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Game-Based computation offloading and Power Allocation for LEO Constellation Networks in Distributed and Dynamic Environment

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IEEE INTERNET OF THINGS JOURNAL 2024年第4期11卷 7040-7058页

作者： Gao, Yufang Ji, Zhi Zhao, Kanglian de Cola, Tomaso Li, Wenfeng Nanjing Univ Sch Elect Sci & Engn Nanjing 210046 Peoples R China Army Engn Univ PLA Coll Commun Engn Nanjing 210007 Peoples R China German Aerosp Ctr Inst Commun & Nav D-82234 Wessling Germany

To build the new generation of ubiquitous communication and service integration networks with "network omnipresence and computing ubiquitous," it is urgent to improve the in-orbit computing ability of low earth orbit (LEO) constellation networks and develop intelligent technology for satellite-ground collaborative edge computing. Communication tasks between ground nodes and satellites are increasing, but the satellite-to-ground spectrum resources are limited. The reasonable application of channels determines the performance of the network, which in turn affects the users' experience. An outstanding issue is how to allocate channels rationally and control the power of data transmission to reduce co-channel interference and minimize system overhead effectively. This article studies multiuser computation offloading for low earth orbit (LEO) constellation networks under dynamic environment, wherein the system overhead is minimized by joint offloading strategy and power optimization. First, we propose a generic network architecture for computation offloading of LEO constellation networks under the dynamic environment. Then, from a game-theoretic perspective, we model the overhead minimization problem as a potential game and prove that the Nash equilibrium (NE) minimizes the system overhead. After that, to reach the NE, we design the Synchronous log-linear learning-based power control algorithm and joint offloading strategy and power optimization algorithm based on SLA (JOPAS), and prove the convergence of the algorithms. Finally, the effectiveness of the proposed algorithm is verified through extensive simulations and comparisons with benchmark algorithms, and the proposed algorithm achieves near-optimal performance.

关键词： Satellites Edge computing Games computational modeling Satellite broadcasting Low earth orbit satellites Task analysis computation offloading dynamic environment LEO constellation network potential game power allocation

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Multiagent Deep Deterministic Policy Gradient-Based computation offloading and Resource Allocation for ISAC-Aided 6G V2X Networks

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IEEE INTERNET OF THINGS JOURNAL 2024年第20期11卷 33890-33902页

作者： Hu, Bintao Zhang, Wenzhang Gao, Yuan Du, Jianbo Chu, Xiaoli Xian Jiaotong Liverpool Univ Sch Internet Things Suzhou 215123 Peoples R China Shanghai Univ Sch Commun & Informat Engn Shanghai 200444 Peoples R China Xian Univ Posts & Telecommun Sch Commun & Informat Engn Xian 710121 Peoples R China Univ Sheffield Dept Elect & Elect Engn Sheffield S1 3JD England

Vehicular communications in future sixth-generation (6G) networks are expected to leverage integrated sensing and communications (ISACs) and mobile edge computing (MEC) techniques. However, the rapid proliferation of vehicle user equipment (V-UE) and the diversity of ISAC-aided and MEC-empowered vehicular communication and computation services demand a more intelligent and efficient resource allocation framework for the next-generation vehicular networks. To address this issue, we propose a comprehensive ISAC-aided vehicle-to-everything (V2X) MEC framework, where the V-UEs can offload their tasks to the edge server collocated at the roadside unit (RSU). We aim to minimize the long-term average total service delay of all the V-UEs by jointly optimizing the offloading decisions of all the V-UEs, the computation resource allocation at the ISAC-aided RSU, the transmission power, and the allocation of resource blocks for all the V-UEs, where the total service delay of a V-UE includes the task processing delay and the transmission delay if the V-UE offloads its task to the RSU. To solve the formulated mixed integer nonlinear programming problem, we design a multiagent deep deterministic policy gradient (MADDPG)-based offloading optimization and resource allocation algorithm (MADDPG-O2RA2). Simulation results demonstrate that our proposed algorithm outperforms the benchmarks in terms of convergence and the long-term average delay among all the V-UEs.

