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Wireless Communications & Mobile Computing
Journal Prestige (SJR): 0.203  Citation Impact (citeScore): 1 Number of Followers: 10  Hybrid journal (It can contain Open Access articles)ISSN (Print) 1530-8669 - ISSN (Online) 1530-8677 Published by Hindawi  [339 journals]
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- Authorization Recycling in Attribute-Based Access Control
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Abstract: In most access control scenarios, the communication between the PDP (policy decision point) and the PEP (policy enforcement point) can cause high authorization overhead. Authorization recycling enables PEP to use the previous access control decisions fetched from the PDP to handle some upcoming access control requests, reduce authorization costs, and increase the efficiency of access control decision-making. Inspired by the RBAC (role-based access control) authorization recycling mechanism, this article first presents an ABAC (attribute-based access control) model based on Boolean expressions of subject and object attributes. It then proposes an authorization recycling approach for this model. In this approach, we provide construction and update methods for authorization data caches and access control decision-making rules for SDP (secondary decision point) by using the caches. The proposed approach can deduce precise and approximate access control decisions from the cache of authorization data, reducing communication between the PEP and the PDP. Finally, the feasibility of the proposed method is verified by conducting a small-scale test. ABAC, SDP, authorization recycling, and authorization caching.
PubDate: Wed, 31 May 2023 07:35:00 +000
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- Dynamic Task Assignment Framework for Mobile Crowdsensing with Deep
Reinforcement Learning-
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Abstract: Task assignment is a key issue in mobile crowdsensing (MCS). Previous task assignment methods were mainly static offline assignment. However, the MCS platform needs to process dynamically changing workers and tasks online in the actual assignment process. Hence, a reliable dynamic assignment strategy is crucial to improving the platform’s efficiency. This paper proposes an MCS dynamic task assignment framework to solve the task maximization assignment problem with spatiotemporal properties. First, a single worker is modeled for the Markov decision process, and a deep reinforcement learning algorithm (DDQN) is used to perform offline learning on historical task data. Then, in the dynamic assignment process, we consider the impact of current decisions on future decisions. Use the maximum flow model to maximize the number of tasks completed in each period while maximizing the expected value of all workers to achieve the optimal global assignment. Experiments show that the strategy proposed in this paper has good performance compared with the baseline strategy under different conditions.
PubDate: Mon, 29 May 2023 10:50:00 +000
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- Lightweight APIT with Bat Optimization with Simulated Annealing
Localization for Resource-Constrained Sensor Networks-
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Abstract: In a wireless sensor network, information processing, and information acquisition, localization technology is the key to making it practically possible application. Approximate Point-in-Triangulation (APIT) is the most widely used localization estimation which has high accuracy in localizing the nodes and ease of deployment of nodes in the real-time environment. Though it has numerous key advantages, some of the drawbacks which make it a little setback in preference are the unevenness in the distribution of nodes. Tracking is more appropriate for mobile sensor nodes than tracking is for static sensor nodes. The two main types of localization algorithms are range-based and range-free techniques. In an indoor setting, the projected range (distance) between an anchor and an unknown node is very inaccurate. By utilizing a large number of already existing access points (APs) in the range-free localization approach, this issue can be overcome to a great extent. The utilization of multisensor data, such as magnetic, inertial, compass, gyroscope, ultrasonic, infrared, visual, and/or odometer, is stressed in recent research to further increase localization accuracy. The tracking system also makes location predictions for the future based on historical location data. To overcome this issue, the proposed localization algorithm of APIT with Bat-SA proves its efficiency. Due to its low localization error, the traditional Bat method is more accurate than APIT. The proposed Bat using the SA algorithm is found to perform better than the traditional APIT algorithm in terms of convergence of computing rate and success rate. In order to mimic the suggested APIT method, it is paired with the Bat-SA localization technique. Simulation evaluation proves the performance efficiency of the proposed algorithm. The performance metrics parameters are latency, node distribution map, positioning error map, and neighbor relationship diagram which are used to evaluate the proposed method.
PubDate: Sat, 27 May 2023 05:35:00 +000
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- An Approach for Three-Dimensional Sectorization in the Terminal Area Based
on Airspace Function-
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Abstract: As explainable artificial intelligence (XAI) has grown in popularity, it has become possible to more clearly explain the functional correspondence between airspace and traffic flow, which can alleviate the contradiction between the continuously increasing traffic demand in the terminal area and the limited airspace capacity; this paper studies the three-dimensional sectorization in the terminal area based on the airspace function. First, trajectory clustering is adopted to classify the functions of air traffic flows. Then, a functional sectorization framework is proposed to enable different airspace sectors with varied functions, where specifically the functional consistency objective between the airspace sector and the traversed air traffic flows is proposed. Third, a corresponding efficient algorithm is designed to generate functional sectors with an accurate scope that can well separate different traffic flows. Finally, the proposed method is evaluated by the actual operation datasets in the Shanghai terminal area. The results show that the method proposed in this paper can not only generate three-dimensional sectors with specific functions according to the prevailing traffic flow in the complex terminal area, which is conducive to the construction of controllers’ situational awareness, but also can reduce potential conflicts and traffic density variance, increasing average sector flight time a lot.
