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IEEE TRANSACTIONS ON GREEN COMMUNICATIONS AND NETWORKING 2024年第3期8卷 1259-1272页

作者： Liu, Chang Wang, Jun-Bo Zeng, Cheng Chen, Yijian Yu, Hongkang Pan, Yijin Southeast Univ Natl Mobile Commun Res Lab Nanjing 2100096 Peoples R China ZTE Corp State Key Lab Mobile Network & Mobile Multimedia T Shenzhen Peoples R China ZTE Corp Wireless Res Inst Shenzhen 518055 Peoples R China

multi-access edge computing (MEC) and wireless power transfer (WPT) have emerged as promising paradigms to address the bottlenecks of computing power and battery capacity of mobile devices. In this paper, we investigate the integrated scheduling of WPT and task offloading in a rechargeable multi-access edge computing network (RMECN). Specifically, we focus on exploring the tradeoff between energy efficiency, buffer stability, and battery level stability in the RMECN to obtain reasonable scheduling. In addition, we adopt a dynamic Li-ion battery model to describe the charge/discharge characteristics. Given the stochastic nature of channel states and task arrivals, we formulate a stochastic optimization problem that minimizes system energy consumption while ensuring buffer and battery level stability. In this problem, we jointly consider offloading decisions, local central processing unit (CPU) frequency, transmission power, and current of charge/discharge as optimization variables. To solve this stochastic non-convex problem, we first transform it into an online optimization problem using the Lyapunov optimization theory. Then, we propose a distributed algorithm based on game theory to overcome the excessive computation and time consumption of traditional centralized optimization algorithms. The numerical results demonstrate that the proposed tradeoff scheme and corresponding algorithm can effectively reduce the system's energy consumption while ensuring the stability of buffer and battery level.

关键词： Optimization Task analysis Wireless communication Lithium-ion batteries Protocols Mobile handsets Stochastic processes multi-access edge computing wireless powered communication network Li-ion battery model energy efficiency

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Efficient Dynamic Distributed Resource Slicing in 6G multi-access edge computing Networks With Online ADMM and Message Passing Graph Neural Networks

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IEEE TRANSACTIONS ON MOBILE computing 2024年第4期23卷 2614-2638页

作者： Asheralieva, Alia Niyato, Dusit Miyanaga, Yoshikazu Southern Univ Sci & Technol Dept Comp Sci & Engn Shenzhen 518055 Guangdong Peoples R China Southern Univ Sci & Technol Res Inst Trustworthy Autonomous Syst Shenzhen 518055 Guangdong Peoples R China Nanyang Technol Univ Sch Comp Sci & Engn Singapore 639798 Singapore Chitose Inst Sci & Technol Grad Sch Sci & Engn Chitose Hokkaido 0660012 Japan

We consider the problem of resource slicing in the 6[sup]th[/sup] generation multi-access edge computing (6G-MEC) network. The network includes many non-stationary space-air-ground-sea nodes with dynamic, unstable connections and resources, where any node can be in one of two hidden states: i) reliable-when the node generates/propagates no data errors;ii) unreliable-when the node can generate/propagate random errors. We show that solving this problem is challenging, since it represents a non-deterministic polynomial-time (NP) hard dynamic combinatorial optimization problem depending on the unknown distribution of hidden nodes' states and time-varying parameters (connections and resources of nodes) which can only be observed locally. To tackle these challenges, we develop a new deep learning (DL) model based on the message passing graph neural network (MPNN) to estimate hidden nodes' states in dynamic network environments. We then propose a novel algorithm based on the integration of MPNN-based DL and online alternating direction method of multipliers (ADMM)-extension of the well-known classical "static" ADMM to dynamic settings, where the slicing problem is solved distributedly, in real time, based on local information. We prove that our algorithm converges to a global optimum of our problem with a superior performance even in the highly-dynamic, unreliable scenarios.

关键词： 6G deep learning dynamic combinatorial optimization graph neural networks graph theory mathematical decomposition multi-access edge computing network slicing space-air-ground-sea networks ultra-reliable low-latency communications

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Task Offloading in Terrestrial-Support-Free multi-Layer multi-access edge computing

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IEEE COMMUNICATIONS MAGAZINE 2024年第7期62卷 82-87页

作者： Peng, Limei Ho, Pin-Han Zhao, Ke Kyungpook Natl Univ Sch Comp Sci & Engn Daegu South Korea UESTC Shenzhen Inst Adv Study Chengdu Peoples R China Univ Waterloo Dept Elect & Comp Engn Waterloo ON Canada

The article investigates aerial computing terrestrial-support-free multi-layer multi-access edge computing (AC-TMMEC) systems within the context of massive machine-type communications (mMTC) beyond 5G (B5G). With its four layers, namely space platform (S), high-altitude platform (HAP), low-altitude platform (LAP), and ground mobile access point platform (G), task offloading of ground users takes place in a sparsely distributed and dynamic environment mainly due to the high mobility of the AC-TMMEC entities. The article begins by reviewing the state-of-the-art, then identifies data routing and trajectory planning as new challenges in the design of AC-TMMEC offloading schemes. A simple yet effective task offloading scheme under AC-TMMEC, called iterative maximum flow algorithm (IMFA), is introduced, aiming to jointly solve the problems of source-destination pairing and data routing in a single shot via iterative maximum flow search. Extensive case studies are conducted to gain insights on the offloading latency performance of the proposed IMFA and compare it with a couple of counterparts with respect to two key parameters, namely the number of task requests and data size of each request.