关键词： Resource management Task analysis Optimization Vehicle-to-everything Delays Communication networks Servers computation offloading deep reinforcement learning (DRL) edge intelligence integrated sensing and communications (ISACs) resource allocation vehicle-to-everything (V2X) communications

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Joint Distributed computation offloading and Radio Resource Slicing Based on Reinforcement Learning in Vehicular Networks

IEEE OPEN JOURNAL OF THE COMMUNICATIONS SOCIETY

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IEEE OPEN JOURNAL OF THE COMMUNICATIONS SOCIETY 2025年 6卷 1231-1245页

作者： Alaghbari, Khaled A. Lim, Heng-Siong Zarakovitis, Charilaos C. Latiff, N. M. Abdul Ariffin, Sharifah Hafizah Syed Chien, Su Fong Univ Teknol Malaysia Fac Elect Engn Johor Baharu 81310 Malaysia Multimedia Univ Fac Engn & Technol Melaka 75450 Malaysia Axon Log IKE ICT Dept Athens Greece

computation offloading in Internet of Vehicles (IoV) networks is a promising technology for transferring computation-intensive and latency-sensitive tasks to mobile-edge computing (MEC) or cloud servers. Privacy is an important concern in vehicular networks, as centralized system can compromise it by sharing raw data from MEC servers with cloud servers. A distributed system offers a more attractive solution, allowing each MEC server to process data locally and make offloading decisions without sharing sensitive information. However, without a mechanism to control its load, the cloud server's computation capacity can become overloaded. In this study, we propose distributed computation offloading systems using reinforcement learning, such as Q-learning, to optimize offloading decisions and balance computation load across the network while minimizing the number of task offloading switches. We introduce both fixed and adaptive low-complexity mechanisms to allocate resources of the cloud server, formulating the reward function of the Q-learning method to achieve efficient offloading decisions. The proposed adaptive approach enables cooperative utilization of cloud resources by multiple agents. A joint optimization framework is established to maximize overall communication and computing resource utilization, where task offloading is performed on a small-time scale at local edge servers, while radio resource slicing is adjusted on a larger time scale at the cloud server. Simulation results using real vehicle tracing datasets demonstrate the effectiveness of the proposed distributed systems in achieving lower computation load costs, offloading switching costs, and reduce latency while increasing cloud server utilization compared to centralized systems.

关键词： Servers Cloud computing Resource management Q-learning computational efficiency Costs Vehicle dynamics Quality of service Energy consumption computational modeling computation offloading radio resource slicing reinforcement learning distributed system mobile-edge computing (MEC) cloud computing Internet of Vehicles

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Joint computation offloading and Resource Allocation in Multi-Edge Smart Communities With Personalized Federated Deep Reinforcement Learning

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IEEE TRANSACTIONS ON MOBILE COMPUTING 2024年第12期23卷 11604-11619页

作者： Chen, Zheyi Xiong, Bing Chen, Xing Min, Geyong Li, Jie Fuzhou Univ Coll Comp & Data Sci Fuzhou 350116 Peoples R China Univ Exeter Fac Environm Sci & Econ Dept Comp Sci Exeter England Shanghai Jiao Tong Univ Dept Comp Sci & Engn Shanghai 200240 Peoples R China

Through deploying computing resources at the network edge, Mobile Edge Computing (MEC) alleviates the contradiction between the high requirements of intelligent mobile applications and the limited capacities of mobile End Devices (EDs) in smart communities. However, existing solutions of computation offloading and resource allocation commonly rely on prior knowledge or centralized decision-making, which cannot adapt to dynamic MEC environments with changeable system states and personalized user demands, resulting in degraded Quality-of-Service (QoS) and excessive system overheads. To address this important challenge, we propose a novel Personalized Federated deep Reinforcement learning based computation offloading and resource Allocation method (PFR-OA). This innovative PFR-OA considers the personalized demands in smart communities when generating proper policies of computation offloading and resource allocation. To relieve the negative impact of local updates on global model convergence, we design a new proximal term to improve the manner of only optimizing local Q-value loss functions in classic reinforcement learning. Moreover, we develop a new partial-greedy based participant selection mechanism to reduce the complexity of federated aggregation while endowing sufficient exploration. Using real-world system settings and testbed, extensive experiments demonstrate the effectiveness of the PFR-OA. Compared to benchmark methods, the PFR-OA achieves better trade-offs between delay and energy consumption and higher task execution success rates under different scenarios.