PubDate: Wed, 24 May 2023 03:05:00 +000
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- A Review of Intelligent Vision Enhancement Technology for Battlefield
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Abstract: This paper reviews the application of intelligent vision enhancement technology in the battlefield environment and explores new research directions. This paper mainly introduces three parts. First, we introduce a solution for enhancing the battlefield situation and using head-mounted displays for soldier combat. Second, we summarize three core technologies supporting intelligent vision enhancement technology: 3D environment reconstruction, tracking and registration, and situational awareness. Third, we summarize three application directions of intelligent vision enhancement technology on the battlefield. Finally, the problems and challenges in the research work are proposed, including current issues such as accurate battlefield situational awareness by augmented reality technology, multiperson collaborative management and data flow, as well as challenges in future battlefield situation enhancement and perception. The future development trend of intelligent vision enhancement technology on the battlefield has been prospected. Two meanings of this paper are as follows: the first is to review the research status of intelligent vision enhancement technology from the technical level and identify the key technical points that may restrict development in the future; the second is to analyze the advantages and disadvantages of intelligent vision enhancement technology from the level of battlefield application and the roles of users. In addition, this paper proposes how to take the lead and take initiative in future wars.
PubDate: Fri, 19 May 2023 10:35:00 +000
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- Efficient Secure Computation from SM Series Cryptography
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Abstract: The wireless network suffers from many security problems, and computation in a wireless network environment may fail to preserve privacy as well as correctness when the adversaries conduct attacks through backdoors, steganography, kleptography, etc. Secure computation ensures the execution security in such an environment, and compared with computation on the plaintext, the performance of secure computation is bounded by the underlying cryptographic algorithms and the network environment between the involved parties. Besides, the Chinese cryptography laws require the cryptographic algorithms that appeared in the commercial market to be authorized. In this work, we show how to implement oblivious transfer (OT), an important primitive in secure multiparty computation (MPC), using the Chinese government-approved SM2 and SM3 algorithms. The SM2 algorithm is based on the elliptic curve cryptography and is much faster than the discrete logarithm-based solutions. Moreover, by adopting the standard OT extension technique, we can extend the number of OTs efficiently with one more round of communication and invocations to the SM3 and SM4 algorithms. The OT primitive can be used in the Beaver multiplication triple generation and other MPC protocols, e.g., private set intersection. Therefore, we can utilize the SM series cryptography, specifically, the SM2, SM3, and SM4 algorithms, to build highly efficient secure computation frameworks which are suitable for the wireless network environment and for commercial applications in China. The experimental evaluation results show that our protocols have comparable performance to existing protocols; specifically, our protocols are quite suitable for bad network environments.
PubDate: Wed, 17 May 2023 12:20:00 +000
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- Hybrid Low-Rank Tensor CP and Tucker Decomposition with Total Variation
Regularization for HSI Noise Removal-
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Abstract: The acquired hyperspectral images (HSIs) are affected by a mixture of several types of noise, which often suffer from information missing. Corrupted HSIs limit the precision of the subsequent processing. In this paper, the weighted Total Variation-regularized Hybrid Model of CP and Tucker (WTV-HMCT) is proposed to accurately identify the intrinsic structures of the clean HSIs. By jointly minimizing CP rank and Tucker rank in the low-rank tensor approximation, WTV-HMCT fully exploits the high-dimensional structure correlations of HSI. To ensure the piecewise smoothness of the recovered image, the hybrid low-rank tensor decomposition approach integrates the weighted spatial spectral total variation regularization for the separation of the noise-free HSI and mixed noise. By the Alternating Direction Method of Multipliers (ADMM), the optimization model is transformed into two subproblems. Finally, an efficient proximal alternating minimization algorithm is developed to optimize the proposed hybrid low-rank tensor decomposition efficiently. The experimental results show that the proposed model effectively handles Gauss noise, striping noise, and mixed noise and that it outperforms the most advanced methods in terms of evaluation metrics and visual evaluation.
