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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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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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Empirical Evaluation of QUIC-Based Software-Defined Service Migration in multi-access edge computing Over 5G Networks

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JOURNAL OF NETWORK AND SYSTEMS MANAGEMENT 2025年第2期33卷 1-25页

作者： Chu, Tran-Tuan Labiod, Mohamed Aymen Augustin, Brice Mathialahan, Kajanan Mellouk, Abdelhamid Univ Paris Est Creteil LISSI TincNET F-94400 Vitry Sur Seine Val De Marne France

Live Service Migration in multi-access edge computing (MEC) environments is essential to ensure seamless service continuity for users in dynamic network scenarios. While advances such as QUIC-based connection migration and CRIU-based container migration have improved reliability, they remain dependent on maintaining active connections with the source server, which is impractical in conditions of high mobility, unstable links, or server outages. Additionally, existing solutions lack a centralized mechanism to optimally manage migration paths. This study introduces the Software-Defined Service Migration (SDSM) paradigm, leveraging a centralized SDN controller, the QUIC protocol, and MEC technologies to enhance service migration. Our approach overcomes the limitations of traditional methods by preemptively coordinating container and connection migrations. Empirical evaluations on a 5G-based testbed and emulated environments demonstrate significant reductions in migration times and improved recovery in mobile scenarios. These results establish SDSM as a robust solution for next-generation network applications, ensuring consistent and adaptive service quality.

关键词： multi-access edge computing Software-defined networking Service migration QUIC protocol OpenAirInterface

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Load-balanced multi-user mobility-aware task offloading in multi-access edge computing

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COMPUTER COMMUNICATIONS 2025年 235卷

作者： Pang, Shanchen Zhou, Meng Gui, Haiyuan He, Xiao Wang, Nuanlai Wang, Luqi China Univ Petr East China Coll Comp Sci & Technol Qingdao 266580 Shandong Peoples R China State Key Lab Chem Safety Qingdao Peoples R China Shandong Key Lab Intelligent Oil & Gas Ind Softwar Qingdao Shandong Peoples R China

In scenarios with dense user network service requests, multi-access edge computing demonstrates significant advantages in reducing user device load and decreasing service response time. However, the dynamic changes in user trajectories cause edge server load fluctuations, inevitably impacting the overall service processing performance. To tackle this problem, this paper introduces a load-balanced multi-user mobility-aware service request offloading method, achieving efficient service request offloading in mobile user scenarios. Specifically, this paper divides the service request offloading problem into two stages: dynamic edge server allocation and real-time offloading decision generation. In the first stage, users are allocated edge servers based on their location distribution, implementing an adaptive decreasing variance optimization server load balancing algorithm to achieve edge server load balancing. In the second stage, based on the edge server allocation results from the first stage, a latency performance self-optimizing task offloading decision-making algorithm is employed to minimize the processing latency of user requests, utilizing dueling double deep Q-network to generate real-time decisions on whether to offload service requests to the corresponding edge servers. According to experimental results, the proposed algorithm markedly decreases the processing latency of user network service requests in scenarios of different scales, with an average task completion rate of 99.94%. This effectively addresses the problem of inefficient processing requests caused by load fluctuations due to user movement in multi-access edge computing.

关键词： multi-access edge computing Load balance Mobility-aware task offloading Deep reinforcement learning edge server allocation

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Soca: secure offloading considering computational acceleration for multi-access edge computing

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WIRELESS NETWORKS 2025年第2期31卷 1021-1035页

作者： Yi, Meng Yang, Peng Xie, Jinhu Fang, Cheng Li, Bing Southeast Univ Sch Comp Sci & Engn Nanjing 211189 Peoples R China Southeast Univ Key Lab Comp Network & Informat Integrat Minist Educ Nanjing 211189 Peoples R China Southeast Univ Coll Software Engn Nanjing 211189 Peoples R China Southeast Univ Sch Cyber Sci & Engn Nanjing 211189 Peoples R China

Due to the complexity and security requirements of edge computing environments and the limited resources of terminals, secure offloading in multi-access edge computing (MEC) networks has emerged as a critical and urgent research area. However, many studies on task offloading often ignore the necessary balance between security requirements and efficiency. To address this issue, we propose a Secure Offloading Strategy Considering Computational Acceleration, named SOCA, designed to bolster security while preserving offloading efficiency. Specifically, the secure offloading problem is modeled as a multi-objective optimization problem by achieving a composite function of latency mitigation and security metrics as the optimization objective, which is solved by the ChaCha20-based offloading decision algorithm (ChaCha20-ODA). The algorithm employs the ChaCha20 encryption protocol as its security mechanism. By executing a quarter-round function to generate a keystream, it provides robust protection for data tasks, ensuring that the data remains impervious to malevolent interception by adversaries throughout the transmission process. Furthermore, to improve the computational efficiency of task offloading, the algorithm simultaneously leverages both edge and local computing resources, achieving computational acceleration by optimizing the appropriate offload ratio. The experimental results illustrate that as compared with baselines, our approach achieves remarkable improvement in the balance between latency and safety benchmarks, which demonstrates the superiority of our method.