关键词： Training Resource management Task analysis Smart cities Servers Quality of service Delays Mobile edge computing computation offloading resource allocation deep reinforcement learning personalized federated learning

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Cloud-Edge-End Collaborative Intelligent Service computation offloading: A Digital Twin Driven Edge Coalition Approach for Industrial IoT

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IEEE TRANSACTIONS ON NETWORK AND SERVICE MANAGEMENT 2024年第6期21卷 6318-6330页

作者： Li, Xiaohuan Chen, Bitao Fan, Junchuan Kang, Jiawen Ye, Jin Wang, Xun Niyato, Dusit Guilin Univ Elect Technol Sch Informat & Commun Key Lab Intelligent Networking & Scenario Syst Guilin 541004 Peoples R China Natl Engn Lab Comprehens Transportat Big Data Appl Guangxi Res Inst Integrated Transportat Big Data Nanning 530001 Peoples R China Guilin Univ Elect Technol Sch Informat & Commun Guilin 541004 Peoples R China Guangdong Univ Technol Sch Automat Guangzhou 510006 Peoples R China Guangxi Univ Sch Comp & Elect Informat Nanning 530004 Peoples R China Nanyang Technol Univ Sch Comp Sci & Engn Singapore 639798 Singapore

By using the intelligent edge computing technologies, a large number of computing tasks of end devices in Industrial Internet of Things (IIoT) can be offloaded to edge servers, which can effectively alleviate the burden and enhance the performance of IIoT. However, in large-scale multi-service-oriented IIoT scenarios, offloading service resources are heterogeneous and offloading requirements are mutually exclusive and time-varying, which reduce the offloading efficiency. In this paper, we propose a cloud-edge-end collaboration intelligent service computation offloading scheme based on Digital Twin (DT) driven Edge Coalition Formation (DECF) approach to improve the offloading efficiency and the total utility of edge servers, respectively. Firstly, we establish a DT model to obtain accurate digital representations of heterogeneous end devices and network state parameters in dynamic and complex IIoT scenarios. The DT model can capture time-varying requirements in a low latency manner. Secondly, we formulate two optimization problems to maximize the offloading throughput and total system utility. Finally, we convert the multi-objective optimization problems to a Stackelberg coalition game model and develop a distributed coalition formation approach to balance the two optimizing objectives. Simulation results indicate that, compared with the nearest coalition scheme and non-coalition scheme, the proposed approach achieves offloading throughput improvements of 11.5% and 148%, and enhances the overall utility by 12% and 170%, respectively.

关键词： Cloud computing Industrial Internet of Things Task analysis Optimization Games computational modeling Heuristic algorithms Cloud-edge-end digital twin (DT) coalition game computation offloading

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Minimizing Response Delay in UAV-Assisted Mobile Edge Computing by Joint UAV Deployment and computation offloading

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IEEE TRANSACTIONS ON CLOUD COMPUTING 2024年第4期12卷 1372-1386页

作者： Zhang, Jianshan Luo, Haibo Chen, Xing Shen, Hong Guo, Longkun Minjiang Univ Sch Comp & Big Data Fujian Prov Key Lab Informat Proc & Intelligent Co Fuzhou 350121 Peoples R China Fuzhou Univ Minist Educ Engn Res Ctr Big Data Intelligence Coll Comp & Data SciFujian Key Lab Network Comp & Fuzhou 350118 Peoples R China Cent Queensland Univ Sch Engn & Technol Brisbane Qld 4000 Australia Fuzhou Univ Sch Math & Stat Fuzhou 350118 Peoples R China

