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Blockchain-Enabled computing offloading and Resource Allocation in Multi-UAVs MEC Network: A Stackelberg Game Learning Approach

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IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY 2025年 20卷 3632-3645页

作者： Chen, Jiayi Kuang, Zhufang Zhang, Yuhao Lin, Siyu Liu, Anfeng Cent South Univ Forestry & Technol Coll Comp & Math Changsha 410004 Peoples R China Beijing Jiaotong Univ Sch Elect & Informat Engn Beijing 100044 Peoples R China Beijing Jiaotong Univ State Key Lab Rail Traff Control & Safety Beijing Peoples R China Cent South Univ Sch Elect Informat Changsha 410010 Peoples R China

Unmanned Aerial Vehicle (UAV) is a promising technology that can serve as aerial base stations to assist the Internet of Things (IoT) network and solve various problems, such as expanding network coverage, improving network performance, transmitting energy to IoT devices, and performing IoT compute-intensive tasks. However, due to the communication between UAVs and the migration of computing tasks, privacy and security during the computing offloading process are challenging issues. To this end, we design an air-to-air multi-UAVs MEC network system based on multi-coalition game, and introduce blockchain technology to ensure privacy and security between UAVs, effectively ensuring the security and confidentiality of computing offloading between UAVs. In this paper, the joint optimization problem of UAV channel selection, UAV location deployment, block processor decision, block processor transmission power, and block processor generation frequency is studied. The goal is to minimize the weighted average sum of energy consumption and delay for MEC task computing and blockchain task processing. To handle this intractable issue, the original problem is decomposed into two subproblems and solved alternately with each other. In addition, the Joint Convex Optimization and Stackelberg Game Hierarchical (JCSH) algorithm is proposed, which solves the problem of blockchain-enabled computing offloading and resource allocation. The simulation results show that the JCSH algorithm has better performance and stronger robustness compared to other algorithms under different parameter settings.

关键词： Autonomous aerial vehicles Blockchains Delays Energy consumption Optimization Games Security Resource management Privacy Network systems Multi-unmanned aerial vehicles mobile edge computing blockchain Stackelberg game computing offloading

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Energy-Efficient Aerial STAR-RIS-Aided computing offloading and Content Caching for Wireless Sensor Networks

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SENSORS 2025年第2期25卷 393-393页

作者： Yang, Xiaoping Wang, Quanzeng Yang, Bin Cao, Xiaofang Beijing Univ Technol Coll Comp Sci Beijing 100124 Peoples R China Beijing Wuzi Univ Sch Business Beijing 101149 Peoples R China

Unmanned aerial vehicle (UAV)-based wireless sensor networks (WSNs) hold great promise for supporting ground-based sensors due to the mobility of UAVs and the ease of establishing line-of-sight links. UAV-based WSNs equipped with mobile edge computing (MEC) servers effectively mitigate challenges associated with long-distance transmission and the limited coverage of edge base stations (BSs), emerging as a powerful paradigm for both communication and computing services. Furthermore, incorporating simultaneously transmitting and reflecting reconfigurable intelligent surfaces (STAR-RISs) as passive relays significantly enhances the propagation environment and service quality of UAV-based WSNs. However, most existing studies place STAR-RISs in fixed positions, ignoring the flexibility of STAR-RISs. Some other studies equip UAVs with STAR-RISs, and UAVs act as flight carriers, ignoring the computing and caching capabilities of UAVs. To address these limitations, we propose an energy-efficient aerial STAR-RIS-aided computing offloading and content caching framework, where we formulate an energy consumption minimization problem to jointly optimize content caching decisions, computing offloading decisions, UAV hovering positions, and STAR-RIS passive beamforming. Given the non-convex nature of this problem, we decompose it into a content caching decision subproblem, a computing offloading decision subproblem, a hovering position subproblem, and a STAR-RIS resource allocation subproblem. We propose a deep reinforcement learning (DRL)-successive convex approximation (SCA) combined algorithm to iteratively achieve near-optimal solutions with low complexity. The numerical results demonstrate that the proposed framework effectively utilizes resources in UAV-based WSNs and significantly reduces overall system energy consumption.