PubDate: Wed, 17 May 2023 04:05:01 +000
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- A Model Predicting CRM Resource Effect on Business Performance through CRM
Capabilities-
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Abstract: This study employs the CRM measurement model to the context of customer relationship management (CRM). It is aimed at indirectly examining the relationships between various resources of CRM and business performance. Additionally, this study is aimed at contributing to marketing research by placing an emphasis on CRM technology and their impact on performance. Through collecting secondary data, the direct and indirect effects of CRM resources and capabilities on business performance are examined within a sample of 6 case companies in the UK grocery market during 2015~2017. Additional measure of CRM capability is aggregated into the firm level to examine its relationship with their corporate performance. Furthermore, capability is categorized through defining the intention of initiating CRM programme. The results find a positive relationship between both CRM resources and their capabilities and performance. Besides, interactive capability is most essential for companies to enhance their CRM. Lastly, the interaction between technology and other resource is significantly associated with business performance. Managers may improve their CRM programs and eliminate side effects more effectively by concentrating on one type of resource to strengthen their most common CRM capability. This paper bridges significant gaps in the current literature through combining RBV and DC perspective, meanwhile, taking a capability view of CRM. Under a contemporary CRM measurement model, it examines how the possession of important CRM resource influences business performance in UK supermarket.
PubDate: Sat, 13 May 2023 04:35:00 +000
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- Immersive Multimedia Art Design Based on Deep Learning Intelligent VR
Technology-
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Abstract: The rapid development of computer science has led to the rapid development of new media technology. Among them, VR technology is sought after by the vast market for its multidimensional experience and immersive model. VR technology plays an important role in today’s art exhibition and presentation. However, in the process of transforming 2D images into 3D models, traditional VR technology has some problems, such as long loading time, low model decision-making efficiency, and chaotic operation process, which often lead to visual interference when viewers watch art. Based on this background and the interactive page algorithm based on traditional VR technology, this paper studies and improves the visual interference in immersive multimedia art design by using task technology perception matching model (TTF). The results show that the improved task technology perception matching model reduces the visual and auditory interference by 10% in the process of virtual reality art display and viewing, and the model layout is more reasonable, and the utilization rate is higher.
PubDate: Fri, 12 May 2023 05:20:00 +000
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- Human Motion Recognition in Dance Video Images Based on Attitude
Estimation-
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Abstract: With the deep integration of science and technology and culture, the estimation of human movements in dance video images will become an important application field of computer vision technology, which can be used not only for professional dancers’ movement correction, dance self-help teaching, and other application scenarios but also for athletes’ movement analysis. Therefore, it will greatly promote the implementation of teaching students in accordance with their aptitude by applying information technology to estimate dancers’ movements and postures in real time and obtaining information of classroom dance teaching status in time. In this paper, human motion recognition in dance video images is studied based on an attitude estimation algorithm. When the number of experiments reaches 20, the average value of deep learning algorithm and particle swarm optimization algorithm is 76.23 and 75.23, respectively, while the average value of attitude estimation algorithm in this paper is 77.95. Therefore, the average results of attitude estimation algorithm in this paper are slightly higher than those of other algorithms.
PubDate: Thu, 11 May 2023 10:05:00 +000
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- A Novel and Efficient Authentication Scheme Based on UAV-UAV Environment
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Abstract: Nowadays, unmanned aerial vehicles (UAVs) are used in various fields due to their high maneuverability and low cost of construction and use. With the development of UAV technology, it has become a trend for UAVs to cooperate with each other to complete assigned tasks. Multiple UAVs are combined according to a certain structure, and through the information sharing between them, a cooperative effect is generated to achieve intelligent collaborative task execution. However, information sharing is carried out on a public channel, so ensuring secure communication between UAVs is crucial. Moreover, UAVs are easily captured by an adversary, who can impersonate legitimate UAVs to disrupt communications if UAVs’ internal secrets that are stolen. Therefore, we propose a lightweight authentication scheme based on physical unclonable function (PUF), to provide mutual authentication between UAVs. PUF is embedded in the unmanned aerial vehicle (UAV) to defend against physical capture attack. Furthermore, to evaluate the security and performance of our scheme, formal and informal security analyses and formal security verification of the scheme are performed, and the performance of the scheme is compared with existing UAV schemes. The above analyses show that our scheme has great advantages in terms of security and overheads.
PubDate: Thu, 11 May 2023 05:50:00 +000
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- Big Data Analytics, Processing Models, Taxonomy of Tools, V’s, and
Challenges: State-of-Art Review and Future Implications-
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Abstract: In the current digital era, data is budding tremendously from various sources like banks, businesses, education, entertainment, etc. Due to its significant consequence, it became a prominent proceeding for numerous research areas like the semantic web, machine learning, computational intelligence, and data mining. For knowledge extraction, several corporate sectors depend on tweets, blogs, and social data to get adequate analysis. It helps them predict the customer’s tastes and preferences, optimize the usage of resources. In some cases, the same data creates complications that lead to a problem named as big data. To solve this, so many researchers have given various solutions. Based on literature analysis formulated 6-s simulation towards big data, detailed information about characteristics, a taxonomy of tools, and discussed various processing paradigms. No one tool can truly fit for all solutions, so this paper helps to make decisions smoothly by providing enough information and discussing major privacy issues and future directions.