关键词： Surveys Space vehicles multi-access edge computing Trajectory planning Routing Search problems Iterative methods

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Joint Partial Offloading and Resource Allocation for Parked Vehicle-Assisted multi-access edge computing

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IEEE TRANSACTIONS ON EMERGING TOPICS IN computing 2024年第3期12卷 918-923页

作者： Pham, Xuan-Qui Huynh-The, Thien Kim, Dong-Seong Kumoh Natl Inst Technol ICT Convergence Res Ctr Gumi 39177 South Korea Ho Chi Minh City Univ Technol & Educ Dept Comp & Commun Engn Ho Chi Minh City 700000 Vietnam

In recent years, parked vehicle-assisted multi-access edge computing (PVMEC) has emerged to expand the computational power of MEC networks by utilizing the opportunistic resources of parked vehicles (PVs) for computation offloading. In this article, we study a joint optimization problem of partial offloading and resource allocation in a PVMEC paradigm that enables each mobile device (MD) to offload its task partially to either the MEC server or nearby PVs. The problem is first formulated as a mixed-integer nonlinear programming problem with the aim of maximizing the total offloading utility of all MDs in terms of the benefit of reducing latency through offloading and the overall cost of using computing and networking resources. We then propose a partial offloading scheme, which employs a differentiation method to derive the optimal offloading ratio and resource allocation while optimizing the task assignment using a metaheuristic solution based on the whale optimization algorithm. Finally, evaluation results justify the superior system utility of our proposal compared with existing baselines.

关键词： parked vehicle partial computation offloading resource allocation multi-access edge computing parked vehicle multi-access edge computing partial computation offloading resource allocation

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LEO satellites selection-based computation offloading algorithm in aircraft-satellite multi-access edge computing networks

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COMPUTER COMMUNICATIONS 2024年 223卷 115-127页

作者： Zhang, Jiadong Zhang, Ruidong Shi, Wenxiao Jilin Univ Coll Commun Engn Changchun 130012 Peoples R China

The emergence of sixth -generation (6G) mobile networks and non -terrestrial networks (NTNs) has led to increased interest in low Earth orbit (LEO) satellite -based communication networks for their potential to provide global coverage and ubiquitous connectivity. In this paper, we investigate the LEO satellites selectionbased computation offloading problem in the aircraft-satellite multi-access edge computing (ASMEC) network, where LEO satellites and edge computing processors are integrated to provide ubiquitous and low -latency communication and computation services for aircraft during flights. In contrast to most existing works, which directly assume a fixed number of satellites or orbits in satellite -based MEC networks, we investigate the problem of how many satellites and which satellites to select in the ASMEC network. Our objective is to minimize the average total time delay of tasks during aircraft-satellite computation offloading. To achieve this, we formulate a nonlinear integer programming (NLIP) problem and propose the LEO satellites selectionbased computation offloading (LSSBCO) algorithm to solve it, which includes the shortest aircraft-satellite distance based access satellite selection (ASS -SD) algorithm and the nearest k ( t ) neighboring satellites selection (NSS- k ( t ) ) algorithm. We evaluate the performance of the LSSBCO algorithm in terms of the average total time delay, the maximum throughput, the average aircraft-satellite distance, the average connection duration, and the number of satellite handovers. Numerical results show that the proposed algorithm outperforms the benchmark algorithms with a lower average total time delay.

关键词： Aircraft-satellite communication Computation offloading LEO satellites selection multi-access edge computing Nonlinear integer programming Queuing system

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A Joint Caching and Offloading Strategy Using Reinforcement Learning for multi-access edge computing Users

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MOBILE NETWORKS & APPLICATIONS 2024年 1-14页

作者： Yuan, Yuan Su, Wei Hong, Gaofeng Li, Haoru Wang, Chang Beijing Jiaotong Univ Sch Elect & Informat Engn Beijing Peoples R China

The increasing adoption of UHD video applications in connected car mobile terminals is placing significant demands on the real-time processing capabilities of in-vehicle devices. Previous studies have proposed a combination of caching and offloading techniques to ensure efficient processing at the edge network. However, these studies often handle edge caching and computation offloading decisions concurrently, which adds complexity to the decision-making process and increases the update overhead of the pre-cache. In this paper, we present a novel joint control scheme that establishes a dual time-scale resource management framework for cache updates and offload scheduling. In the first phase, the edge pre-cache strategy is executed in a longer time scale, and we employ a DQN-based edge cache service replacement algorithm to ensure that local pre-cache services can be hit after offload requests. In the second phase, resource offload scheduling occurs in shorter time slots, and we guarantee low latency using a DDPG-based intra-domain computational resource allocation algorithm. Simulation results demonstrate that our proposed scheme effectively maintains a high hit rate of requests without frequent updates of the intra-domain cache, thereby reducing the overall latency of task processing. Compared to the benchmark scheme without intra-domain collaboration, our proposed scheme achieves a 50% reduction in average total processing latency and a 20% improvement in average resource utilization.