关键词： unmanned aerial vehicle wireless sensor networks simultaneously transmitting and reflecting reconfigurable intelligent surface computing offloading content caching

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computing offloading Decision Based on Multi-objective Immune Algorithm in Mobile Edge computing Scenario

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WIRELESS PERSONAL COMMUNICATIONS 2023年第2期130卷 1025-1043页

作者： Zhu, Si-feng Sun, En-lin Zhang, Qing-hua Cai, Jiang-hao Tianjin Chengjian Univ Sch Comp & Informat Engn Tianjin 300384 Peoples R China Tianjin Chengjian Univ Lib Tianjin 300384 Peoples R China

Mobile edge computing (MEC) technology enables mobile devices in communication network systems offloading tasks to edge servers, effectively reducing response time and energy consumption. However, task offloading decisions become a significant difficulty when the system's mobile and service device count rises. In this paper, the problem of response time and energy consumption of the system is modeled as a multi-objective optimization problem, and we design an improved evolutionary algorithm based on immune algorithm, which can effectively obtain a set of solutions between response time and energy consumption. The simulation results show that our scheme can meet the response time requirements and obtain a lower energy consumption strategy when compared to the offloading scheme in the existing literature.

关键词： Mobile edge computing computing offloading Multi-objective algorithm Immune algorithm

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computing offloading With Fairness Guarantee: A Deep Reinforcement Learning Method

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 2023年第10期33卷 6117-6130页

作者： Hao, Hao Xu, Changqiao Zhang, Wei Yang, Shujie Muntean, Gabriel-Miro Qilu Univ Technol Shandong Acad Sci Shandong Comp Sci Ctr Natl Supercomp Ctr Jinan Jinan 250014 Peoples R China Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing 100876 Peoples R China Dublin City Univ Sch Elect Engn Performance Engn Lab Dublin 9 Ireland

Edge computing can reduce service latency and save backhaul bandwidth by completing services at network edges, providing support for diverse computation-intensive and delay-sensitive services. However, it is not practical to support all services at edge nodes due to the limited network resources. The decision that which services can be provided locally and which services should been offloaded to cloud significantly impacts the user experience. Cloud-edge computing offloading becomes an important issue in edge computing. In this paper, we take the fairness into the optimization objective of computing offloading problem, and consider both computing capacity and storage space as problem constraints. The problem is formulated as a long-term average optimization problem to maximize the alpha-fair utility function of saved time, and further translated as a Markov decision process. As the optimization problem with fairness guarantee and huge action space, we cannot solve it with traditional methods. Therefore, an innovative multi-update deep reinforcement learning algorithm is proposed which can optimize the objective with alpha -fair utility function and reduce dramatically the size of action space. We also prove the convergence of our algorithm theoretically. To our best knowledge, the long-term average optimization of computing offloading with fairness guarantee is rarely seen in literature. Extensive simulation experiments show that our algorithm can converge quickly and has better performance in terms of service delay and fairness.

关键词： Mobile edge computing computing offloading deep reinforcement learning fairness guarantee

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computing offloading for RIS-Aided Internet of Everything: A Cybertwin Version

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IEEE INTERNET OF THINGS JOURNAL 2024年第11期11卷 20443-20456页

作者： Gu, Xiaohui Zhang, Guoan Duan, Wei Dang, Shuping Wen, Miaowen Ho, Pin-Han Nantong Univ Sch Informat Sci & Technol Nantong 226019 Peoples R China Univ Bristol Sch Elect Elect & Mech Engn Bristol BS8 1UB Glocs England South China Univ Technol Sch Elect & Informat Engn Guangzhou 510640 Peoples R China Univ Elect Sci & Technol China Shenzhen Inst Adv Study Shenzhen 518000 Peoples R China Univ Waterloo Dept Elect & Comp Engn Waterloo ON N2L 3G1 Canada

Cybertwin technology introduces a novel paradigm employing digital twins to model complex physical systems within a cyber environment, thus enhancing communication, collaboration, and decision-making capabilities. By harnessing advanced technologies, such as reconfigurable intelligent surfaces (RISs) and multiaccess edge computing (MEC), seamless interaction between physical and virtual entities is facilitated. In this article, we propose a cybertwin-driven edge computing framework that leverages RIS technology, complemented by an efficient computing offloading strategy to support large-scale Internet of Everything (IoE) applications. Specifically, the proposed strategy focuses on a multicell system where numerous randomly distributed end users have the option to offload delay-sensitive and computing-intensive tasks to edge computing nodes. The offloading channels are enhanced by RISs through passive beamforming, while cybertwin technology directs resource cooperation among multicells and allocates computing and communication resources. Our main objective is to optimize the system's utility with respect to task completion latency and energy consumption reduction. To achieve this goal, we conduct the joint optimization of task offloading and resource allocation. Furthermore, we develop a joint task offloading and resource allocation (JTORA) algorithm to derive optimal solutions for passive beamforming design, computing offloading decisions, communication resource scheduling, and computing capacity allocation. The simulation results demonstrate the superiority of the proposed algorithm over benchmark schemes in terms of edge computing efficiency. Furthermore, the system utility can be further enhanced by increasing the number of embedded RIS elements.

