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UAV-Assisted Heterogeneous Multi-Server computation offloading With Enhanced Deep Reinforcement Learning in Vehicular Networks

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IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING 2024年第6期11卷 5323-5335页

作者： Song, Xiaoqin Zhang, Wenjing Lei, Lei Zhang, Xinting Zhang, Lijuan Nanjing Univ Aeronaut & Astronaut Coll Elect & Informat Engn Nanjing 210016 Peoples R China Nanjing Univ Posts & Telecommun Key Lab Broadband Wireless Commun & Sensor Network Minist Educ Nanjing 210003 Peoples R China

With the development of intelligent transportation systems (ITS), computation-intensive and latency-sensitive applications are flourishing, posing significant challenges to resource-constrained task vehicles (TVEs). Multi-access edge computing (MEC) is recognized as a paradigm that addresses these issues by deploying hybrid servers at the edge and seamlessly integrating computing capabilities. Additionally, flexible unmanned aerial vehicles (UAVs) serve as relays to overcome the problem of non-line-of-sight (NLoS) propagation in vehicle-to-vehicle (V2V) communications. In this paper, we propose a UAV-assisted heterogeneous multi-server computation offloading (HMSCO) scheme. Specifically, our optimization objective to minimize the cost, measured by a weighted sum of delay and energy consumption, under the constraints of reliability requirements, tolerable delay, and computing resource limits, among others. Since the problem is non-convex, it is further decomposed into two sub-problems. First, a game-based binary offloading decision (BOD) is employed to determine whether to offload based on the parameters of computing tasks and networks. Then, a multi-agent enhanced dueling double deep Q-network (ED3QN) with centralized training and distributed execution is introduced to optimize server offloading decision and resource allocation. Simulation results demonstrate the good convergence and robustness of the proposed algorithm in a highly dynamic vehicular environment.

关键词： Servers Task analysis Delays TV Autonomous aerial vehicles Vehicle dynamics Costs computation offloading deep reinforcement learning Internet of Vehicles multi-access edge computing (MEC) resource allocation

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Energy-efficient UAV-enabled computation offloading for industrial internet of things: a deep reinforcement learning approach

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WIRELESS NETWORKS 2024年第5期30卷 3921-3934页

作者： Shi, Shuo Wang, Meng Gu, Shushi Zheng, Zhong Harbin Inst Technol Sch Elect & Informat Engn Harbin 150001 Peoples R China Harbin Inst Technol Shenzhen Sch Elect & Informat Engn Shenzhen 518055 Peoples R China Peng Cheng Lab Shenzhen 518055 Peoples R China Int Innovat Inst HIT Huizhou Huizhou 516000 Guangdong Peoples R China

Industrial Internet of Things (IIoT) has been envisioned as a killer application of 5G and beyond. However, due to the shortness of computation ablility and batery capacity, it is challenging for IIoT devices to process latency-sensitive and resource-sensitive tasks. Moblie Edge Computing (MEC), as a promising paradigm for handling tasks with high quality of service (QoS) requirement and for energy-constrained IIoT devices, allows IIoT devices to offload their tasks to MEC servers, which can significantly reduce the task process delay and energy consumptions. However, the deployment of the MEC servers rely heavily on communication infrastructure, which greatly reduce the flexibility. Toward this end, in this paper, we consider multiple Unmanned Aerial Vehicles (UAV) eqqipped with transceivers as aerial MEC servers to provide IIoT devices computation offloading opportunities due to their high controbility. IIoT devices can choose to offload the tasks to UAVs through air-ground links, or to offload the tasks to the remote cloud center through ground cellular network, or to process the tasks locally. We formulate the multi-UAV-Enabled computation offloading problem as a mixed integer non-linear programming (MINLP) problem and prove its NP-hardness. To obtain the energy-efficient and low complexity solution, we propose an intelligent computation offloading algorithm called multi-agent deep Q-learning with stochastic prioritized replay (MDSPR). Numerical results show that the proposed MDSPR converges fast and outperforms the benchmark algorithms, including random method, deep Q-learning method and double deep Q-learning method in terms of energy efficiency and task successful rate.

