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

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 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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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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A Two-Stage Hybrid Multi-Objective Optimization Evolutionary Algorithm for computing offloading in Sustainable Edge computing

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IEEE TRANSACTIONS ON CONSUMER ELECTRONICS 2024年第1期70卷 735-746页

作者： Li, Lingjie Qiu, Qijie Xiao, Zhijiao Lin, Qiuzhen Gu, Jiongjiong Ming, Zhong Shenzhen Univ Guangdong Lab Artificial Intelligence & Digital Ec Shenzhen 518060 Peoples R China Shenzhen Univ Coll Comp Sci & Software Engn Shenzhen 518060 Peoples R China Huawei Cloud Comp Technol Co Ltd Cloud Prod Serv Dept Shenzhen 518129 Peoples R China Shenzhen Technol Univ Coll Big Data & Internet Shenzhen 518118 Peoples R China

Edge computing is an effective complementary technology to cloud computing, allowing end devices to offload tasks onto edge base stations (BSs) to satisfy the quality of experience of consumers. Due to the limitation of storage and computing resources, a single BS cannot satisfy the heavy computing tasks. In this regard, multi-BS collaboration is an effective way to alleviate this issue. Moreover, service caching and cloud-edge collaboration computing also show attractive advantages in handling the surging data traffic. However, to the best of our knowledge, there is rarely work that consider all of the aforementioned scenarios simultaneously. To fill this research gap, this paper comprehensively considers the computing offloading problem in sustainable edge computing based on the above scenarios. Specifically, the computing offloading problem is first modeled as a multi-objective optimization problem with the purpose of minimizing the delay and energy consumption. Then, a two-stage hybrid multi-objective optimization evolutionary algorithm, called TH-MOEA, is designed to address the above formulated problem, which uses a novel competitive swarm optimizer to accelerate convergence in the early evolutionary stage and adopts a diversity-enhanced immune algorithm to improve diversity in the later evolutionary stage. Simulation results show that TH-MOEA outperforms several state-of-the-art peer methods.

关键词： Cloud computing Task analysis Optimization Edge computing Delays Collaboration Energy consumption computing offloading multiobjective optimization evolutionary algorithm

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Energy-Stabilized computing offloading Algorithm for UAVs With Energy Harvesting

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IEEE INTERNET OF THINGS JOURNAL 2024年第4期11卷 6020-6031页

作者： Zeng, Kai Li, Xin Shen, Tao Kunming Univ Sci & Technol China Fac Informat Engn & Automat Yunnan Key Lab Comp Technol Applicat Kunming 066000 Peoples R China

Recent research on unmanned aerial vehicle-based (UAV) computational offloading algorithms has employed energy harvesting mechanisms to improve the efficiency of edge computing. However, these approaches treat UAVs as relays or power providers which are rarely regarded as the computational nodes in the energy harvesting condition. The most challenging issue is not only the computational efficiency, but also the energy stability of individual computing devices. Stable energy state represents a stable computing service capability, which is very important for edge systems. It depends heavily on proper computational offloading algorithms. In this study, we construct a novel model that uses a cluster of UAVs with energy harvesting capability as a computational core. It is capable of providing long-term computational services for various scenarios. Then, we construct a Lyapunov function through a designed virtual battery energy queue and prove the existence of an upper bound for the Lyapunov drift-plus-penalty function through mathematical transformations. Therefore, we obtain a theoretically stable battery energy queue and design a Lyapunov-chain offloading algorithm based on it. Simulation results show that the proposed Lyapunov-chain offloading algorithm is able to maintain the strong energy stability of each node. It also provides robustness for edge UAV clusters while minimizing the execution delay compared to the baseline offloading scheme.

关键词： computing offloading edge computing energy harvesting Lyapunov unmanned aerial vehicle based (UAV)

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DRL-based computing offloading approach for large-scale heterogeneous tasks in mobile edge computing

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CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE 2024年第19期36卷 e8156-e8156页

作者： He, Bingkun Li, Haokun Chen, Tong Wuhan Univ Sch Cyber Sci & Engn Wuhan 430081 Peoples R China Tongji Univ Coll Mech Engn Shanghai Peoples R China

In the last few years, the rapid advancement of the Internet of Things (IoT) and the widespread adoption of smart cities have posed new challenges to computing services. Traditional cloud computing models fail to fulfil the rapid response requirement of latency-sensitive applications, while mobile edge computing (MEC) improves service efficiency and customer experience by transferring computing tasks to servers located at the network edge. However, designing an effective computing offloading strategy in complex scenarios involving multiple computing tasks, nodes, and services remains a pressing issue. In this paper, a computing offloading approach based on Deep Reinforcement Learning (DRL) is proposed for large-scale heterogeneous computing tasks. First, Markov Decision Processes (MDPs) is used to formulate computing offloading decision and resource allocation problems in large-scale heterogeneous MEC systems. Subsequently, a comprehensive framework comprising the "end-edge-cloud" along with the corresponding time-overhead and resource allocation models is constructed. Finally, through extensive experiments on real datasets, the proposed approach is demonstrated to outperform existing methods in enhancing service response speed, reducing latency, balancing server loads, and saving energy.

关键词： computing offloading deep reinforcement learning internet of things mobile edge computing

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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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Privacy-preserving edge computing offloading scheme based on whale optimization algorithm

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JOURNAL OF SUPERcomputing 2023年第3期79卷 3005-3023页

作者： Liu, Zhenpeng Wang, Jingyi Gao, Zilin Wei, Jianhang Hebei Univ Sch Elect Informat Engn Baoding 071002 Peoples R China Hebei Univ Informat Technol Ctr Baoding 071002 Peoples R China Xinjiang Univ Sci & Technol Network & Expt Management Ctr Korla 841000 Peoples R China

Aiming at the problem of user's task offloading in mobile edge computing and the potential leakage of location privacy during the offloading process, a privacy-preserving computing offloading scheme based on whale optimization algorithm is proposed. Using differential privacy technology to obfuscate the user's location information, the user can make task offloading decisions according to the obfuscated distance. Considering the delay, energy consumption, and their weighted sum, the offloading problem is modeled as a convex optimization problem. Then, the whale optimization algorithm is adopted to solve this optimization problem to achieve a balance between privacy protection and resource consumption. Experiments are conducted to verify the relationship between the degree of privacy leakage, the computation-offloading cost and real distance, privacy-preserving impact factor, the respective weights of time delay and energy consumption The experimental results show that the offloading scheme proposed in this paper has good performance in terms of cost and privacy protection.

关键词： Edge computing computing offloading Differential privacy Whale optimization algorithm

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