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DDPG-Based Intelligent computation offloading and Resource Allocation for LEO Satellite Edge Computing Network

China Communications 2025年第3期22卷 1-15页

作者： Jia Min Wu Jian Zhang Liang Wang Xinyu Guo Qing School of Electronics and Information Engineering Harbin Institute of TechnologyHarbin 150000China

Low earth orbit(LEO)satellites with wide coverage can carry the mobile edge computing(MEC)servers with powerful computing capabilities to form the LEO satellite edge computing system,providing computing services for the global ground *** this paper,the computation offloading problem and resource allocation problem are formulated as a mixed integer nonlinear program(MINLP)*** paper proposes a computation offloading algorithm based on deep deterministic policy gradient(DDPG)to obtain the user offloading decisions and user uplink transmission *** paper uses the convex optimization algorithm based on Lagrange multiplier method to obtain the optimal MEC server resource allocation *** addition,the expression of suboptimal user local CPU cycles is derived by relaxation *** results show that the proposed algorithm can achieve excellent convergence effect,and the proposed algorithm significantly reduces the system utility values at considerable time cost compared with other algorithms.

关键词： computation offloading deep deterministic policy gradient low earth orbit satellite mobile edge computing resource allocation

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Review on Meta-heuristic Algorithm-Based Priority-Aware computation offloading in Edge Computing System

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Journal of The Institution of Engineers (India): Series B 2025年 1-26页

作者： Pilli, Neelima Mohapatra, Debasis Reddy, Shiva Shankar Department of Computer Science and Engineering Biju Patnaik University of Technology Odisha Rourkela India Department of Computer Science and Engineering Parala Maharaja Engineering College Odisha Brahmpur India Department of Computer Science and Engineering Sagi Rama Krishnam Raju Engineering College Andhra Pradesh Bhimavaram India

Advancements in smart applications highlight the need for increased processing and storage capacity at Smart Devices (SDs). To tackle this, Edge computing (EC) is enabled to offload SD workloads to distant edge servers. So, a variety of offloading strategies are offered in previous research to enhance the efficiency of SDs by offloading their workloads to neighboring cloudlets or distant cloud computing resources. Consequently, optimization techniques are crucial for scheduling computation offloading tasks in (ED) networks. To the greatest extent of current understanding, an extensive analysis of meta-heuristic-based computation offloading (CO) techniques in the EC has not been systematically surveyed. This systematic survey creates an inventory of CO in EC and further summarizes the most advanced recently available EC technologies. In this survey, three types of optimization techniques are examined: Lyapunov, Meta-heuristic, and Convex. The objective functions, application domains, offloading methods, evaluation techniques, merits, and demerits of the existing algorithms are summarized. The proposed survey undergoes four main CO processes: task scheduling, priority-aware task scheduling, load balancing, and edge server selection. In addition, this research provides a comparative analysis of the offloading techniques. Subsequently, unresolved problems and upcoming unfocused or inadequately covered research tasks are explored. © The Institution of Engineers (India) 2025.

关键词： computation offloading

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An energy-efficient distributed computation offloading algorithm for ground-air cooperative networks

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VEHICULAR COMMUNICATIONS 2025年 52卷

作者： Shao, Yanling Xu, Hairui Liu, Liming Dong, Wenyong Shan, Pingping Guo, Junying Xu, Wenxuan Nanyang Inst Technol Sch Comp & Software Nanyang 473000 Peoples R China Zhengzhou Univ Sch Comp & Artificial Intelligence Zhengzhou 450001 Peoples R China Wuhan Univ Sch Comp Sci Wuhan 430072 Peoples R China Xinjiang Univ Polit Sci & Law Sch Informat Network Secur Tumushuke 843900 Peoples R China

Due to the shortage of energy resources and computational capability, unmanned aerial vehicles (UAVs) tend to fail to execute tasks with time-delay sensitive and complex demands like artificial intelligence (AI) enabled applications. Most offloading method literature in ground-air cooperative systems simply uses edge servers or remote cloud servers to provide computation resources and storage space. Unfortunately, their performance degrades since it is difficult to guarantee UAV's quality of experience (QoE) considering the long-distance transmission delay. To address this issue, this paper proposes a ground-air cooperative edge computing framework in which multiprocessing computation is implemented by the UAVs locally or offloads specific calculations to the edge server on unmanned ground vehicles (UGVs). The proposed framework consists of two innovative mechanisms: one is to consider a mobility-aware link prediction method and other indicators, including compute capacity and workload, to ensure a stable offloading environment, the another is to propose an energy-efficient distributed computation offloading algorithm (EDCOA) by modelling the computation offloading issue for UAVs as an analytical optimization problem. By offloading subtasks to multiple UGV nodes for multiprocessing, UAVs can leverage the computation resources of the surrounding edge network entities to enhance their computational capabilities. Extensive experiments and comparisons with state-of-the-art realtime offloading methods showed that the proposed framework outperforms other approaches by delivering better performance in reducing UAV energy consumption, ensuring successful task offloading rates and meeting latency requirements.

