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multi-access edge computing Handover Strategies, Management, and Challenges: A Review

IEEE access 2024年 12卷 4660-4673页

作者： Alkaabi, Shaimaa R. Gregory, Mark A. Li, Shuo RMIT Univ Sch Engn Melbourne VIC 3000 Australia

The deployment and operation of multi-access edge computing (MEC) at the network edge provides low latency computing and storage services for end user devices. Support for device mobility is a functional requirement for MEC and a handover strategy that supports the movement of device and application state between MEC nodes is an underlying technology. The handover strategy utilizes an MEC node selection technique and a handover technique to provide a smooth transition between one MEC node to another. Research into MEC handover strategies has focused on algorithms, queuing, and other considerations. The migration of the MEC device and application state is a complex process that requires resource allocation in the destination MEC node that could involve provisioning application instances and, in some scenarios, support for containers or Virtual Machines to be received from the originating MEC node. This paper reviews MEC handover strategies and provides a description of the MEC reference architecture and proposed handover algorithms and techniques found in the literature. MEC handover challenges and gaps in the body of knowledge are discussed to provide guidance for future work.

关键词： Handover multi-access edge computing handover protocol state relocation edge network cloud computing application migration

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multi-access edge computing for Real-Time Applications With Sporadic DAG Tasks - A Graphical Game Approach

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IEEE TRANSACTIONS ON MOBILE computing 2024年第10期23卷 9167-9190页

作者： Asheralieva, Alia Niyato, Dusit Loughborough Univ Dept Comp Sci Loughborough LE11 3TU Leics England Nanyang Technol Univ Sch Comp Sci & Engn Singapore 639798 Singapore

We consider a multi-operator multi-access edge computing (MEC) network for applications with dependent tasks. Each task includes jobs executed based on logical precedence modelled as a directed acyclic graph, where each vertex is a job, each edge - precedence constraint, such that the job can be started only after its preceding jobs are completed. Tasks are executed by MEC servers with the assistance of workers - nearby edge devices. Each MEC server acts as a master deciding on jobs assigned to its workers. The master's decision problem is complex, as its workers can be associated with other masters in proximity. Thus, the available workers' resources depend on job assignments of all neighboring masters. Yet, as masters select their decisions simultaneously, no master knows concurrent decisions of its neighbors. Besides, some masters can belong to competing operators that have no incentives to exchange information about their decisions. To address these challenges, we formulate a novel framework based on the graphical stochastic Bayesian game, where masters play under uncertainty about their neighbors' decisions. We prove that the game admits a perfect Bayesian equilibrium (PBE), and develop new Bayesian reinforcement learning and Bayesian deep reinforcement learning algorithms enabling each master to reach the PBE independently.

关键词： Task analysis Servers Games Bayes methods Distributed computing Computational modeling Optimization 5G New Radio Bayesian methods distributed computing game theory graph theory incomplete information machine learning multi-access edge computing

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multi-access edge computing Empowered Integrated Hybrid Sensing and Communication: A Computation-Efficient Design

Multi-Access Edge Computing Empowered Integrated Hybrid Sens...

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International Conference on Ubiquitous Communication (Ucom)

作者： Dou, Chenglong Huang, Xumin Wu, Yuan Qian, Liping Quek, Tony Q. S. Univ Macau State Key Lab Internet Things Smart City Macau Peoples R China Zhejiang Univ Technol Informat Engn Hangzhou Peoples R China Singapore Univ Technol & Design Informat Syst Technol & Design Singapore Singapore

ISBN: (纸本)9798350374223;9798350374216

Integrated sensing and communication (ISAC) system has been emerged as a crucial paradigm for addressing the growing demand of emerging wireless applications that require both ultra-reliable data transmission and high-precision sensing. However, due to the limited computation capability of ISAC devices, the large amount of collected sensing data is difficult to be timely processed. In this paper, we consider that the ISAC device can offload the sensing data to a group edge helper nodes via multi-access edge computing to improve the efficiency of sensing data processing. We propose a multi-access edge computing empowered ISAC with hybrid active and passive sensing, in which the ISAC device can perform passive sensing through the sensing reflected signal from the edge helper nodes while performing active sensing. To investigate this problem, we formulate a joint optimization of the transmit beamforming for active sensing, passive sensing and offloading, as well as the computation rates of both the ISAC device and the edge helper nodes, with the objective of maximizing the total computation rates for the sensing data. Despite the non-convexity of the formulated problem, we propose an efficient algorithm to obtain its solutions. Simulation results validate the performance advantages of our multi-access edge computing empowered integrated hybrid sensing and communication.