As a promising technique for offloading computation tasks from mobile devices, Unmanned Aerial Vehicle (UAV)-assisted Mobile Edge Computing (MEC) utilizes UAVs as computational resources. A popular method for enhancing the quality of service (QoS) of UAV-assisted MEC systems is to jointly optimize UAV deployment and computation task offloading. This imposes the challenge of dynamically adjusting UAV deployment and computation offloading to accommodate the changing positions and computational requirements of mobile devices. Due to the real-time requirements of MEC computation tasks, finding an efficient joint optimization approach is imperative. This paper proposes an algorithm aimed at minimizing the average response delay in a UAV-assisted MEC system. The approach revolves around the joint optimization of UAV deployment and computation offloading through convex optimization. We break down the problem into three sub-problems: UAV deployment, Ground Device (GD) access, and computation tasks offloading, which we address using the block coordinate descent algorithm. Observing the $NP$NP-hardness nature of the original problem, we present near-optimal solutions to the decomposed sub-problems. Simulation results demonstrate that our approach can generate a joint optimization solution within seconds and diminish the average response delay compared to state-of-the-art algorithms and other advanced algorithms, with improvements ranging from 4.70% to 42.94%.

关键词： Autonomous aerial vehicles Optimization Mobile handsets Servers Delays Relays Heuristic algorithms Multi-access edge computing Computer architecture Cloud computing Block coordinate descent computation offloading mobile edge computing unmanned aerial vehicle deployment

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D2D-assisted cooperative computation offloading and resource allocation in wireless-powered mobile edge computing networks

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PEER-TO-PEER NETWORKING AND APPLICATIONS 2024年第6期17卷 3765-3779页

作者： Tian, Xianzhong Shao, Yuheng Zou, Yujia Zhang, Junxian Zhejiang Univ Technol Sch Comp Sci & Technol Hangzhou 310023 Peoples R China

With the increasing popularity of the internet of things (IoT) and 5th generation mobile communication technology (5G), mobile edge computing (MEC) has emerged as an innovative approach to support smart devices (SDs) in performing computational tasks. Nevertheless, the process of offloading can be energy-intensive. Traditional battery-powered SDs often encounter the challenge of battery depletion when offloading tasks. However, with the advancements in wireless power transfer technology, SDs can now achieve a sustainable power supply by harvesting ambient radio frequency energy. This paper studies the computation offloading in wireless-powered MEC networks with device-to-device (D2D) assistance. The SDs are categorized into near and far SDs based on their proximity to the MEC server. With the support of near SDs, far SDs can reduce transmission energy consumption and overall latency. In this paper, we comprehensively consider the allocation of energy harvesting time, transmission power, computation resources, and offloading decisions for SDs, establishing a mathematical model aimed at minimizing long-term average delay under energy constraints. To address the time-varying stochastic nature resulting from dynamic task arrivals and varying battery levels, we transform the long-term problem into a deterministic one for each time slot by introducing a queue and leveraging Lyapunov optimization theory. We then solve the transformed problem using deep reinforcement learning. Simulation results demonstrate that the proposed algorithm performs effectively in reducing delay and enhancing task completion rates.

关键词： computation offloading D2D communications Energy harvesting Deep reinforcement learning Lyapunov optimization

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Joint routing and computation offloading based deep reinforcement learning for Flying Ad hoc Networks

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COMPUTER NETWORKS 2024年 249卷

作者： Lin, Na Huang, Jinjiao Hawbani, Ammar Zhao, Liang Tang, Hailun Guan, Yunchong Sun, Yunhe Shenyang Aerosp Univ Sch Comp Sci Shenyang Peoples R China