关键词： UAV UGV Ground-air cooperative Edge computing computation offloading

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Joint computation offloading and Target Tracking in Integrated Sensing and Communication Enabled UAV Networks

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IEEE COMMUNICATIONS LETTERS 2024年第6期28卷 1327-1331页

作者： Chien, Trinh Van Cong, Mai Dinh Cong Luong, Nguyen Do, Tri Nhu Kim, Dong In Chatzinotas, Symeon Hanoi Univ Sci & Technol Sch Informat & Commun Technol Hanoi 100000 Vietnam Phenikaa Univ Fac Comp Sci Hanoi 12100 Vietnam Polytech Montreal Dept Elect Engn Montreal PQ H3T 1J4 Canada Sungkyunkwan Univ Dept Elect & Comp Engn Suwon 16419 South Korea Univ Luxembourg Interdisciplinary Ctr Secur Reliabil & Trust L-4365 Esch Sur Alzette Luxembourg

In this letter, we investigate a joint computation offloading and target tracking in Integrated Sensing and Communication (ISAC)-enabled unmanned aerial vehicle (UAV) network. Therein, the UAV has a computing task that is partially offloaded to the ground UE for execution. Meanwhile, the UAV uses the offloading bit sequence to estimate the velocity of a ground target based on an autocorrelation function. The performance of the velocity estimation that is represented by Cramer-Rao lower bound (CRB) depends on the length of the offloading bit sequence and the UAV's location. Thus, we jointly optimize the task size for offloading and the UAV's location to minimize the overall computation latency and the CRB of the mean square error for velocity estimation subject to the UAV's budget. The problem is non-convex, and we propose a genetic algorithm to solve it. Simulation results are provided to demonstrate the effectiveness of the proposed algorithm.

关键词： computation offloading autocorrelation target tracking integrated radar and communication UAV

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Deep Reinforcement Learning-Based Adaptive computation offloading and Power Allocation in Vehicular Edge Computing Networks

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2024年第10期25卷 13339-13349页

作者： Qiu, Bin Wang, Yunxiao Xiao, Hailin Zhang, Zhongshan Guilin Univ Technol Sch Comp Sci & Engn Guilin 541004 Peoples R China Guilin Univ Technol Guangxi Key Lab Embedded Technol & Intelligent Sys Guilin 541004 Peoples R China Hubei Univ Sch Comp Sci & Informat Engn Wuhan 430062 Peoples R China Beijing Inst Technol Sch Cyberspace Sci & Technol Beijing 100081 Peoples R China

As a novel paradigm, Vehicular Edge Computing (VEC) can effectively support computation-intensive or delay-sensitive applications in the Internet of Vehicles era. computation offloading and resource management strategies are key technologies that directly determine the system cost in VEC networks. However, due to vehicle mobility and stochastic arrival computation tasks, designing an optimal offloading and resource allocation policy is extremely challenging. To solve this issue, a deep reinforcement learning-based intelligent offloading and power allocation scheme is proposed for minimizing the total delay cost and energy consumption in dynamic heterogeneous VEC networks. Specifically, we first construct an end-edge-cloud offloading model in a bidirectional road scenario, taking into account stochastic task arrival, time-varying channel conditions, and vehicle mobility. With the objective of minimizing the long-term total cost composed of the energy consumption and task delay, the Markov Decision Process (MDP) can be employed to solve such optimization problems. Moreover, considering the high-dimensional continuity of the action space and the dynamics of task generation, we propose a deep deterministic policy gradient-based adaptive computation offloading and power allocation (DDPG-ACOPA) algorithm to solve the formulated MDP problem. Extensive simulation results demonstrate that the proposed DDPG-ACOPA algorithm performs better in the dynamic heterogeneous VEC environment, significantly outperforming the other four baseline schemes.