关键词： Integrated sensing and communication (ISAC) multi-access edge computing hybrid active and passive sensing

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Bring Your Device Group (BYDG): Efficient and Privacy-Preserving User-Device Authentication Protocol in multi-access edge computing

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IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY 2025年 20卷 3346-3361页

作者： Zhang, Yan Gu, Chunsheng Shi, Peizhong Jing, Zhengjun Li, Bing Liu, Bo Jiangsu Univ Technol Sch Comp Engn Changzhou 213000 Jiangsu Peoples R China Southeast Univ Sch Cyber Sci & Engn Nanjing 210000 Jiangsu Peoples R China Univ Technol Sch Comp Sci Sydney NSW 2007 Australia

Authentication is an important security issue for multi-access edge computing (MEC). To restrict user access from untrusted devices, Bring Your Own Device (BYOD) policy has been proposed to authenticate users and devices simultaneously. However, when integrating BYOD policy into MEC authentication to improve security, issues of efficient binding and user-device conditional anonymity have not been well supported. To address these issues, we propose Bring Your Device Group (BYDG) policy by constructing efficient and privacy-preserving user-device authentication. Our core idea is to use key sequences generated by PUFs-based key derivation functions (KDFs) to not only construct efficient binding relationships, but also achieve conditional anonymity for device groups. Specifically, a flexible and secure binding method is first developed by leveraging Chinese Remainder Theorem (CRT) to bind user with device groups. Each device's CRT modulus is derived from the key sequence to construct many-to-many user-device binding relationships, which are managed in the form of on-chain Pedersen Commitment. Moreover, we design an identity anonymizing and tracing method for device groups. The key sequence is regarded as traceable device pseudo-identities, and then inserted into the cuckoo filter to reduce the on-chain storage overhead and mitigate malicious login attempts with low costs. Based on above two methods, the combination of Pedersen Commitment and Zero-Knowledge Proof of Knowledge is used to achieve user-device authentication with conditional anonymity. The security analysis was presented to demonstrate important security properties. A proof-of-concept prototype was implemented to conduct performance evaluation and comparative analysis.

关键词： Authentication Biometrics Protocols Security Cryptography Blockchains Servers Bring your own device Privacy multi-access edge computing user-device authentication security and privacy physically unclonable functions blockchain Chinese remainder theorem

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A survey on resource scheduling approaches in multi-access edge computing environment: a deep reinforcement learning study

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CLUSTER computing-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS 2025年第3期28卷 1-45页

作者： Ismail, Ahmed A. Khalifa, Nour Eldeen El-Khoribi, Reda A. Cairo Univ Fac Comp & Artificial Intelligence Informat Technol Dept 5 Dr Ahmed Zewail St Giza 12613 Egypt

multi-access edge computing (MEC) brings many services closer to user devices, alleviating the pressure on resource-constrained devices. It enables devices to offload compute-intensive tasks to nearby MEC servers. Hence, improving users' quality of experience (QoS) by reducing both application execution time and energy consumption. However, to meet the huge demands, efficient resource scheduling algorithms are an essential and challenging problem. Resource scheduling involves efficiently allocating and managing MEC resources. In this paper, we survey the state-of-the-art research regarding this issue and focus on deep reinforcement learning (DRL) solutions. DRL algorithms reach optimal or near-optimal policies when adapted to a particular scenario. To the best of our knowledge, this is the first survey that specifically focuses on the use of RL and DRL techniques for resource scheduling in multi-access computing. We analyze recent literature in three research aspects, namely, content caching, computation offloading, and resource management. Moreover, we compare and classify the reviewed papers in terms of application use cases, network architectures, objectives, utilized RL algorithms, evaluation metrics, and model approaches: centralized and distributed. Furthermore, we investigate the issue of user mobility and its effect on the model. Finally, we point out a few unresolved research challenges and suggest several open research topics for future studies.