PubDate: Wed, 10 May 2023 07:50:01 +000
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- An Efficient Pairing-Free Certificateless Signature Scheme with KGC Trust
Level 3 for Wireless Sensor Network-
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Abstract: With the widespread adoption of wireless sensor networks (WSN), the security of the WSN has been a wide concern. Certificateless signature eliminates the certificate management problem and key escrow problem and is considered a feasible solution to solve the data integrity and authentication of WSN. Recently, Thumbur et al. proposed an efficient pairing-free certificateless signature scheme, and Xu et al. pointed out that their scheme is not resistant to signature forgery attacks and proposed an improved scheme. Based on the trust hierarchy defined by Girault, we find that Xu et al.’s scheme is still only able to achieve security under KGC trust level 2. Moreover, Thumbur et al.’s scheme uses the Schnorr signature algorithm form, which makes it favorable for scaling, while Xu et al.’s scheme breaks this advantage. Therefore, we propose a pairing-free certificateless scheme capable of reaching KGC trust level 3, still using the Schnorr signature algorithm form, and prove the security of the new scheme under the random oracle model. The final efficiency analysis shows that the new scheme has shorter public key length and higher computational efficiency.
PubDate: Tue, 09 May 2023 09:20:01 +000
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- Forest Health Consumption Satisfaction Evaluation with IPA and Factor
Analysis-
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Abstract: To evaluate the current status of forest health and consumption of the elderly, this work used the questionnaire survey method to collect 587 valid questionnaires about the status of forest health and consumption of the elderly in the capital cities of the three northeastern provinces. This work constructs an importance performance analysis (IPA) quartile and factor analysis satisfaction evaluation system by processing the satisfaction and importance distribution data of the three dimensions of infrastructure, leisure and entertainment, and medical rehabilitation. First, this work extracts four common satisfaction factors based on factor analysis. They are satisfaction of forest characteristic physiotherapy entertainment project, satisfaction of forest rehabilitation basic service project, satisfaction of forest rehabilitation infrastructure construction project, and satisfaction of forest health culture consumption project. Secondly, by calculating the factor scores, it can be found that the forest health consumption satisfaction of the elderly population in the three northeastern provinces is the highest in Shenyang, followed by the Northeast, Harbin is slightly lower than the Northeast, and Changchun is the lowest. At present, the elderly population in the capital cities of the three northeastern provinces is relatively satisfied with the supply of forest sanitation services, and only Shenyang has a driving effect on the comprehensive satisfaction of the capital cities in the three northeastern provinces. It was found that Shenyang’s satisfaction with forest characteristic physiotherapy and entertainment projects and forest health cultural consumption projects and Harbin’s satisfaction with forest reclamation basic service projects and forest reclamation infrastructure construction projects have positive effects on forest reclamation infrastructure construction projects of the three provincial capital cities in Northeast China. However, Changchun City showed opposite effects on the four satisfaction factors. Finally, based on the in-depth analysis of the conclusions, this work puts forward suggestions for the development of the forest health industry and its aging market in the three northeastern provinces.
PubDate: Mon, 08 May 2023 12:20:00 +000
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- Indoor and Outdoor Seamless Positioning Technology Based on Artificial
Intelligence and Intelligent Switching Algorithm-
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Abstract: Navigation and positioning is a new growth point of wireless services. And seamless positioning is the current development trend of navigation and positioning services. In view of the problems that dynamic handover cannot be performed during site movement, STA only selects the optimal AP according to the indication of received signal strength, and the handover delay is too long. This paper proposes a research on indoor and outdoor seamless positioning technology based on artificial intelligence and intelligent switching algorithm. The purpose is to study the influence of reducing the positioning data with too large error on the final positioning result. The method of this paper is to propose an intelligent switching algorithm and then discuss the research status and development direction of indoor positioning technology and outdoor positioning technology, respectively, and point out the switching methods between them. The role of these methods is to analyze the current situation of indoor and outdoor navigation technology, especially the development route of seamless positioning technology. It determines the strategy of integrating heterogeneous positioning systems to build a seamless positioning system to maximize the use of existing positioning system resources. This paper studies the design and implementation of the seamless positioning system through the simulation experiment of the intelligent switching algorithm and finally tests the system through the experimental vehicle. The online experiment results show that the system can achieve high positioning accuracy in indoor and outdoor environments, especially at its junction, and the positioning errors in all directions are within 0.2 m.