关键词： Vehicular edge computing power allocation computation offloading deep deterministic policy gradient (DDPG)

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Edge-Intelligence-Powered Joint computation offloading and Unmanned Aerial Vehicle Trajectory Optimization Strategy

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DRONES 2024年第9期8卷 485-485页

作者： Liu, Qian Qi, Zhi Wang, Sihong Liu, Qilie Chongqing Univ Posts & Telecommun Sch Commun & Informat Engn Chongqing 400065 Peoples R China Key Lab Mobile Commun Technol Chongqing 400065 Peoples R China Minist Educ Postdoctoral Res Workstat Engn Res Ctr Mobile Comm Chongqing 400065 Peoples R China

UAV-based air-ground integrated networks offer a significant benefit in terms of providing ubiquitous communications and computing services for Internet of Things (IoT) devices. With the empowerment of edge intelligence (EI) technology, they can efficiently deploy various intelligent IoT applications. However, the trajectory of UAVs can significantly affect the quality of service (QoS) and resource optimization decisions. Joint computation offloading and UAV trajectory optimization bring many challenges, including coupled decision variables, information uncertainty, and long-term queue delay constraints. Therefore, this paper introduces an air-ground integrated architecture with EI and proposes a TD3-based joint computation offloading and UAV trajectory optimization (TCOTO) algorithm. Specifically, we use the principle of the TD3 algorithm to transform the original problem into a cumulative reward maximization problem in deep reinforcement learning (DRL) to obtain the UAV trajectory and offloading strategy. Additionally, the Lyapunov framework is used to convert the original long-term optimization problem into a deterministic short-term time-slot problem to ensure the long-term stability of the UAV queue. Based on the simulation results, it can be concluded that our novel TD3-based algorithm effectively solves the joint computation offloading and UAV trajectory optimization problems. The proposed algorithm improves the performance of the system energy efficiency by 3.77%, 22.90%, and 67.62%, respectively, compared to the other three benchmark schemes.

关键词： edge intelligence computation offloading UAV trajectory Lyapunov optimization deep reinforcement learning

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Multitask computation offloading Based on Evolutionary Multiobjective Optimization in Industrial Internet of Things

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IEEE INTERNET OF THINGS JOURNAL 2024年第9期11卷 15894-15908页

作者： Chai, Zheng-Yi Zhao, Ying-Jie Li, Ya-Lun Tiangong Univ Sch Comp Sci & Technol Tianjin Key Lab Autonomous Intelligence Technol & Tianjin 300387 Peoples R China Tiangong Univ Sch Elect & Informat Engn Tianjin 300387 Peoples R China

The development of Industrial Internet of Things (IIoT) has completely changed traditional manufacturing industry. Industrial equipments with limited resources often cannot meet the diverse demands of numerous computing-intensive and latency-sensitive tasks. Mobile edge computing (MEC) offloads these tasks to nearby edge servers to achieve lower latency and energy consumption. However, considering the channel interferences of the network and the diverse demands of different tasks, coordinating computation offloading among multiple devices is challenging. To address this challenge, the computation offloading is formulated as a multiobjective optimization problem, and a new task model composed of scientific workflow tasks and concurrency workflow tasks is proposed to represent the multitask in the industrial environment. In addition, a two-hierarchical optimization framework is devised to optimize the bandwidth allocation and the multitask computation offloading through the dynamic bandwidth preallocation and the improved multiobjective evolutionary algorithm based on decomposition with two performance enhancing schemes. Comprehensive experiments demonstrate that the effectiveness and efficiency of our proposed framework in terms of the tradeoffs between latency and energy consumption, as well as the convergence and diversity of obtained nondominated solutions.

关键词： Task analysis Energy consumption Optimization Industrial Internet of Things Servers computational modeling Evolutionary computation computation offloading Industrial Internet of Things (IIoT) mobile edge computing (MEC) multiobjective evolutionary algorithm multitask offloading

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Multiuser computation offloading for Long-Term Sequential Tasks in Mobile Edge Computing Environments

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Tsinghua Science and Technology 2023年第1期28卷 93-104页

作者： Huanhuan Xu Jingya Zhou Wenqi Wei Baolei Cheng School of Computer Science and Technology Soochow UniversitySuzhou 215006China State Key Laboratory of Mathematical Engineering and Advanced Computing Wuxi 214125China Provincial Key Laboratory for Computer Information Processing Technology Soochow UniversitySuzhou 215006China School of Computer Science Georgia Institute of TechnologyAtlantaGA 30332USA

