关键词： DRL   Fusion System Design   Intelligent Video Surveillance   Internet of Things   Machine Learning   Mobile Edge Computing  

摘要： Currenlty, wireless communication that is successful in the Internet of Things (IoT) must be long-lasting and self-sustaining. The integration of machine learning (ML) techniques, including deep learning (DL), has enabled IoT networks to become highly effective and self-sufficient. DL models, such as enhanced DRL (EDRL), have been developed for intelligent video surveillance (IVS) applications. Combining multiple models and optimizing fusion scores can improve fusion system design and decision-making processes. These intelligent systems for information fusion have a wide range of potential applications, including in robotics and cloud environments. Fuzzy approaches and optimization algorithms can be used to improve data fusion in multimedia applications and e-systems. The camera sensor is developing algorithms for mobile edge computing (MEC) that use action-value techniques to instruct system actions through collaborative decision-making optimization. Combining IoT and deep learning technologies to improve the overall performance of apps is a difficult task. With this strategy, designers can increase security, performance, and accuracy by more than 97.24%, as per research observations. © 2023, American Scientific Publishing Group (ASPG). All rights reserved.

关键词： Delays   Task analysis   Servers   Internet of Things   Edge computing   Ultra reliable low latency communication   Queueing analysis   Grant-free random access   6G   mobile edge computing   digital twin  

摘要： Digital twin-aided (DT) edge computing is investigated, where users utilize grant-free random access with adaptive rate to offload their tasks to the edge server. A novel, with lower implementation complexity, probabilistic partial offloading scheme is introduced, while each device is assumed to have an infinite buffer to store its tasks. The aim of the proposed work is to minimize the average delay of the partial offloading. To that end, the average delay of waiting in the queue, the delay of offloading, and the local computation delay are extracted by using queuing theory tools. Then, the non-convex problem of minimizing the average delay of all clients is formulated, while taking into account DT imperfections. Successive convex approximation (SCA), alternating optimization (AO), and various algebraic manipulations are utilized to transform the problem into an equivalent convex problem with tractable solution. Finally, simulation results showcase the value of the proposed analysis and offer important insights for the proposed DT-aided edge network. Specifically, the proposed partial offloading scheme is shown to be more delay efficient compared to both local computing and full offloading, particularly, for greater task generation rates at the users. Also, the impact of the DT imperfections at the average delay is shown to be more notable as the number of users, or the tasks' size, increases.

关键词： generalized singular value decomposition   MIMO   mobile edge computing   non-orthogonal multiple-access  

摘要： This paper presents a comprehensive study that examines the fundamental concept of the non-orthogonal multiple access (NOMA) scheme and provides its detailed comparison with the orthogonal multiple access (OMA) technique. Furthermore, the paper explores the application of the generalized singular value decomposition (GSVD) method in conjunction with NOMA, accompanied by a detailed review of GSVD-based NOMA systems. This study also introduces the concept of mobile edge computing (MEC) and extensively discusses its key parameters. Furthermore, a comprehensive analysis of NOMA MEC is presented, shedding light on its potential advantages and challenges. The aims of this study are to provide a comprehensive understanding of the aforementioned topics and contribute to the advancement of MIMO-NOMA systems. © 2023 National Institute of Telecommunications. All rights reserved.

关键词： Data integrity   Protocols   Cloud computing   Edge computing   Servers   Time factors   Privacy   Mobile edge computing   data integrity checking   privacy-preserving  

摘要： Mobile edge computing (MEC) is proposed as an extension of cloud computing in the scenarios where the end devices desire better services in terms of response time. Because the edges are usually owned by individuals or small organizations with limited operation capabilities, the data on the edges are easily corrupted (due to external attacks or internal hardware failures). Therefore, it is essential to verify data integrity i the MEC. We propose two Integrity Checking protocols for the mobile Edge storage, called ICE-basic and ICE-batch. Our protocols allow a third-party verifier to check the data integrity on the edges without violating users' data privacy and query pattern privacy. We rigorously prove the security and privacy guarantees of the protocols. In addition, we have investigated how to let the end devices cache some verification tags such that the communication cost between end devices and the cloud can be further reduced when a user connects to multiple edges in sequence. We have implemented a proof-of-concept system that runs ICE, and extensive experiments are conducted to evaluate the performance of the proposed protocols. The theoretical analysis and experimental results demonstrate the proposed protocols are efficient both in computation and communication.

关键词： Mobile edge computing  

摘要： As a new cloud service mode, mobile edge computing distributes the traditional centralized deployment and management of cloud resources to the wireless access network. Mobile services can be processed for the sake of obtaining a good service experience and reduce the network load of the backtrip network. In order to effectively improve the end-to-end service delay of the flow in multi-clusters coexisting mobile edge computing (MEC) network, an improved virtual network function (VNF) service chain deployment algorithm based on Q-learning was proposed. Based on the planning model, a spatial-temporal optimization model of VNF service chain deployment was established by markov decision process, and an improved BPQ-learning algorithm was designed to solve the model. The method considered both the virtual deployment of MEC service chain in space dimension and the life cycle management of VNF in time dimension. The optimization of VNF deployment is realized. The possible network congestion is avoided by deployment service nodes in advance. Experimental results show that the proposed strategy can provide end-to-end services with lower latency and better experience for delaysensitive mobile services under different VNF service chain scale, service node scale, cluster number and logical connection relationship among the virtual network functions. © 2023, IAENG International Journal of Computer Science. All Rights Reserved.

