices, it has an average latency of only 39ms for 512 bit signature generation, and the average error rate is below 0.06%. Due to its low latency, low error rate, and high power efficiency, the HaLo method can progress the field of IoT encryption standards by accurately and efficiently authenticating remote sensors without sacrificing encryption integrity.
  PubDate: 2021-10-20
  DOI: 10.1007/s43926-021-00019-2
Social behavior prediction with graph U-Net+
- Abstract: Abstract We focus on the problem of predicting social media user’s future behavior and consider it as a graph node binary classification task. Existing works use graph representation learning methods to give each node an embedding vector, then update the node representations by designing different information passing and aggregation mechanisms, like GCN or GAT methods. In this paper, we follow the fact that social media users have influence on their neighbor area, and extract subgraph structures from real-world social networks. We propose an encoder–decoder architecture based on graph U-Net, known as the graph U-Net+. In order to improve the feature extraction capability in convolutional process and eliminate the effect of over-smoothing problem, we introduce the bilinear information aggregator and NodeNorm normalization approaches into both encoding and decoding blocks. We reuse four datasets from DeepInf and extensive experimental results demonstrate that our methods achieve better performance than previous models.
  PubDate: 2021-09-06
  DOI: 10.1007/s43926-021-00018-3
Hybrid high-order semantic graph representation learning for
recommendations
- Abstract: Abstract The amount of Internet data is increasing day by day with the rapid development of information technology. To process massive amounts of data and solve information overload, researchers proposed recommender systems. Traditional recommendation methods are mainly based on collaborative filtering algorithms, which have data sparsity problems. At present, most model-based collaborative filtering recommendation algorithms can only capture first-order semantic information and cannot process high-order semantic information. To solve the above issues, in this paper, we propose a hybrid graph neural network model based on heterogeneous graphs with high-order semantic information extraction, which models users and items respectively by learning low-dimensional representations for them. We introduced an attention mechanism to allow the model to understand the corresponding edge weights adaptively. Simultaneously, the model also integrates social information in the data to learn more abundant information. We performed some experiments on related datasets. Our method achieved better results than some current advanced models, which verified the proposed model’s effectiveness.
  PubDate: 2021-08-19
  DOI: 10.1007/s43926-021-00017-4
Social media analytics of the Internet of Things
- Abstract: Abstract The Internet of Things technology offers convenience and innovation in areas such as smart homes and smart cities. Internet of Things solutions require careful management of devices and the risk mitigation of potential vulnerabilities within cyber-physical systems. The Internet of Things concept, its implementations, and applications are frequently discussed on social media platforms. This research illuminates the public view of the Internet of Things through a content-based and network analysis of contemporary conversations occurring on the Twitter platform. Tweets can be analyzed with machine learning methods to converge the volume and variety of conversations into predictive and descriptive models. We have reviewed 684,503 tweets collected in a 2-week period. Using supervised and unsupervised machine learning methods, we have identified trends within the realm of IoT and their interconnecting relationships between the most mentioned industries. We have identified characteristics of language sentiment which can help to predict the popularity of IoT conversation topics. We found the healthcare industry as the leading use case industry for IoT implementations. This is not surprising as the current COVID-19 pandemic is driving significant social media discussions. There was an alarming dearth of conversations towards cybersecurity. Recent breaches and ransomware events denote that organizations should spend more time communicating about risks and mitigations. Only 12% of the tweets relating to the Internet of Things contained any mention of topics such as encryption, vulnerabilities, or risk, among other cybersecurity-related terms. We propose an IoT Cybersecurity Communication Scorecard to help organizations benchmark the density and sentiment of their corporate communications regarding security against their specific industry.
  PubDate: 2021-07-19
  DOI: 10.1007/s43926-021-00016-5
Research on college gymnastics teaching model based on multimedia image
and image texture feature analysis
- Abstract: Abstract With the rapid development of gymnastics technology, novel movements are also emerging. Due to the emergence of various complicated new movements, higher requirements are put forward for college gymnastics teaching. Therefore, it is necessary to combine the multimedia simulation technology to construct the human body rigid model and combine the image texture features to display the simulation image in texture form. In the study, GeBOD morphological database modeling was used to provide the data needed for the modeling of the whole-body human body of the joint and used for dynamics simulation. Simultaneously, in order to analyze and summarize the technical essentials of the innovative action, this experiment compared and analyzed the hem stage of the cross-headstand movement of the subject and the hem stage of the 180° movement. Research shows that the method proposed in this paper has certain practical effects.