关键词： Task analysis Resource management Edge computing Servers Optimization Computational modeling Internet of Things computing offloading cybertwin edge computing reconfigurable intelligent surface (RIS) resource allocation (RA)

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computing offloading AND RESOURCE ALLOCATION ALGORITHM BASED ON GAME THEORY FOR IOT DEVICES IN MOBILE EDGE computing

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INTERNATIONAL JOURNAL OF INNOVATIVE computing INFORMATION AND CONTROL 2020年第6期16卷 1895-1914页

作者： Xu, Jianqiang Hu, Zhujiao Zou, Junzhong East China Univ Sci & Technol Sch Informat Sci & Engn 130 Meilong Rd Shanghai 200237 Peoples R China Fudan Univ Sch Microelect 825 Zhangheng Rd Shanghai 201203 Peoples R China

With the explosive growth of Internet of Things (IoT) devices and the popularization of intelligent terminals, the computing resources of various new applications are intensive, and existing work offloads them to mobile edge centers for computing. However, due to limitations in the coverage and resource capabilities of edge devices, there is still a problem of unbalanced load in the network. Therefore, in order to balance the load reasonably, this paper proposes a computing offloading and resource allocation algorithm for IoT devices using game theory in Mobile Edge computing (MEC). Firstly, a multi-user multi-MEC computing offloading and resource allocation model is designed, which manages a large number of devices by multiple base stations and multiple MEC partitions, and models the system including computing model and task model. Then, a prediction method of MEC server waiting time is proposed according to the task queue in base stations. The task processing sequence, time and frequency are arranged to improve the calculation efficiency of this algorithm, which makes rational use of computing resources for devices and MEC. Finally, the problem of multi-user and multi-MEC computing offloading and resource allocation is transformed into a multi-user game problem, and it is concluded that there is always a Nash equilibrium in this game problem. Furthermore, the optimal solution of this problem, that is, the optimal device computing offloading and resource allocation scheme, is obtained when the system is in Nash equilibrium state. The simulation results based on MATLAB platform show that our proposed algorithm is effective compared with other algorithms. It can converge reliably and has obvious optimization in energy consumption and time delay;besides the performance of this algorithm is improved.

关键词： Mobile edge computing Game theory computing offloading Resource allocation IoT device Nash equilibrium

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Energy-Efficient Multimedia Task Assignment and computing offloading for Mobile Edge computing Networks

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IEEE ACCESS 2020年 8卷 36702-36713页

作者： Sun, Yang Wei, Tingting Li, Huixin Zhang, Yanhua Wu, Wenjun Beijing Univ Technol Fac Informat Technol Beijing 100124 Peoples R China

With the rapid development of 5G technology in recent years, multimedia communication services, such as live online and short video, have received wide attention and become the important means of people's daily social intercourse. However, the rapid growth of multimedia communication demands pose greater challenges to both the wireless network communication capacity and the network processing capacity. Mobile Edge computing (MEC) is widely regarded as a promising technology to cope with the above challenges. To satisfy the growing demands and improve the quality of experience for users, it is in urge need to seek the effective and efficient task assignment and computing offloading strategy for MEC networks. In this paper, we focused on the multimedia services which need to be processed, uploaded and shared in the network and research the long-term task assignment and resource coordination problem. We formulate the optimization problem as a stochastic optimization problem with the aim of the minimizing the time-average energy consumption of the system. By using the Lyapunov optimization technique, we decompose the original problem into several subproblems which can be solved with current system information and low computational complexities. On this basis, we propose an online energy-efficient task assignment and computing offloading strategy to adaptively decide the task assignment, coordinate and optimize the wireless and computation resource allocation by taking the dynamic wireless condition and service delay constraints into consideration. Extensive simulation results show that our proposed algorithm can achieve considerate energy consumption and delay performances under different conditions.