Flying ad -hoc networks (FANETs) consisting of multiple Unmanned Aerial Vehicles (UAVs) are widely used due to their flexibility and low cost. In scenarios such as crowdsensing and data collection, data collected by UAVs are transmitted to base stations for processing and then sent to data centers. Still, the deployment of base stations is costly and inflexible. To address this issue, this paper introduces a position -based Computing First Routing (CFR) protocol designed for efficient task transmission and computation offloading in FANETs. This protocol facilitates task processing during data transfer and ensures the delivery of fully processed results to the data center. Considering the dynamically changing topology of the FANETs and the uneven distribution of the UAVs' computation power, deep reinforcement learning is used to make multi -objective decisions based on the Q -values computed by the model. FANETs are centerless clusters, and two -hop neighbor tables containing position and computing power information are used to make less costly decisions. Simulation experiments demonstrate that CFR outperforms other benchmark schemes with an approximately 6% higher packet delivery rate, an approximately 21% reduction in end -to -end delay, and about a 34% decrease in total cost. Furthermore, it effectively ensures the completion of task offloading before reaching the destination node. This occurs due to the design of a hierarchical reward function that takes into account dynamic changes in delay and energy consumption, as well as the injection of neighbor computing power information into the two -hop neighbor table.

关键词： Unmanned Aerial Vehicles (UAVs) computation offloading Routing Flying Ad-hoc Networks (FANETs)

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Multiobjective Deep Reinforcement Learning for computation offloading and Trajectory Control in UAV-Base-Station-Assisted MEC

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IEEE INTERNET OF THINGS JOURNAL 2024年第19期11卷 31805-31821页

作者： Huang, Hao Chai, Zheng-Yi Sun, Bao-Shan Kang, Hong-Shen Zhao, Ying-Jie Tiangong Univ Sch Comp Sci Tianjin Key Lab Autonomous Intelligence Technol & Tianjin 300387 Peoples R China

Unmanned aerial vehicle (UAV) and base station jointly assisted multiaccess edge computing (UB-MEC) technology is a promising direction to provide flexible computing services for resource-limited devices. Due to the non-real-time observation of device loads and the dynamic nature of demand in UB-MEC, it is a highly challenging problem to make UAV respond in real time to meet user's dynamic preferences in UB-MEC. To this end, we propose a multiobjective deep reinforcement learning (MODRL) for computation offloading and trajectory control (COTC) of UAV. First, the problem is formulated as a multiobjective Markov decision process (MOMDP), where the traditional scalar rewards are extended to vector, corresponding to the number of task data collected, the completion delay, and the UAV's energy consumption, and the weights are dynamically adjusted to meet different user preferences. Then, considering the device load information stored in UAV is non-real-time, an attentional long short-term memory (ALSTM) network is designed to predict real-time states by autofocusing important historical information. The near on-policy experience replay (NOER) reviews experiences close to on-policy can better promote learning of current strategy. The simulation results show that the proposed algorithm can obtain the action policy which meets the user's time-varying preferences, and can achieve a good balance between different objectives under different preferences.

关键词： Autonomous aerial vehicles Task analysis Delays Energy consumption Real-time systems Trajectory Servers computation offloading multiaccess edge computing (MEC) multiobjective reinforcement learning trajectory control unmanned aerial vehicle (UAV)

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Multi-Agent Deep Reinforcement Learning-Based computation offloading in LEO Satellite Edge Computing System

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IEEE COMMUNICATIONS LETTERS 2024年第10期28卷 2352-2356页

作者： Wu, Jian Jia, Min Zhang, Ningtao Guo, Qing Harbin Inst Technol Sch Elect & Informat Engn Harbin 150006 Peoples R China

Efficient computation offloading is crucial for resource-constrained users in low earth orbit (LEO) satellite edge computing system. The proposed Dueling Double Deep Q Network (D3QN)-based computation offloading algorithm considers LEO satellite mobility, dynamic load levels, queuing theory, and jointly optimizes system delay and energy consumption. Simulation results show that the proposed algorithm has better system cost than other comparison algorithms.

关键词： Low earth orbit satellites Satellites Task analysis computational modeling Edge computing Heuristic algorithms Energy consumption Mobile edge computing low earth orbit satellite D3QN computation offloading queue

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