关键词： multi-access edge computing Security Offloading strategy ChaCha20 Computational acceleration

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Advanced Beamforming and multi-access edge computing: Empowering Ultra-Reliable and Low-Latency Applications in 6G Networks

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INTERNATIONAL JOURNAL OF COMMUNICATION SYSTEMS 2025年第5期38卷

作者： Gunasekaran, K. Dhanasekaran, S. Kumar, R. Vinod Aswath, S. ASAN Mem Coll Engn & Technol Dept Elect & Commun Engn Kancheepuram India Sri Eshwar Coll Engn Dept Elect & Commun Engn Coimbatore India Saveetha Engn Coll Dept Elect & Commun Engn Chennai India Veltech Rangarajan Dr Sagunthala R&D Inst Sci & Te Dept Elect & Commun Engn Chennai India

6G technology is expected to transform communications by allowing the Internet of Everything, representing a huge advance in 2030. While B5G has not yet been established, various nations are actively working on 5G, but certain research groups are presently devoting their attention to the creation of 6G technologies. The upcoming 6G networks promise higher quality of service (QoS) features, including virtual reality and holographic communications. multi-access edge computing (MEC) and, in particular, the offloading idea are key components of 6G innovation that enable resource-intensive application design. If the wireless link used for computational offloading is inefficient, MEC's true potential can be impeded. Intelligent beamforming and MEC have garnered attention recently. Systematically optimizing the wireless communication environment, these developments improve connectivity between user equipment (UE) and base station (BS). By increasing the electrical charge of the reflectable signal, intelligent beamforming increases the range and efficacy of Back Communication. Particularly, this study assesses how well the MEC structure performs in urban microcellular settings when intelligent beamforming is used in communications. It has been demonstrated that the use of Intelligent Reflecting Surfaces (IRS) greatly reduces spectrum and energy usage. This study's results are implemented in Python software. Our suggested strategy shows better latency compared to other optimization methods and shorter task completion times than traditional methods. When compared to conventional techniques, the suggested MEC-enabled network showcasing lower latency (4.9 ms) and efficient network congestion management and task completion time (30 ms) demonstrates a significant performance. These results highlight how intelligent beamforming and MEC have a lot of potential to shape the architecture of 6G networks in the future.

关键词： 6th generation base station beamforming multi-access edge computing user equipment

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A joint optimization of resource allocation management and multi-task offloading in high-mobility vehicular multi-access edge computing networks

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AD HOC NETWORKS 2025年 166卷

作者： Min, Hong Rahmani, Amir Masoud Ghaderkourehpaz, Payam Moghaddasi, Komeil Hosseinzadeh, Mehdi Gachon Univ Sch Comp Seongnam South Korea Natl Yunlin Univ Sci & Technol Future Technol Res Ctr Touliu Yunlin Taiwan Khajeh Nasir Toosi Univ Technol Dept Informat Technol Tehran Iran Islamic Azad Univ Dept Comp Engn Urmia Branch Orumiyeh Iran Duy Tan Univ Sch Comp Sci Da Nang Vietnam Duy Tan Univ DTU AI & Data Sci Hub DAIDASH Da Nang Vietnam

Vehicular communications have advanced data exchange and real-time services in intelligent transportation systems by exploiting advanced communication between vehicles and infrastructure. The emergence of multiaccess edge computing (MEC) has further elevated this field by utilizing distributed edge resources near vehicles for low-latency data processing and high-reliability communication. In this dynamic environment, adequate resource allocation and task offloading are pivotal to ensure superior performance, lower latency, and efficient network resource utilization, enhancing Quality of Service (QoS) and overall driving experience and safety. This paper presents a developed vehicular network and offloading mechanism, introducing a resource management model with real-time allocation and load balancing. The proposed method integrates task prioritization, multi- agent collaboration, context-aware decision-making, and distributed learning to optimize network performance. The introduced optimized algorithm initializes Q-networks and target networks, sets up an experience replay buffer, and configures agents with local state representations. Agents use an epsilon-greedy policy for action selection, update Q-values through experience replay, and prioritize tasks based on urgency while sharing state information for collaborative decision-making. Evaluations through simulation demonstrate optimized performance, enhancing efficiency in vehicular MEC networks compared to baseline and the other well-known algorithms.

关键词： Double Deep Q-Network Vehicular communication multi-access edge computing Resource allocation Task offloading

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