关键词： Attention Mechanism   Feature Extraction   Hypergraph   Mobile Edge Computing   Multi-Feature   Video Recommendation  

摘要： With the massive growth of edge devices, how to provide users with video recommendation services in a mobile edge environment has become a research hotspot. Most traditional video recommendation methods regard the relationship between user and neighbor to be linear and ignore higher-order connectivity among users, which results in poor recommendation performance. Besides, these methods use a single feature to represent user preferences, which cannot effectively alleviate the data sparsity problem. To improve the performance of video recommendation, this article proposes a multi-feature video recommendation method based on hypergraph convolution (MVRHC). Hypergraph convolution is adopted to compute user neighborhood-level features for modeling high-order correlations among users. Final features are obtained by fusing multi-party features through attention mechanism. And video recommendation is then implemented based on the obtained features. Experimental results on two real-world datasets demonstrate that MVRHC has better performance compared with baseline methods.

关键词： Mobile edge computing  

摘要： The daily functioning of civilization depends heavily on transportation. In most cities, a sizable section of the working class consistently attends both employment and education. There are many different things you can do when commuting, such as unwinding, eating out, and other things. The most popular means of transportation in North Cyprus, particularly in developing cities, is island transportation, which includes the usage of both private cars and commercial vehicles. The advent of edge computing, which offers the opportunity to connect potent processing servers next to the mobile device, is a significant step toward improving user experience and reducing resource use. Mobile Edge Computing is the next trustworthy approach for how mobile devices consume communications and computing. Offloading computation is a key component developing mobile edge computing, which enables devices to get around clustering techniques' limitations and get around computing, storage, and energy constraints. However, computation offloading is not always the best strategy to use;making choosing unloading is a critical step that requires consideration of numerous factors. For instance, shifting the high-resource node to an edge server and granting similar capabilities to the low-resource nodes would delegate heavy duties to the external unit inside the network. The evaluations' results were noteworthy and substantial. Problems involving the vehicles of institutions and organizations can be resolved using the suggested solution to the school bus routing issue. We also test the impact of network latency on the delivery of a particular result using an Edge Computing simulator. © 2023,International Journal of Interactive Mobile Technologies. All Rights Reserved.

关键词： Bayesian classifier   Cloud computing   K-nearest neighbor   mobile edge computing   monitoring   QoS  

摘要： Mobile edge computing is a new computing paradigm that performs computing on the edge of a network. It provides services to users by deploying edge servers near mobile devices. Services may be unavailable or do not satisfy the needs of users due to changing edge environments. Quality of service (QoS) is commonly employed as a critical means to indicate qualitative status of services. It is particularly important to monitor QoS of services timely and effectively in the mobile edge environment. However, user mobility and dependencies among QoS values often cause the monitoring results to deviate from the real results in the mobile edge environment. Existing QoS monitoring approaches have not taken into account these problems. To address the problems, this paper proposes ghBSRM-MEC (Gaussian hidden BayeSian Runtime Monitoring for Mobile Edge Computing), a novel mobility and dependence-aware QoS monitoring approach for the mobile edge environment. This approach assumes that the QoS attribute values of edge servers obey Gaussian distribution. It constructs a parent property for each property, thus reducing the dependence between properties. During the training stage, a Gaussian Hidden Bayesian classifier is constructed for each edge server. During the monitoring stage, combining with a KNN algorithm, the classifier is changed dynamically based on user mobility to realize QoS monitoring in the mobile edge environment. The experimental results validate the feasibility, effectiveness, and efficiency of ghBSRM-MEC. © 2023 Taru Publications. All rights reserved.

关键词： metaverse   Web 30   resource slicing   task coordination   digital twin   service realisation delay   blockchain   6G   users economic cost   utility   dissipated energy value   mobile edge computing   quality maintain ratio  

摘要： Web 3.0 is referred to as immersive web technology in which human users can use metaverse platforms and experience digital realities. Metaverse is a virtual world in which users can interact with other users/services in an immersive manner. The existing research articles did not provide any unified resource slicing and task service coordination framework for both current/Web 3.0 and previous generation/Web 2.0-based task execution by considering intelligent resource slicing, worker selection, task types, and deadlines. To bridle these issues, this paper delivers an intelligent resource slicing scheme with multi-task scheduling and task service coordination framework for Web 2.0, Web 3.0, and metaverse-based task execution over 6G enhanced immersive networks by scrutinising resources, different current and previous generation tasks with deadline requirements, delay, and utility gain, into account. The results notified that 59.90% delay and 78% utility gain is attained in the proposed scheme over the benchmark scheme.

关键词： Mobile edge computing   Hyper-heuristics   Meta-heuristics   Task offloading   Optimization  

摘要： To overcome with the computation limitation of resource-constrained wireless IoT edge devices, providing an efficient task computation offloading and resource allocation in distributed mobile edge computing environment is consider as a challenging and promising solution. Hyper-heuristic in recent times is gaining popularity due to its general applicability of same solution to solve different types of problems. Hyper-heuristic is generally a heuristic method or framework which iteratively evaluates and chooses the best low-level heuristic, to solve different types of problems. In this paper, we try to solve wireless device task offloading in mobile edge computing, which is a non-convex and NP-Hard problem by using a proposed novel Hyper-Heuristic Framework using Stochastic Heuristic Selection (HHFSHS) using Contextual Multi-Armed Bandit (CMAB) with Epsilon-Decreasing strategy, considering two key Quality of Service (QoS) objectives computation time and energy consumption. These multiobjective criteria are modeled as single-objective optimization problem with the goal to minimize latency and energy consumption of wireless devices without losing the pareto optimality. Finally, evaluate its performance by comparing with other individual meta-heuristic algorithms.