关键词： Energy efficiency Deep reinforcement learning computation offloading Mobile edge computing Unmanned aerial vehicles

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Multi-Time-Scale Markov Decision Process for Joint Service Placement, Network Selection, and computation offloading in Aerial IoV Scenarios

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IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING 2024年第6期11卷 5364-5379页

作者： Shinde, Swapnil Sadashiv Tarchi, Daniele Univ Bologna Dept Elect Elect & Informat Engn Guglielmo Marconi I-40136 Bologna Italy Univ Bologna Res Unit CNIT I-40136 Bologna Italy Univ Florence Dept Informat Engn I-50139 Florence Italy

Vehicular Edge Computing (VEC) is considered a major enabler for multi-service vehicular 6G scenarios. However, limited computation, communication, and storage resources of terrestrial edge servers are becoming a bottleneck and hindering the performance of VEC-enabled Vehicular Networks (VNs). Aerial platforms are considered a viable solution allowing for extended coverage and expanding available resources. However, in such a dynamic scenario, it is important to perform a proper service placement based on the users' demands. Furthermore, with limited computing and communication resources, proper user-server assignments and offloading strategies need to be adopted. Considering their different time scales, a multi-time-scale optimization process is proposed here to address the joint service placement, network selection, and computation offloading problem effectively. With this scope in mind, we propose a multi-time-scale Markov Decision Process (MDP) based Reinforcement Learning (RL) to solve this problem and improve the latency and energy performance of VEC-enabled VNs. Given the complex nature of the joint optimization process, an advanced deep Q-learning method is considered. Comparison with various benchmark methods shows an overall improvement in latency and energy performance in different VN scenarios.

关键词： Optimization Vehicle dynamics Satellites Task analysis Games Markov decision processes Internet of Things Vehicular Edge Computing Aerial Networks Service Placement Network Selection computation offloading Markov Decision Processes Multi-time Scale Optimization

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Fuzzy Logic Based Binary computation offloading Scheme in V2X Communication Networks

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IEEE ACCESS 2024年 12卷 45507-45518页

作者： Men, Rui Fan, Xiumei Shan, Axida Yuan, Gang Xian Univ Technol Dept Automat & Informat Engn Xian 710048 Peoples R China Longdong Univ Sch Math & Informat Engn Qingyang 745000 Peoples R China Baotou Teachers Coll Sch Informat Sci & Technol Baotou 014030 Peoples R China

With the recent development of intelligent transportation systems, vehicles are getting more and more powerful and involving huge number of real-time applications including computation-intensive and delay-sensitive applications in vehicle-to-vehicle communication networks, which are challenging to be processed by an individual vehicle, especially for a legacy one. These resource-consuming computation tasks can be offloaded to another vehicle with idle computing resources for processing. However, it is difficult to select an optimal resource vehicle for each computational task in a dynamic vehicular network where the wireless communication links are lossy and the network topology is time-varying due to the high mobility of vehicles. In this work, we investigate the resource vehicle selection problem, which determines one of vehicles as the most suitable resource vehicle for the current computational task. To this end, a novel fuzzy logic based binary offloading scheme is proposed in vehicle-to-everything (V2X) communication networks. The proposed scheme evaluates the vehicles within the proximity by jointly considering the computational power, mobility, and link quality between neighboring vehicles based on fuzzy logic to ensure a high task completion rate for binary offloading applications. In order to validate the performance of the proposed offloading scheme, we conducted extensively computer simulations with the real traffic network in Xi'an city in China. As the results show, the offloading scheme expeditiously makes optimal decision in various environments, and outperforms other baseline proposals in terms of average task completion time and task completion rate. The average task completion time is reduced by 37.5%, whereas the best reduction of other baselines is 21.1% in our simulations.

关键词： computation offloading edge computing fuzzy logic V2X communications

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Federated deep reinforcement learning based computation offloading in a low Earth orbit satellite edge computing system

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FRONTIERS OF INFORMATION TECHNOLOGY & ELECTRONIC ENGINEERING 2024年 1-11页

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

Recent studies have shown that system capacity is very important for cellular networks. In this paper, we consider maximizing the weighted sum-rate of the cellular network downlink and uplink, where each cell consists of a full-duplex (FD) base station (BS) and half-duplex (HD) users. Federated learning (FL) can train models in the absence of centralized data, which can achieve privacy protection of user data. A low Earth orbit (LEO) satellite edge computing system (LSECS) can be formed by placing the mobile edge computing (MEC) servers on LEO satellites, which greatly increases the processing capacities of the satellites. Therefore, we consider a combination of FL and MEC and propose an FL-based computation offloading algorithm to maximize the weighted sum-rate while ensuring the security of user data. We consider solving the sub-channel assignment and power allocation problems using deep reinforcement learning (DRL) algorithms with excellent global search capabilities. The simulation results show that our proposed algorithm achieves the maximum weighted sum-rate compared with the baseline algorithms and excellent convergence.