PubDate: Mon, 08 May 2023 10:35:00 +000
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- The HRM Model Based on Competency Model in the Context of New Age
Intelligence-
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Abstract: In the new era of rapid technological development, new technologies are emerging: mobile positioning technology, face recognition technology, two-dimensional code technology, artificial intelligence technology, etc. In the trend of the times, these emerging technologies continue to penetrate into people’s daily life and influence their social habits. At present, the shadow of emerging technologies such as artificial intelligence has already appeared in human resource management and organizational behavior, and the application of these emerging technologies will certainly promote the change of human resource management and organizational behavior and have a double impact on human resource management and organizational behavior. On the one hand, emerging technologies such as artificial intelligence bring new opportunities for the development of human resources, which can not only improve the efficiency of human resource management, optimize human resource organizational behavior, and accelerate the transformation of enterprise management mode. On the other hand, the application of these emerging technologies also brings new challenges for HR development and management: the enterprises’ demand for low-end positions decreases, increasing the employment pressure in the talent market; the enterprises’ development faces transformation and rectification, increasing the difficulty of survival of small enterprises; the employment difficulties and development prospects of HR departments are uncertain. In the context of the new era of intelligence, the impact of emerging technologies on human resource management and organizational behavior is bound to intensify, and enterprises should innovate ideas and integrate new ways and methods to upgrade the traditional human resource management model and seize the opportunity to promote human resource management changes, so that emerging technologies such as artificial intelligence can inject new vitality into modern human resource management. Based on this, this paper proposes an enterprise HR management strategy based on the competency model and combines it with a practical case study and finds that the strategy has been recognized by 91% of employees within the company and has effectively improved the efficiency of HR management.
PubDate: Mon, 08 May 2023 09:50:00 +000
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- Intelligent Construction of Hospital Management Organization Based on
Communication Technology and Information Fusion-
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Abstract: With the mature application of 5G communication and the development of artificial intelligence, the deep integration of modern hospital management and information technology has been fully realized. Therefore, this paper explores the organizational structure and system design of our institute and implements the construction and operation of the information system according to the concept of “management institutionalization, organization informatization, and form computerization.” The construction of information management departments was strengthened, the entire 69 information systems with systematic thinking were managed, the dynamic management mechanism of information system operation and maintenance was established, and the fine closed-loop management of hospital processes was realized. The results show that the information system based on institutional management will improve the management efficiency of the hospital and ensure the real-time, accuracy, and security of hospital data information.
PubDate: Fri, 05 May 2023 08:35:00 +000
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- An Enhanced Deep Reinforcement Learning-Based Global Router for VLSI
Design-
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Abstract: Global routing is a crucial step in the design of Very Large-Scale Integration (VLSI) circuits. However, most of the existing methods are heuristic algorithms, which cannot conjointly optimize the subproblems of global routing, resulting in congestion and overflow. In response to this challenge, an enhanced Deep Reinforcement Learning- (DRL-) based global router has been proposed, which comprises the following effective strategies. First, to avoid the overestimation problem generated by -learning, the proposed global router adopts the Double Deep -Network (DDQN) model. The DDQN-based global router has better performance in wire length optimization and convergence. Second, to avoid the agent from learning redundant information, an action elimination method is added to the action selection part, which significantly enhances the convergence performance of the training process. Third, to avoid the unfair allocation problem of routing resources in serial training, concurrent training is proposed to enhance the routability. Fourth, to reduce wire length and disperse routing resources, a new reward function is proposed to guide the agent to learn better routing solutions regarding wire length and congestion standard deviation. Experimental results demonstrate that the proposed algorithm outperforms others in several important performance metrics, including wire length, convergence performance, routability, and congestion standard deviation. In conclusion, the proposed enhanced DRL-based global router is a promising approach for solving the global routing problem in VLSI design, which can achieve superior performance compared to the heuristic method and DRL-based global router.
PubDate: Fri, 05 May 2023 06:20:00 +000
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- A Caching-Enabled Permissioned Blockchain Scheme for Industrial Internet
of Things Based on Deep Reinforcement Learning-
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Abstract: The integration of the industrial internet of things (IIoT) and blockchain has become a popular concept that provides IIoT with a trustworthy computing environment. Numerous IIoT nodes together form a decentralized network with rich location-aware computation resources, which can offer great data processing capabilities and low-latency services. However, we still face the challenges of how to efficiently process the massive IIoT data on resource-constrained IIoT nodes by blockchain smart contracts, as their storage capacity only allows them to store limited blockchain data. This work is aimed at improving the smart contract execution efficiency on these IIoT nodes by caching based on deep reinforcement learning. On the one hand, focusing on the characteristics of IIoT, the ledger structure, network architecture, and transaction flow are optimized. IIoT nodes are enabled to store and cache part of block data without affecting global data consistency. On the other hand, we formulated the blockchain caching problem as a Markov decision process and implemented a lightweight caching agent based on deep Q-learning. Proper features and a reward function are defined to minimize the execution delay of smart contracts. The extensive experimental results show that our proposed scheme can effectively reduce the data dissemination costs and smart contract execution delays of IIoT nodes that hold limited blockchain data.