  PubDate: 2021-07-01
  DOI: 10.1007/s43926-021-00015-6
Correction to: Bayesian Topology Learning and noise removal from network
data
- Abstract: A correction to this paper has been published: https://doi.org/10.1007/s43926-021-00013-8
  PubDate: 2021-05-10
  DOI: 10.1007/s43926-021-00013-8
Drones as internet of video things front-end sensors: challenges and
opportunities
- Abstract: Abstract Internet of Video Things (IoVT) has become an emerging class of IoT systems that are equipped with visual sensors at the front end. Most of such visual sensors are fixed one whereas the drones are considered flying IoT nodes capable of capturing visual data continuously while flying over the targets of interest. With such a dynamic operational mode, we can imagine significant technical challenges in sensor data acquisition, information transmission, and knowledge extraction. This paper will begin with an analysis on some unique characteristics of IoVT systems with drones as its front end sensors. We shall then discuss several inherent technical challenges for designing drone-based IoVT systems. Furthermore, we will present major opportunities to adopt drone-based IoVT in several contemporary applications. Finally, we conclude this paper with a summary and an outlook for future research directions.
  PubDate: 2021-05-10
  DOI: 10.1007/s43926-021-00014-7
Particle swarm optimization in image processing of power flow learning
distribution
- Abstract: Abstract In order to realize the coordination and integration optimization of the power system itself, this paper constructed the mathematical model of the hybrid power system and solved the multi-objective optimization problem of the heating system through the optimized particle swarm optimization algorithm. Based on the back-to-back VSC-HVDC grid-connected composite system, this paper studied the integrated control strategy of the device to achieve the simultaneous parallel and tie line currents. At the same time, this paper simplified and improved the proposed disassembly criteria for grid-connected composite devices and integrated them into the grid-connected composite device. In addition, on this basis, the integrated control of the three functions of de-listing, juxtaposition and tie line power adjustment of the same device was further studied. Simulation studies show that the proposed algorithm has certain effects and can provide theoretical reference for subsequent related research.
  PubDate: 2021-04-19
  DOI: 10.1007/s43926-021-00012-9
Bayesian Topology Learning and noise removal from network data
- Abstract: Abstract Learning the topology of a graph from available data is of great interest in many emerging applications. Some examples are social networks, internet of things networks (intelligent IoT and industrial IoT), biological connection networks, sensor networks and traffic network patterns. In this paper, a graph topology inference approach is proposed to learn the underlying graph structure from a given set of noisy multi-variate observations, which are modeled as graph signals generated from a Gaussian Markov Random Field (GMRF) process. A factor analysis model is applied to represent the graph signals in a latent space where the basis is related to the underlying graph structure. An optimal graph filter is also developed to recover the graph signals from noisy observations. In the final step, an optimization problem is proposed to learn the underlying graph topology from the recovered signals. Moreover, a fast algorithm employing the proximal point method has been proposed to solve the problem efficiently. Experimental results employing both synthetic and real data show the effectiveness of the proposed method in recovering the signals and inferring the underlying graph.
  PubDate: 2021-03-19
  DOI: 10.1007/s43926-021-00011-w
Study and design of a retrofitted smart water meter solution with energy
harvesting integration
- Abstract: Abstract The reduction of water resources due to climate change and the increasing demand associated with population growth is a renewed concern. Water distribution monitoring and smart metering are essential tools to improve distribution efficiency. This paper reports on the study, design, and implementation of a smart water meter (SWM) prototype, designed for mechanical water meters that need to undergo a retrofitting process to enable automatic metering readings. Metering data is transmitted through innovative narrowband internet of things (NB-IoT) technology with low power, long-range, and effective penetration. A flexible power management design allows the introduction of an energy harvester that recovers energy from the surrounding environment and charges the internal battery. The energy harvesting feasibility was demonstrated with two proof-of-concept configurations, light and water-turbine based. The details on the performance of the proposed solution are presented, including the output voltages and harvested power. Although the energy harvesting technologies have not been integrated yet in commercial SWM applications, the results show that the integration is feasible and, once employed in a controlled environment, it can create business advantages by reducing the size and capacity of the internal batteries, enabling one to reduce the operation cost and mitigate long-term ecological problems associated with the use and disposal of batteries.
  PubDate: 2021-03-04
  DOI: 10.1007/s43926-021-00010-x
Discover Internet of Things editorial, inaugural issue
- PubDate: 2021-02-24
  DOI: 10.1007/s43926-021-00007-6
QoE in IoT: a vision, survey and future directions
- Abstract: Abstract The rapid evolution of the Internet of Things (IoT) is making way for the development of several IoT applications that require minimal or no human involvement in the data collection, transformation, knowledge extraction, and decision-making (actuation) process. To ensure that such IoT applications (we term them autonomic) function as expected, it is necessary to measure and evaluate their quality, which is challenging in the absence of any human involvement or feedback. Existing Quality of Experience (QoE) literature and most QoE definitions focuses on evaluating application quality from the lens of human receiving application services. However, in autonomic IoT applications, poor quality of decisions and resulting actions can degrade the application quality leading to economic and social losses. In this paper, we present a vision, survey and future directions for QoE research in IoT. We review existing QoE definitions followed by a survey of techniques and approaches in the literature used to evaluate QoE in IoT. We identify and review the role of data from the perspective of IoT architectures, which is a critical factor when evaluating the QoE of IoT applications. We conclude the paper by identifying and presenting our vision for future research in evaluating the QoE of autonomic IoT applications.