关键词： Federated learning Low Earth orbit satellite Mobile edge computing Deep reinforcement learning computation offloading TN929.5

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Cost-Aware computation offloading and Resource Allocation in Ultra-Dense Multi-Cell, Multi-User and Multi-Task MEC Networks

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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2024年第5期73卷 6642-6657页

作者： Zhou, Tianqing Zeng, Xinliang Qin, Dong Jiang, Nan Nie, Xuefang Li, Chunguo East China Jiaotong Univ Sch Informat Engn Nanchang 330013 Peoples R China Nanchang Univ Sch Informat Engn Nanchang 330031 Peoples R China Southeast Univ Sch Informat Sci & Engn Nanjing Peoples R China

When many users offload computation tasks to edge servers in mobile edge computing (MEC) networks, how to economically utilize edge nodes with limited computation resources to ensure users' quality of experiences (QoEs) is a vital issue, especially for an ultra-dense multi-cell, multi-user, and multi-task framework. To minimize the task processing cost of all users, we try jointly optimizing computational offloading decisions, task offloading proportion, and allocation of communication and computation resources in this paper. Herein, the task processing cost is mainly caused by local energy consumption, wireless communication, and edge computing. Although the proposed optimization problem is non-convex, we design an efficient algorithm to find its optimum solution. Specifically, by taking advantage of both the improved artificial fish swarm algorithm (IAFSA) and the improved particle swarm optimization (IPSO), a hierarchical algorithm used for computation offloading (HACO) is designed. Then, the convergence, complexity, and parallel implementation of such an algorithm are analyzed. Finally, compared with other existing algorithms through simulation experiments, it is verified that the designed algorithm may achieve better performance in reducing the task processing cost under strict delay constraints.

关键词： Task analysis Costs Resource management Servers Delays Ultra-dense networks Heuristic algorithms mobile edge computing (MEC) multi-task computation offloading task processing cost

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A centralized delay-sensitive hierarchical computation offloading in fog radio access networks

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JOURNAL OF SUPERCOMPUTING 2024年第19期80卷 26831-26864页

作者： Taheri, Samira Moghim, Neda Movahhedinia, Naser Shetty, Sachin Univ Isfahan Dept Comp Engn Esfahan Iran Old Dominion Univ Virginia Modeling Anal & Simulat Ctr Dept Computat Modeling & Simulat Engn Suffolk VA 23435 USA

MEC (Multi-access Edge Computing) is vital in 5G and beyond (B5G) for reducing latency and enhancing network efficiency through local processing, crucial for real-time applications and improved security. This drives the adoption of advanced architectures like Fog Radio Access Network (F-RAN), which uses distributed resources from Radio Resource Heads (RRHs) or fog nodes to enable parallel computation. Each user equipment (UE) task can be processed by RRHs, fog access points, cloud servers, or the UE itself, depending on resource capacities. We propose MoNoR, a centralized approach for optimal task processing in F-RAN. MoNoR optimizes the selection of offloading modes, assignment of tasks to computation nodes, and allocation of radio resources using global network information. Given the computational complexity of this endeavor, we employ an evolutionary optimization technique rooted in Genetic Algorithms to address the problem efficiently. Simulations show MoNoR's superiority in minimizing latency over previous F-RAN offloading strategies.

关键词： computation offloading Cloud computing Fog computing computation-intensive applications Fog radio access networks

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Primal-Dual-Based computation offloading Method for Energy-Aware Cloud-Edge Collaboration

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IEEE TRANSACTIONS ON MOBILE COMPUTING 2024年第2期23卷 1534-1549页

作者： Su, Qian Zhang, Qinghui Li, Weidong Zhang, Xuejie Yunnan Univ Sch Informat Sci & Engn Kunming 650500 Yunnan Peoples R China Yunnan Univ Dept Math Kunming 650500 Yunnan Peoples R China

In the context of the Internet of Things (IoT), resource-constrained mobile edge computing (MEC) can no longer fully meet the needs of the rapidly growing number of mobile users;hence, cloud-edge collaborative computing has been developed. This paper focuses on the total energy consumption of the system and heterogeneity of scenarios, and a collaborative cloud-edge computation offloading approach with near real-time decision making is proposed. First, a general cloud-edge collaborative computation offloading model is abstracted from typical applications, and the energy consumption for edge and cloud offloading is calculated separately by considering both transmission and computational energy consumption. The problem is formulated as an integer linear program (ILP) with multidimensional resource constraints and is proven to be NP-hard. Then, a novel primal-dual computation offloading (PDCO) algorithm is designed to make near real-time offloading decisions one by one based on the sequential arrival of task requests. The approximation ratio of PDCO is derived through the weak duality property and the price-resource increment relationship. The experimental results show that under the guidance of total cost influenced by marginal prices, PDCO not only avoids blindly making offloading decisions but also effectively alleviates the shortage of resources on edge servers (ESs), approaching the optimal performance in terms of total energy consumption and resource utilization.