PubDate: Thu, 04 May 2023 14:20:00 +000
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- A Deep Learning-Based Algorithm for Energy and Performance Optimization of
Computational Offloading in Mobile Edge Computing-
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Abstract: Mobile edge computing (MEC) has produced incredible outcomes in the context of computationally intensive mobile applications by offloading computation to a neighboring server to limit the energy usage of user equipment (UE). However, choosing a pool of application components to offload in addition to the volume of data transfer along with the latency in communication is an intricate issue. In this article, we introduce a novel energy-efficient offloading scheme based on deep neural networks. The proposed scheme trains an intelligent decision-making model that picks a robust pool of application components. The selection is based on factors such as the remaining UE battery power, network conditions, the volume of data transfer, required energy by the application components, postponements in communication, and computational load. We have designed the cost function taking all the mentioned factors, get the cost for all conceivable combinations of component offloading decisions, pick the robust decisions over an extensive dataset, and train a deep neural network as a substitute for the exhaustive computations associated. Model outcomes illustrate that our proposed scheme is proficient in the context of accuracy, root mean square error (RMSE), mean absolute error (MAE), and energy usage of UE.
PubDate: Thu, 04 May 2023 12:20:00 +000
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- Miniaturized Ultrawideband Microstrip Antenna for IoT-Based Wireless Body
Area Network Applications-
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Abstract: In this paper, we present an extremely compact ultrawideband (UWB) monopole microstrip patch antenna for a wireless body area network (WBAN). The proposed antenna is fabricated on a flexible Rogers RT-5880 dielectric substrate of thickness 0.5 mm and has an overall size of . The proposed antenna achieves a wideband characteristic with the help of a modified ground plane with a monopole pair. The monopole antenna is fed through a microstrip line and has a good impedance matching over a frequency band of 3.2 to 15 GHz (and beyond), with an axial ratio below 3 dB and a high efficiency of 77–95%. This antenna is designed to cover almost the complete UWB range; bandwidth for antenna is 11.52 GHz (3.48-15 GHz). The antenna has a realized gain of 2.3–7.2 dBi throughout the frequency band and has been tested for conformality. Measured results are found to be in good correlation with the simulated results. The antenna has also been tested for specific absorption rate (SAR) values within the simulation to compare with Federal Communications Commission (FCC) limits and verify their suitability for the Internet of Things- (IoT-) based wearable body area network.
PubDate: Thu, 04 May 2023 08:35:00 +000
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- Edge UAV Detection Based on Cyclic Spectral Feature: An Intelligent Scheme
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Abstract: With the commercialization of the fifth-generation mobile communication network (5G), the scale of the unmanned aerial vehicle (UAV) industry has continued to expand. However, the unregistered UAV has caused frequent harassment incidents at international airports, and the problem of UAV crimes is increasing. Radio technology supports long-distance detection of unregistered UAV and can be used as an efficient early warning method for unregistered UAV, which has attracted extensive attention from academia and industry. The classic UAV detection based on remote control signal method faces technical bottlenecks such as being easily affected by environmental noise, high complexity, and low detection accuracy. In the paper, an UAV remote control signal detection method is proposed based on cyclic spectrum features. More specifically, a dataset of UAV remote control signal UAV-CYCset is firstly constructed in the frequency domain. Based on UAV-CYCset dataset, a network architecture is proposed based on improved AlexNet, and the average detection accuracy of the improved model reaches 85% (from -10 dB to 10 dB) according to the simulation experiments.
PubDate: Thu, 04 May 2023 07:35:00 +000
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- 5G Channel Estimation Based on Whale Optimization Algorithm
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Abstract: This paper presents a novel approach, based on the whale optimization algorithm (WOA), for channel estimation in wireless communication systems. The proposed method provides a means to accurately estimate the wireless channel, while not requiring the statistical characteristics of the channel. This method uses the WOA to search for the best channel statistical characteristics toward the ultimate goal of having the smallest bit error rate (BER). The proposed approach is aimed at enhancing the efficiency of pilot-based OFDM systems under frequency-selective fading channels used in the performance testing of 5G New Radio gNodeB. In terms of BER and mean square error (MSE), the performance of the proposed WOA-based channel estimation algorithm is evaluated and compared with the conventional least square (LS) and minimum mean square error (MMSE) algorithms. The simulation results demonstrate that the proposed algorithm provides highly competitive performance over the MMSE algorithm and significantly outperforms the LS algorithm in a variety of system configurations. Since the requirement on prior channel statistics information makes the MMSE algorithm impractical or extremely complex, the proposed WOA-based channel estimation algorithm should be a suitable and promising candidate for dealing with channel estimation problems. The simulation framework is implemented in MATLAB and available upon request.