关键词： Deep reinforcement learning Resource scheduling multi-access edge computing Content caching Computation offloading Resource management

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Availability assessment of SDN-ICN service for multi-access edge computing

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JOURNAL OF SUPERcomputing 2025年第2期81卷 1-32页

作者： Nascimento, Erick Lins, Luan Tavares, Eduardo Pereira, Paulo Dantas, Jamilson Kosta, Sokol Maciel, Paulo Univ Fed Pernambuco Ctr Informat CIn Recife PE Brazil Aalborg Univ Dept Elect Syst Aalborg Denmark

Traffic on mobile and wireless networks has seen exponential growth in the last decade. With the advent of the fifth-generation (5 G) cellular technology, there is a strict requirement to ensure system availability, capacity, and reliability to provide operation services that work through wireless network mechanisms. multi-access edge computing (MEC) is an appropriate approach for wireless access networks as such a technology. It provides support at the edge, encompassing the Mist, Fog, and Cloudlet paradigms. Additionally, software-defined networking (SDN) can enhance network performance and promote flexibility to support computation offloading and network slices. Therefore, stochastic models are important for designing MEC systems because they enable distinct arrangements to assess availability before implementing the real system. Thus, this paper presents a hierarchical modeling approach based on continuous-time Markov chains (CTMC), stochastic Petri nets (SPN), and reliability block diagrams (RBD) to assess the availability of an MEC system that adopts SDN for information-centric networks (ICN) context. Case studies demonstrate the practical feasibility of the proposed approach, in which results indicate system downtime can be reduced up to 97.52% using the conceived models to assess distinct redundancy techniques.

关键词： SDN networks multi-access edge computing Availability evaluation Hierarchical models

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multi-agent reinforcement learning for task offloading with hybrid decision space in multi-access edge computing

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AD HOC NETWORKS 2025年 166卷

作者： Wang, Ji Zhang, Miao Yin, Quanjun Yin, Lujia Peng, Yong Natl Univ Def Technol Coll Syst Engn Changsha 410073 Hunan Peoples R China

multi-access edge computing (MEC) has become a significant technology for supporting the computation-intensive and time-sensitive applications on the Internet of Things (IoT) devices. However, it is challenging to jointly optimize task offloading and resource allocation in the dynamic wireless environment with constrained edge resource. In this paper, we investigate a multi-user and multi-MEC servers system with varying task request and stochastic channel condition. Our purpose is to minimize the total energy consumption and time delay by optimizing the offloading decision, offloading ratio and computing resource allocation simultaneously. As the users are geographically distributed within an area, we formulate the problem of task offloading and resource allocation in MEC system as a partially observable Markov decision process (POMDP) and propose a novel multi-agent deep reinforcement learning (MADRL) -based algorithm to solve it. In particular, two aspects have been modified for performance enhancement: (1) To make fine-grained control, we design a novel neural network structure to effectively handle the hybrid action space arisen by the heterogeneous variables. (2) An adaptive reward mechanism is proposed to reasonably evaluate the infeasible actions and to mitigate the instability caused by manual configuration. Simulation results show the proposed method can achieve 7.12%-20.97% performance enhancements compared with the existing approaches.

关键词： multi-access edge computing multiagent proximal policy optimization (MAPPO) Hybrid action space Reward design Task offloading Task offloading

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Context-Aware Fault Classification for multi-access edge computing

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IEEE TRANSACTIONS ON NETWORK AND SERVICE MANAGEMENT 2024年第6期21卷 6290-6300页

作者： Ray, Kaustabha IBM Res India Bengaluru 560045 India

multi-access edge computing (MEC) is increasingly being adopted as the de facto enabler for ultra-low latency access to application services. By placing application services on MEC servers situated in proximity to end users, MEC avoids the large network latencies frequently experienced while accessing cloud services. MEC is envisioned as the fundamental enabler for a number of ultra-low latency safety-critical systems, including data inferencing for autonomous vehicles amongst others. The MEC paradigm is, however, highly susceptible to various types of faults such as MEC server downtime, communication link faults, network hardware faults and so on owing to the heterogeneity of hardware configurations and diverse geographies of operations. For real-time and safety-critical workloads, averting the impact of faults is a key facet. To address this challenge, we synthesize a fault classification policy for MEC that categorizes a fault as critical requiring immediate rectification or non-critical by leveraging Probabilistic Model Checking, a Formal Methods technique, to ensure probabilistic guarantees with respect to a specified failure context. We present experimental results on a real-world datasets to show the effectiveness of our approach.

