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collaborative computation offloading and wireless charging scheduling in multi-UAV-assisted MEC networks: A TD3-based approach

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COMPUTER NETWORKS 2024年 251卷

作者： Zhao, Liang Yao, Yujun Guo, Jianmeng Zuo, Qingjun Leung, Victor C. M. China Three Gorges Univ Key Lab Geol Hazards Three Gorges Reservoir Area Minist Educ Yichang 443002 Peoples R China China Three Gorges Univ Coll Comp & Informat Technol Hubei Key Lab Intelligent Vis Based Monitoring Hyd Yichang 443002 Peoples R China Univ British Columbia Dept Elect & Comp Engn Vancouver BC V6T 1Z4 Canada

computation offloading, resource allocation, and endurance issues in unmanned aerial vehicle (UAV)-aided mobile edge computing (MEC) networks have always been a research focus. UAV-aided MEC allows mobile users' (MUs') tasks to be offloaded to drones for processing in special scenarios, such as natural disasters or military attacks. However, as the number and size of offloaded tasks continuously increase, it is difficult for a single UAV to meet all computational demands, result in the decline of QoS. In order to address this issue, this paper presents a collaborative computation offloading scheme where multiple UAVs can cooperate to handle massive tasks. Firstly, considering that battery -limited UAVs cannot complete all tasks and sustain flight without recharging, we incorporate charging stations (CS) into multi-UAV-assisted MEC networks. Subsequently, we design a price -based incentive mechanism to encourage the adjacent UAVs with unused computation resources to cooperate with busy UAVs, so as to maximize the total revenue obtained from UAVs' collaborative computation. Then, we formulate the joint optimization problem of computation offloading, resource allocation and charging scheduling as a Markov Decision Process (MDP), and propose a Twin Delayed Deep Deterministic policy gradient (TD3) algorithm to find optimal strategies. Finally, extensive simulations demonstrate that the proposed TD3 algorithm outperforms other benchmark methods, achieving the maximum overall system utility under different scenarios.

关键词： collaborative computation offloading Resource allocation Wireless charging scheduling MEC UAV Deep reinforcement learning (DRL) TD3

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collaborative computation offloading for scheduling emergency tasks in SDN-based mobile edge computing networks

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COMPUTER NETWORKS 2024年 238卷

作者： Al-hammadi, Ikhlas Li, Mingchu Islam, Sardar M. N. Al-Mosharea, Esmail Dalian Univ Technol Sch Software Dalian 116620 Liaoning Peoples R China Victoria Univ Melbourne VIC Australia

Due to the growing demand for time-intensive tasks, several tasks coexist, some of which last for a long time or only occur once in a while, such as emergencies. Emergencies such as natural disasters, accidents or medical emergencies require immediate attention and often demand real-time execution. Such tasks are the most difficult to plan for and foresee. Planning, resource allocation, and clear communication between all involved parties and network layers are crucial for scheduling emergency tasks. In the extensive literature, when an emergency task triggers, it will be handled like any other regular task. If the deadline is missed, a catastrophe will ensue. They ignored the impact of insufficient mobile edge computing (MEC) capacity and network congestion, which might result in execution delays. collaborative offloading, which involves distributing computation tasks across multiple servers, shows promise in enhancing MEC network performance, particularly in emergency scenarios. In this context, efficient task scheduling plays a vital role in minimizing the total execution time of regular tasks while meeting the deadlines of emergency tasks. Our proposed scheme utilizes a collaborative offloading approach to schedule emergency tasks in MEC networks, leveraging the computing capacity of edge-deployed MEC servers. By utilizing the software-defined networking (SDN)'s global view of the network, task requests are collected and allocated to suitable MEC servers capable of meeting the demands. To address key challenges, our scheme propose four scheduling algorithms to address the following issues: (i) ensuring tasks are assigned to the nearest MEC server with sufficient computational resources, (ii) controlling a threshold to prevent network congestion, (iii) selecting an optimal collaborative MEC server for executing overloaded tasks based on collaborative offloading decisions, and (iv) allocating resources for emergency tasks when triggered to meet urgent deadlines by

关键词： Mobile edge computing Emergency tasks SDN collaborative computation offloading

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collaborative computation offloading for UAVs and USV Fleets in Communication Networks 18

Collaborative Computation Offloading for UAVs and USV Fleets...