PubDate: Wed, 03 May 2023 12:20:00 +000
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- Enhancing Spectral and Energy Efficiencies in a Cognitive Radio CRAN with
PUEAs-
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Abstract: In a cloud radio access network, malicious users mitigate the attributes of primary users in order to occupy a specific idle spectrum band by sending false signals or carry out a denial of service attack. Moreover, with the increase in number of users and limited spectral and energy resources, the malicious users will compete for the spectrum with legitimate users, thus resulting in increase in spectrum scarcity problem. The most widely used defense approach against malicious users is the received signal strength method. However, harmful users can still imitate signal attributes and transmit powers of the primary users. Therefore, in order to elaborate the best method to tackle this vulnerability and hence make more spectrum available, the modified adaptive orthogonal matching pursuit localization algorithm is proposed to detect harmful users existing in the network. However, in order to elaborate the convergence speed of the proposed method, the regularized particle filter algorithm is applied to evaluate the performance of the modified adaptive orthogonal matching pursuit under real-time conditions. The restricted isometry property is used for the performance evaluation. Further, spectral and energy efficiencies are used in the simulation results for performance evaluation, in order to observe spectrum and energy utilization efficiencies. The simulation results show that the proposed method is better in terms of computational complexity, spectral efficiency, and energy efficiency compared to other matching pursuit approaches.
PubDate: Wed, 03 May 2023 12:05:00 +000
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- Combat Response Training Tester Based on Intelligent Force-Measuring
Sensor and Digital Circuit-
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Abstract: In combat sports, the team members take attack and defense as the core of sports, and the technical movements are not periodic and the application process is irregular. During the competition, observe the opponent’s neutral position, judge timely, and react quickly. At the same time, athletes are required not only to master skilled technical movements but also to have rapid and accurate adaptability. In order to improve athletes’ rapid response ability, according to the rapid response training scheme, a combat response training tester based on a force-measuring intelligent sensor and digital circuit is designed to improve the athletes’ response ability. Firstly, a comprehensive test system of combat trainer is designed based on the intelligent force-measuring sensor and MSP430, which can measure the response time, speed, and strength of boxing. Secondly, a wavelet filtering algorithm is used to filter the sensor measurement data. Finally, the striking force and striking time of each action are recorded by sensors and data acquisition devices to reflect the striking effect and reflect the “accurate” objectives of the project characteristics. Based on the noninterference and anti-interference of e-touch piezoelectric film, it can be made into universal force-measuring sensors of different specifications for fight competitions according to needs. After being connected into a module network in parallel, it can be connected with the microcontroller to determine the hit area. In addition, the team reaction training test device has laid a foundation for the scientific evaluation of improving the team members’ reaction ability and attack defense conversion ability. The launch of the comprehensive ability tester for combat athletes introduces the previous fuzzy evaluation of athletes’ comprehensive ability into a new way of scientific quantitative measurement and evaluation, which will provide a scientific basis for the selection of combat vibration items, the regulation of training process, the inspection of training effect, and the formulation of enrollment and examination standards for sports colleges and universities. The results show that the instrument can obtain the parameters of hitting strength, strength endurance, hitting speed, speed endurance, hitting impulse, and hitting power of sports biomechanics on the group of fighting events, which provides a reliable basis for coaches to implement teaching and training for athletes.
PubDate: Tue, 02 May 2023 13:50:00 +000
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- A Network Key Node Identification Method Based on Improved Multiattribute
Fusion-
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Abstract: Considering the shortcomings of the existing network key node identification methods based on multiattribute fusion, which have single evaluation methods and low decision accuracy, combined with the advantages of the high accuracy of TOPSIS (Technique for Order Preference by Similarity to an Ideal Solution) algorithm and the applicability of grey relational analysis method for incomplete information evaluation, the concept of relative closeness is proposed, and nodes are ranked in importance based on the relative closeness; a key node identification method algorithm based on improved multiattribute fusion is designed. First, the identification problem of key nodes is transformed into multiattribute decision-making method, and the decision matrix is obtained. Second, the weighting matrix is obtained by weighting them in both subjective and objective dimensions, the relative closeness is calculated for the weighting matrix. Finally, sort the network nodes by relative closeness, and network performance simulation experiments are designed using various combinations of evaluation methods and key node identification methods. The simulation results show that this method is more adaptable and improves the identification accuracy of the network key nodes.