关键词： Servers Object recognition Probabilistic logic Model checking Containers Social networking (online) Quality of experience multi-access edge computing fault classification probabilistic model checking

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FedICT: Federated multi-Task Distillation for multi-access edge computing

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IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS 2024年第6期35卷 952-966页

作者： Wu, Zhiyuan Sun, Sheng Wang, Yuwei Liu, Min Pan, Quyang Jiang, Xuefeng Gao, Bo Chinese Acad Sci Inst Comp Technol Beijing 100190 Peoples R China Univ Chinese Acad Sci Beijing 101408 Peoples R China Zhongguancun Lab Beijing 100190 Peoples R China Beijing Jiaotong Univ Engn Res Ctr Network Management Technol High Speed Sch Comp & Informat Technol Minist Educ Beijing 100044 Peoples R China

The growing interest in intelligent services and privacy protection for mobile devices has given rise to the widespread application of federated learning in multi-access edge computing (MEC). Diverse user behaviors call for personalized services with heterogeneous Machine Learning (ML) models on different devices. Federated multi-task Learning (FMTL) is proposed to train related but personalized ML models for different devices, whereas previous works suffer from excessive communication overhead during training and neglect the model heterogeneity among devices in MEC. Introducing knowledge distillation into FMTL can simultaneously enable efficient communication and model heterogeneity among clients, whereas existing methods rely on a public dataset, which is impractical in reality. To tackle this dilemma, Federated multi-task Distillation for multi-access edge computing (FedICT) is proposed. FedICT direct local-global knowledge aloof during bi-directional distillation processes between clients and the server, aiming to enable multi-task clients while alleviating client drift derived from divergent optimization directions of client-side local models. Specifically, FedICT includes Federated Prior Knowledge Distillation (FPKD) and Local Knowledge Adjustment (LKA). FPKD is proposed to reinforce the clients' fitting of local data by introducing prior knowledge of local data distributions. Moreover, LKA is proposed to correct the distillation loss of the server, making the transferred local knowledge better match the generalized representation. Extensive experiments on three datasets demonstrate that FedICT significantly outperforms all compared benchmarks in various data heterogeneous and model architecture settings, achieving improved accuracy with less than 1.2% training communication overhead compared with FedAvg and no more than 75% training communication round compared with FedGKT in all considered scenarios.

关键词： Computational modeling Data models Training Servers multitasking Adaptation models Optimization Distributed optimization federated learning knowledge distillation multi-access edge computing multi-task learning

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An efficient model of enhanced optimization and dilated-GRU based secured multi-access edge computing with blockchain for VANET sector

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EXPERT SYSTEMS WITH APPLICATIONS 2025年 260卷

作者： Kalidoss, Lavanya Thouti, Swapna Arunachalam, Rajesh Ramamurthy, Pugalenthi Velammal Engn Coll Dept Elect & Commun Engn Chennai 600066 Tamilnadu India CVR Coll Engn Dept Elect & Commun Engn Hyderabad 501510 Telangana India Saveetha Inst Med & Tech Sci Saveetha Sch Engn Dept Elect & Commun Engn Chennai 602105 Tamilnadu India St Josephs Coll Engn Dept Artificial Intelligence & Data Sci Old Mahabalipuram Rd Chennai 600119 Tamilnadu India

The establishment of Vehicular Ad Hoc Networks (VANETs) has brought significant advantages to humans, yet it also raises crucial safety considerations. Security is one of the major challenges in VANETs and is receiving significant attention. Hostile Onboard Units (OBUs) can use a variety of tactics including blocking, monitoring and tampering to harm neighboring OBUs to make illicit profits. The widespread use of the decentralized architecture built around blockchain technology has enabled the clear, safe, and dispersed storage and transmission of VANET application-related information without the need for an administrative center of authority. Perhaps the most important parts of the blockchain-driven VANET system involve implementing an efficient and adaptable consensus system, which remains an open academic issue. The objective of the suggested solution is to create a deep learning model for blockchain that will guarantee VANET security. This network topology is comprised of three layers: "perception, edge computing, and services". The initial layer that comprises the blockchain operation is employed to demonstrate the privacy of VANET information. Additionally, cloud-based services as well as edge computing are used by the perception layer. The data is safeguarded by the service layer through the application of the blockchain system and storage in the cloud. The last layer aids in meeting user criteria for throughput and QoS. The main objective of this model is to evaluate the reliability of vehicle nodes maintained on the blockchain. Here, the node authentication is handled by an Adaptive Dilated Gated Recurrent Unit (AD-GRU), where the hyper-parameters of the recommended AD-GRU model are optimally tuned by an Enhanced Osprey Optimization Algorithm (EOOA). Finally, the proposed system is simulated and its extensive results are carried out. In contrast with other approaches, the novel system delivers the superior results of securing the data over the VANET environ

关键词： Vehicular Ad Hoc Networks multi-access edge computing Adaptive Dilated Gated Recurrent Unit Enhanced Osprey Optimization Algorithm

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