关键词： Joint Control Asynchronous Service multi-access edge computing Caching and Offloading

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A Bargaining Approach for Service Placement in multi-access edge computing With Information Asymmetries

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IEEE TRANSACTIONS ON MOBILE computing 2025年第6期24卷 5464-5481页

作者： Simon, Bernd Adrian, Paul Weber, Patrick Felka, Patrick Hinz, Oliver Klein, Anja Tech Univ Darmstadt Commun Engn Lab D-64289 Darmstadt Germany Goethe Univ Frankfurt Chair Informat Syst & Informat Management D-60629 Frankfurt Germany

multi-access edge computing (MEC) refers to deploying computation resources, known as cloudlets or edge servers, near the edge of the mobile network. Services like augmented reality (AR) benefit from MEC by service placement, which refers to installing service-specific software and allocating resources on cloudlets. Service placement in MEC improves service quality and potentially reduces costs compared to centralized cloud computing approaches. The main stakeholders in MEC are infrastructure providers (IPs), who manage the MEC infrastructure, and service providers (SPs), who offer services to users. Both have unique technical and economic perspectives, such as resource demands, resource availability, and costs. Information asymmetries exist as only IPs have access to information about their resources, and only SPs have information about service usage and resource demands. This work addresses challenges of service placement in MEC from a multi-stakeholder, techno-economic perspective. We introduce a model including the stakeholders' technical and economic goals and information asymmetries. To solve this problem efficiently, we propose a multi-stakeholder bargaining mechanism, termed Nash Backward Induction with Linear Equilibrium Strategies (NBI-LES). In a case study with 544 users and 16 SPs, we achieve 79% of the optimal reduction in traffic given by a centralized optimal service placement strategy.

关键词： IP networks Cloud computing Stakeholders Costs Biological system modeling Software Resource management Pricing Delays Smart homes Game theory information asymmetries multi-access edge computing nash bargaining service placement techno-economic modeling

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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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Computation Offloading and Band Selection for IoT Devices in multi-access edge computing

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ACM TRANSACTIONS ON MODELING AND COMPUTER SIMULATION 2025年第2期35卷

作者： Ray, Kaustabha Banerjee, Ansuman IBM India Res Lab Bangalore India Indian Stat Inst Kolkata West Bengal India

The advent of multi-access edge computing (MEC) has enabled service providers to mitigate high network latencies often encountered in accessing cloud services. The key idea of MEC involves service providers deploying containerized application services on MEC servers situated near Internet-of-Things (IoT) device users. The users access these services via wireless base stations with ultra low latency. Computation tasks of IoT devices can then either be executed locally on the devices or on the MEC servers. A key cornerstone of the MEC environment is an offloading policy utilized to determine whether to execute computation tasks on IoT devices or to offload the tasks to MEC servers for processing. In this work, we propose a two-phase Probabilistic Model Checking-based offloading policy catering to IoT device user preferences. The first stage evaluates the tradeoffs between local vs. server execution while the second stage evaluates the tradeoffs between choice of wireless communication bands for offloaded tasks. We present experimental results in practical scenarios on data gathered from an IoT test-bed setup with benchmark applications to show the benefits of an adaptive preference-aware approach over conventional approaches in the MEC offloading context.

关键词： multi-access edge computing computation offloading probabilistic model checking

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Energy-efficient collaborative task offloading in multi-access edge computing based on deep reinforcement learning

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AD HOC NETWORKS 2025年 169卷

作者： Wang, Shudong Zhao, Shengzhe Gui, Haiyuan He, Xiao Lu, Zhi Chen, Baoyun Fan, Zixuan Pang, Shanchen China Univ Petr East China Coll Comp Sci & Technol Qingdao 266580 Peoples R China

In the multi-access edge computing (MEC), task offloading through device-to-device (D2D) communication can improve the performance of edge computing by utilizing the computational resources of nearby mobile devices (MDs). However, adapting to the time-varying wireless environment and efficiently and quickly allocating tasks to MEC and other MDs to minimize the energy consumption of MDs is a challenge. First, we constructed a multi-device collaborative task offloading framework, modeling the collaborative task offloading decision problem as a graph state transition problem and utilizing a graph neural network (GNN) to fully explore the potential relationships between MDs and MEC. Then, we proposed a collaborative task offloading algorithm based on graph reinforcement learning and introduced a penalty mechanism that imposes penalties when the tasks of MDs exceed their deadlines. Simulation results show that, compared with other benchmark algorithms, this algorithm reduces energy consumption by approximately 20%, achieves higher task completion rates, and provides a more balanced load distribution.

关键词： multi-access edge computing Collaborative task offloading Graph neural network Deep reinforcement learning Device-to-Device

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