PubDate: Tue, 02 May 2023 09:20:00 +000
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- The Application of VR Technology in Environmental Design Based on
Human-Computer Intelligent Interaction and Computing-
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Abstract: Virtual reality technology makes environmental design more convenient, and the effect is more intuitive, so designers can create more diverse design works. The practical application of virtual reality technology can make the expression of environmental design extremely realistic, can make the public’s visual, auditory, and tactile feelings different to a certain extent, so that people can enjoy the immersive feeling brought by various virtual environments, and can also concentrate people’s attention to a certain extent, so that people’s impressions continue to deepen, and deepen the image of environmental art to a certain extent, so that it plays a role in beautifying the city. The continued maturity of virtual reality technology has made it widely used in many industries in society, as has environmental design expression. Virtual reality technology has a variety of characteristics, such as interactivity, conceptuality, and multidirectional perception. It breaks through the time and space limitations of traditional environmental design and can better display and highlight the design effect to customers.
PubDate: Sun, 30 Apr 2023 14:05:01 +000
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- Industrial Internet of Things Intrusion Detection Method Using Machine
Learning and Optimization Techniques-
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Abstract: The emergence of the Internet of Things (IoT) has witnessed immense growth globally with the use of various devices found in home, transportation, healthcare, and industry. The deployment and implementation of the IoT paradigm in industrial settings lead to the architectural changes of Industrial Automation and Control Systems (IACS) plus the countless connectivity of industrial systems. This resulted in what is referred to as the Industrial Internet of Things (IIoT), which removes the barrier of connecting IACS to isolated conventional ICT platforms. In recent times, the IoT has started hacking our personal lives and not only our world, thus creating a platform for impending IoT cyberattacks. The widespread use of the IoT has created a rich platform for possible IoT cyberattacks. Machine learning (ML) algorithms have been driven solutions to secure wireless communication in IIoT-based systems, and their use in solving various cybersecurity challenges. Therefore, this paper proposes a novel intrusion detection model based on the Particle Swarm Optimization (PSO) and Bat algorithm (BA) for feature selection, and the Random Forest (RF) classifier for the classification of malicious behaviors in IIoT-based network traffic. An IIoT-based cybersecurity dataset, WUSTL-IIOT-2021 Dataset, was used to evaluate the performance of the proposed model using accuracy, recall, precision, and F1-score. The results of the two feature selection were compared to identify the most promising one. The results were compared with other recent state-of-the-art ML and multiobjective algorithms, and the results showed better performance. The RF along with BA classifier had proved to be the best classifier.
PubDate: Sun, 30 Apr 2023 13:20:00 +000
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- Decision Key-Value Feature Construction for Multihoming Big Data Network
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Abstract: The random forest algorithm under the MapReduce framework has too many redundant and irrelevant features, low training feature information, and low parallelization efficiency when dealing with multihoming big data network problems, so parallelism is based on information theory, and norms is proposed for random forest algorithm (PRFITN). In this paper, the technique used first builds a hybrid dimensional reduction approach (DRIGFN) focused on information gain and the Frobenius norm, successfully reducing the number of redundant and irrelevant features; then, an information theory feature is offered. This results in the dimensionality-reduced dataset. Finally, a technique is suggested in the Reduce stage. The features are grouped in the FGSIT strategy, and the stratified sampling approach is employed to assure the information quantity of the training features in the building of the decision tree in the random forest. When datasets are provided as key/value pairs, it is common to want to aggregate statistics across all objects with the same key. To acquire global classification results and achieve a rapid and equal distribution of key-value pairs, a key-value pair redistribution method (RSKP) is used, which improves the cluster’s parallel efficiency. The approach provides a superior classification impact in multihoming large data networks, particularly for datasets with numerous characteristics, according to the experimental findings. We can utilize feature selection and feature extraction together. In addition to minimizing overfitting and redundancy, lowering dimensionality contributes to improved human interpretation and cheaper computing costs through model simplicity.
PubDate: Sun, 30 Apr 2023 13:05:00 +000
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- A Multi-Blockchain-Based Cross-Domain Authentication and Authorization
Scheme for Energy Internet-
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Abstract: The expansion of the scale of the Power Internet of Things stimulated by the development of the Energy Internet makes the growth in demand for the effective authentication and access control technologies in the cross-domain data exchange. Based on the cross-chain technology of the blockchain and the cuckoo filter, this paper proposes a cross-domain authentication scheme for Power Internet of Things. Firstly, a cross-chain authentication architecture is established. Combined with the existing authentication technologies used in intra-domain authentication, a cross-domain authentication process based on the cross-chain technology is proposed to realize the automatic transmission of the authentication credentials from application domain to authentication domain. The cuckoo filter is deployed on the blockchain as smart contracts, and the user certificate fingerprint is inserted into the filter to realize user registration, query, and revocation, which reduces the cost of the user certificate management. Experimental results show the effectiveness and feasibility of our scheme. Based on the proposed authentication scheme, a cross-domain access control scheme based on roles and object classes is presented, by treating the object classes as controlled objects and then applying the role-based access control to the object classes, on the condition that the heterogeneous domains in the Energy Internet have the same kinds of resources.
PubDate: Sat, 29 Apr 2023 07:35:00 +000
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