关键词： Task analysis Cloud computing Energy consumption Collaboration computational modeling Real-time systems Resource management Cloud-Edge collaboration computation offloading energy-aware primal-dual near real-time

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Energy Oriented Three-Tier computation offloading Scheme in Maritime Edge Computing Network

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IEEE Transactions on Vehicular Technology 2025年第5期74卷 8126-8140页

作者： Zhang, Hongxia Xi, Shiyu Shang, Bodong Zhang, Peiying Wu, Sheng Jiang, Chunxiao Qingdao Institute of Software College of Computer Science and Technology Qingdao266580 China Eastern Institute of Technology Eastern Institute for Advanced Study Ningbo315200 China Beijing University of Posts and Telecommunications School of Information and Communication Engineering Beijing100876 China Tsinghua University Beijing National Research Center for Information Science and Technology Beijing100084 China

With the development of maritime continuing to grow, large-scale maritime wireless devices (MWDs) are being deployed for various maritime applications. The rapid development of maritime Internet of Things (IoT) and growing demand for these applications have led to an increasing number of computation-intensive and resource-sensitive tasks. Considering the limited computing capacity and energy of MWDs, it is challenging to transmit and process massive maritime computing tasks in an energy efficient manner. In this paper, we propose a three-tier maritime edge computing system in which low earth orbit (LEO) satellite and offshore base station (OBS) support to provide communication and computing services for MWDs. The system optimizes association variable, task partitioning, transmission power, and computing resource allocation to reduce system energy consumption under latency constraints. The optimization problem is formulated as a mixed integer non-linear programming (MINLP) optimization problem. We decompose the original problem into four subproblems and design algorithms to solve them. Firstly, the association variable optimization subproblem is solved by slack variable method. Then, we adopt quadratic transformation and design a difference of convex algorithm to solve transmission power optimization subproblem. Further, task partitioning subproblem is solved by obtaining upper and lower bounds of offloading task size by strict deduction, and then solved by the standard convex method. Finally, Lagrangian dual method is utilized to deal with joint computing resource optimization subproblem of LEO satellite and OBS. In simulation results, the proposed algorithm saves 39.3% of system energy consumption compared to benchmark schemes. © 1967-2012 IEEE.

关键词： computation offloading

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Network Slicing-Enabled computation offloading in Satellite-Terrestrial Edge Computing Networks: A Bi-Level Game Approach

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IEEE Internet of Things Journal 2025年第11期12卷 16282-16297页

作者： Wei, Fengsheng Wang, Yatong Feng, Gang Qin, Shuang Huzhou313001 China Beijing Institute of Technology China University of Electronic Science and Technology of China National Key Laboratory of Science and Technology on Communications Chengdu611731 China

Satellite-terrestrial edge computing network (STECN) is emerging as a novel computation paradigm that enables a fashion of on-orbit computation, accommodating real-time processing of various types of computation tasks within a wider area. However, STECNs are incapable of meeting the diverse quality-of-service (QoS) requirements of various computational tasks without deploying dedicated infrastructures tailored to each type of task. Therefore, We studied the problem of joint network slicing and task offloading, which is modeled as a problem with a two-layer structure. At the higher level, slice tenants optimize their profits by determining resource allocations from the STECN, while at the lower layer, the users within each network slice maximize their utilities by deciding their offloading policies. In light of the complexity and the intricate interplay of these two problems, a multi-leader-disjoint-follower bi-level game is proposed. We show that both the leader's game and the followers' game admit at least one Nash Equilibrium (NE). We then proposed two distributed algorithms that could prove to converge to the NE of these two games, respectively, without revealing any private information of all stakeholders. We evaluate the performance of our algorithms through extensive simulations, and the results demonstrate that our algorithms converge to the NE rapidly and can achieve superior performance gains compared with some known benchmarks. © 2014 IEEE.

关键词： computation offloading

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