  PubDate: 2021-02-24
  DOI: 10.1007/s43926-021-00006-7
Intelligent IoT systems for civil infrastructure health monitoring: a
research roadmap
- Abstract: Abstract This paper addresses the problem of efficient and effective data collection and analytics for applications such as civil infrastructure monitoring and emergency management. Such problem requires the development of techniques by which data acquisition devices, such as IoT devices, can: (a) perform local analysis of collected data; and (b) based on the results of such analysis, autonomously decide further data acquisition. The ability to perform local analysis is critical in order to reduce the transmission costs and latency as the results of an analysis are usually smaller in size than the original data. As an example, in case of strict real-time requirements, the analysis results can be transmitted in real-time, whereas the actual collected data can be uploaded later on. The ability to autonomously decide about further data acquisition enhances scalability and reduces the need of real-time human involvement in data acquisition processes, especially in contexts with critical real-time requirements. The paper focuses on deep neural networks and discusses techniques for supporting transfer learning and pruning, so to reduce the times for training the networks and the size of the networks for deployment at IoT devices. We also discuss approaches based on machine learning reinforcement techniques enhancing the autonomy of IoT devices.
  PubDate: 2021-02-24
  DOI: 10.1007/s43926-021-00009-4
Role of Artificial Intelligence in the Internet of Things (IoT)
cybersecurity
- Abstract: Abstract In recent years, the use of the Internet of Things (IoT) has increased exponentially, and cybersecurity concerns have increased along with it. On the cutting edge of cybersecurity is Artificial Intelligence (AI), which is used for the development of complex algorithms to protect networks and systems, including IoT systems. However, cyber-attackers have figured out how to exploit AI and have even begun to use adversarial AI in order to carry out cybersecurity attacks. This review paper compiles information from several other surveys and research papers regarding IoT, AI, and attacks with and against AI and explores the relationship between these three topics with the purpose of comprehensively presenting and summarizing relevant literature in these fields.
  PubDate: 2021-02-24
  DOI: 10.1007/s43926-020-00001-4
“Chatty Devices” and edge-based activity classification
- Abstract: Abstract With increasing automation of manufacturing processes (focusing on technologies such as robotics and human-robot interaction), there is a realisation that the manufacturing process and the artefacts/products it produces can be better connected post-production. Built on this requirement, a “chatty" factory involves creating products which are able to send data back to the manufacturing/production environment as they are used, whilst still ensuring user privacy. The intended use of a product during design phase may different significantly from actual usage. Understanding how this data can be used to support continuous product refinement, and how the manufacturing process can be dynamically adapted based on the availability of this data provides a number of opportunities. We describe how data collected on product use can be used to: (i) classify product use; (ii) associate a label with product use using unsupervised learning—making use of edge-based analytics; (iii) transmission of this data to a cloud environment where labels can be compared across different products of the same type. Federated learning strategies are used on edge devices to ensure that any data captured from a product can be analysed locally (ensuring data privacy).
  PubDate: 2021-02-24
  DOI: 10.1007/s43926-021-00004-9
Swarm-based counter UAV defense system
- Abstract: Abstract Unmanned Aerial Vehicles (UAVs) have quickly become one of the promising Internet-of-Things (IoT) devices for smart cities. Thanks to their mobility, agility, and onboard sensors’ customizability, UAVs have already demonstrated immense potential for numerous commercial applications. The UAVs expansion will come at the price of a dense, high-speed and dynamic traffic prone to UAVs going rogue or deployed with malicious intent. Counter UAV systems (C-UAS) are thus required to ensure their operations are safe. Existing C-UAS, which for the majority come from the military domain, lack scalability or induce collateral damages. This paper proposes a C-UAS able to intercept and escort intruders. It relies on an autonomous defense UAV swarm, capable of self-organizing their defense formation and to intercept the malicious UAV. This fully localized and GPS-free approach follows a modular design regarding the defense phases and it uses a newly developed balanced clustering to realize the intercept- and capture-formation. The resulting networked defense UAV swarm is resilient to communication losses. Finally, a prototype UAV simulator has been implemented. Through extensive simulations, we demonstrate the feasibility and performance of our approach.
  PubDate: 2021-02-24
  DOI: 10.1007/s43926-021-00002-x