Mobile edge computing has shown its potential in serving emerging latency-sensitive mobile applications in ultra-dense 5G networks via offloading computation workloads from the remote cloud data center to the nearby network ***,current computation offloading studies in the heterogeneous edge environment face multifaceted challenges:Dependencies among computational tasks,resource competition among multiple users,and diverse long-term *** applications typically consist of several functionalities,and one huge category of the applications can be viewed as a series of sequential *** this study,we first proposed a novel multiuser computation offloading framework for long-term sequential ***,we presented a comprehensive analysis of the task offloading process in the framework and formally defined the multiuser sequential task offloading ***,we decoupled the long-term offloading problem into multiple single time slot offloading problems and proposed a novel adaptive method to solve *** further showed the substantial performance advantage of our proposed method on the basis of extensive experiments.

关键词： mobile edge computing sequential tasks computation offloading dependency

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Asynchronous Federated Deep Reinforcement Learning-Based URLLC-Aware computation offloading in Space-Assisted Vehicular Networks

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2023年第7期24卷 7377-7389页

作者： Pan, Chao Wang, Zhao Liao, Haijun Zhou, Zhenyu Wang, Xiaoyan Tariq, Muhammad Al-Otaibi, Sattam North China Elect Power Univ State Key Lab Alternate Elect Power Syst Renewabl Beijing 102206 Peoples R China Southeast Univ Natl Mobile Commun Res Lab Nanjing 211189 Peoples R China Ibaraki Univ Grad Sch Sci & Engn Ibaraki 3108512 Japan Natl Univ Comp & Emerging Sci Elect Engn Dept Islamabad 44000 Pakistan Taif Univ Coll Engn Innovat & Entrepreneurship Ctr At Taif 26571 Saudi Arabia

Space-assisted vehicular networks (SAVN) provide seamless coverage and on-demand data processing services for user vehicles (UVs). However, ultra-reliable and low-latency communication (URLLC) demands imposed by emerging vehicular applications are hard to be satisfied in SAVN by existing computation offloading techniques. Traditional deep reinforcement learning algorithms are unsuitable for highly dynamic SAVN due to the underutilization of environment observations. An AsynchronouS federaTed deep Q-learning (DQN)-basEd and URLLC-aware computation offloading algorithm (ASTEROID) is presented in this paper to achieve throughput maximization considering the long-term URLLC constraints. Specifically, we first establish an extreme value theory-based URLLC constraint model. Second, the task offloading and computation resource allocation are decomposed by employing Lyapunov optimization. Finally, an asynchronous federated DQN-based (AF-DQN) algorithm is presented to address the UV-side task offloading problem. The server-side computation resource allocation is settled by an queue backlog-aware algorithm. Simulation results verify that ASTEROID achieves superior throughput and URLLC performances.

关键词： Ultra reliable low latency communication Task analysis computational modeling Servers Optimization Heuristic algorithms Throughput Space-assisted vehicular networks (SAVN) ultra-reliable and low-latency communication awareness asynchronous federated deep reinforcement learning computation offloading

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Deep reinforcement learning based computation offloading and resource allocation strategy for maritime internet of things

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COMPUTER NETWORKS 2025年 264卷

作者： Xu, Yanli Yu, Qianlian Shanghai Maritime Univ Sch Informat Engn Shanghai 201306 Peoples R China

With the continuous growth of marine activities, the demands of computation-intensive applications dramatically increase. How to provide guaranteed computation service for maritime Internet of Things (M-IoT) is not well addressed. In this paper, we make use of space-air-ground-sea integrated network (SAGSIN) and mobile edge computing (MEC) to provide wide-coverage and efficient computation services for M-IoT users. The heterogeneous network resources of SAGSIN are fully used by unified management based on software-defined networking (SDN). In addition, we propose a joint computation offloading and resource allocation algorithm based on dueling double deep Q network (D3QN) with the consideration of heterogeneous SAGSIN architecture and scarce service resources of marine scenarios. The proposed algorithm can minimize delays and energy consumption by optimizing task offloading decisions and resource allocation of communication and computation. It achieves efficient resource utilization and ensures that the processing power is highly matched with the task requirements. The simulation results demonstrate that the proposed algorithm outperforms Deep Q Network (DQN), Double DQN, and Dueling DQN in terms of the overall system overhead. Furthermore, the proposed algorithm exhibits exceptional performance and robustness. It can ensure the increasing demand for computing services in Marine networks.

关键词： Maritime Internet of Things (M-IoT) Mobile edge computing (MEC) computation offloading Resource allocation Deep reinforcement learning

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