关键词： Multimedia transmission task assignment computing offloading Lyapunov optimization delay constraints

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Joint Optimization of computing offloading and Service Caching in Edge computing-Based Smart Grid

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IEEE TRANSACTIONS ON CLOUD computing 2023年第2期11卷 1122-1132页

作者： Zhou, Huan Zhang, Zhenyu Li, Dawei Su, Zhou China Three Gorges Univ Coll Comp & Informat Technol Hubei Key Lab Intelligent Vis Based Monitoring Hy Yichang 443002 Peoples R China Montclair State Univ Dept Comp Sci Montclair NJ 07041 USA Xi An Jiao Tong Univ Sch Cyber Sci & Engn Xian 710000 Peoples R China

With the continuous expansion of the power Internet of Things (IoT) and the rapid increase in the number of Smart Devices (SDs), the data generated by SDs has exponentially increased. The traditional cloud-based smart grid cannot meet the low latency and high reliability requirements of emerging applications. By moving computing, data, and services from the centralized cloud to Edge Servers (ESs), edge computing exhibits excellent performance in communication delay and traffic reduction. Simultaneously, service caching also shows attractive advantages in handling the surge in data traffic. In this paper, we consider the joint optimization of computing offloading and service caching in edge computing-based smart grid, and formulate the problem as a Mixed-Integer Non-Linear Program (MINLP), aiming to minimize the task cost of the system. The original problem is decomposed into an equivalent master problem and sub-problem, and a Collaborative computing offloading and Resource Allocation Method (CCORAM) is proposed to solve the optimization problem, which includes two low-complexity algorithms. Specifically, a gradient descent allocation algorithm is first proposed to determine the computing resource allocation strategy, and then a game theory-based algorithm is proposed to determine the computing strategy. Simulation results show that CCORAM with low time complexity is very close to the optimal method, and performs much better than other benchmark methods.

关键词： computing offloading edge computing game theory service caching smart grid

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Opportunistic computing offloading in edge clouds

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JOURNAL OF PARALLEL AND DISTRIBUTED computing 2019年 123卷 69-76页

作者： Li, Wei You, Xinghui Jiang, Yingying Yang, Jun Hu, Long Huazhong Univ Sci & Technol Sch Comp Sci & Technol Wuhan 430074 Hubei Peoples R China

Nowadays, the advanced mobile devices provide considerable computation capacity. However, due to the instinct limitation of resources, mobile devices have to offload computation by Mobile Cloud computing (MCC) and Ad-Hoc Cloudlet for improving the performance and prolonging the battery life. However, efficient model for ad-hoc cloudlet-assisted computation offloading is remaining open issues. In this article, we provide an overview of existing computation offloading modes, e.g. remote cloud service mode and ad-hoc cloudlet-assisted service mode, and propose an opportunistic computation offloading (OPPOCO) to enable a more energy-efficient and intelligent strategy for computation offloading. Moreover, the simulation by OPNET verifies that our proposal is available and practical to improve mobile users' Quality of Service (QoS) and Quality of Experience (QoE). (C) 2018 Published by Elsevier Inc.

关键词： Edge clouds computing offloading IoT

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A reinforcement learning-based computing offloading and resource allocation scheme in F-RAN

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EURASIP JOURNAL ON ADVANCES IN SIGNAL PROCESSING 2021年第1期2021卷 1-25页

作者： Jiang, Fan Ma, Rongxin Gao, Youjun Gu, Zesheng Xian Univ Posts & Telecommun Shaanxi Key Lab Informat Commun Network & Secur Xian Peoples R China China Mobile Syst Integrat Co Ltd Beijing Peoples R China

This paper investigates a computing offloading policy and the allocation of computational resource for multiple user equipments (UEs) in device-to-device (D2D)-aided fog radio access networks (F-RANs). Concerning the dynamically changing wireless environment where the channel state information (CSI) is difficult to predict and know exactly, we formulate the problem of task offloading and resource optimization as a mixed-integer nonlinear programming problem to maximize the total utility of all UEs. Concerning the non-convex property of the formulated problem, we decouple the original problem into two phases to solve. Firstly, a centralized deep reinforcement learning (DRL) algorithm called dueling deep Q-network (DDQN) is utilized to obtain the most suitable offloading mode for each UE. Particularly, to reduce the complexity of the proposed offloading scheme-based DDQN algorithm, a pre-processing procedure is adopted. Then, a distributed deep Q-network (DQN) algorithm based on the training result of the DDQN algorithm is further proposed to allocate the appropriate computational resource for each UE. Combining these two phases, the optimal offloading policy and resource allocation for each UE are finally achieved. Simulation results demonstrate the performance gains of the proposed scheme compared with other existing baseline schemes.

关键词： Fog radio access networks computing offloading Resource allocation Deep reinforcement learning Dueling deep Q-network Deep Q-network

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