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18th IEEE International Wireless Communications and Mobile Computing Conference (IWCMC)

作者： Zeng, Hui Li, Ruidong Su, Zhou Xu, Qichao Wang, Yuntao Dai, Minghui Luan, Tom H. Sun, Xin Liu, Donglan Shanghai Univ Sch Mech Engn & Automat Shanghai Peoples R China Kanazawa Univ Inst Sci & Engn Kanazawa Japan Xi An Jiao Tong Univ Sch Cyber Sci & Engn Xian Peoples R China Univ Macau Dept Comp & Informat Sci Macau Peoples R China Xidian Univ Sch Cyber Informat Engn Xian Peoples R China State Grid Zhejiang Elect Power Res Inst Hangzhou Zhejiang Peoples R China State Grid Shandong Elect Power Res Inst Shandong Peoples R China

ISBN: (纸本)9781665467490

Unmanned aerial vehicles (UAVs) empowered with artificial intelligence (AI) have become a new paradigm for marine monitoring and disaster rescue. In AI-enabled UAV applications, UAVs generate amounts of computation-intensive tasks (e.g., image recognition, video processing, and path planning, etc.) that cannot be locally executed by UAVs in time. How to offload the computation-intensive tasks of UAVs timely and effectively has become an urgent challenge. Multiple unmanned surface vehicles (USVs) integrated into a USV fleet is appealingly advocated to provide abundant computation resources for computation tasks. In this paper, we propose a collaborative computation offloading scheme with UAVs and USV fleets in maritime communication networks. Specifically, we first propose a collaborative computation offloading framework, where UAVs act as the requesters of computation offloading, and USV fleets are the assistants. Then, to minimize the overall execution time of computation tasks, UAVs determine the optimal ratio of computation tasks offloaded to USV fleets in the worst case. Afterwards, the first sealed reverse auction with reserve price is utilized to incentivize USV fleets to assist in executing computation tasks of UAVs, where the reserve price guarantees the satisfied benefits of UAVs. Simulation results demonstrate that the proposed scheme reduces the overall execution time and improves the expected revenue of the USV fleet as compared to conventional schemes.

关键词： Communication networks collaborative computation offloading UAV USV fleet auction game

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collaborative computation offloading and Resource Allocation in Satellite Edge Computing

Collaborative Computation Offloading and Resource Allocation...

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IEEE Global Communications Conference (GLOBECOM)

作者： Wang, Ruisong Zhu, Weichen Liu, Gongliang Ma, Ruofei Zhang, Di Mumtaz, Shahid Cherkaoui, Soumaya Harbin Inst Technol Sch Informat Sci & Engn Weihai Peoples R China Zhengzhou Univ Sch Informat Engn Zhengzhou Peoples R China Inst Telecomunicacoes Aveiro Portugal Univ Sherbrooke Dept Elect & Comp Engn Sherbrooke PQ Canada

ISBN: (纸本)9781665435406

In this paper, we investigate the collaborative computation offloading method in satellite edge computing by allowing computation tasks to be executed by multiple satellites with computing capacity. The main purpose is to optimize the resource allocation to minimize the energy consumption of the network, which is formulated as a non-convex optimization problem. To solve it efficiently, we first provide the optimal task allocation scheme and then divide the original optimization problem into two subproblems based on an alternative optimization method. Although two subproblems are still non-convex, we can apply successive convex approximation method to deal with them and design an iterative algorithm to solve them. Finally, simulation results demonstrate the superiority and effectiveness of our proposed algorithm.

关键词： collaborative computation offloading resource allocation edge computing satellite networks

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collaborative computation offloading and Resource Allocation in Multi-UAV-Assisted IoT Networks: A Deep Reinforcement Learning Approach

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IEEE INTERNET OF THINGS JOURNAL 2021年第15期8卷 12203-12218页

作者： Seid, Abegaz Mohammed Boateng, Gordon Owusu Anokye, Stephen Kwantwi, Thomas Sun, Guolin Liu, Guisong Univ Elect Sci & Technol China Sch Comp Sci & Engn Chengdu 611731 Peoples R China Southwestern Univ Finance & Econ Econ Informat Engn Sch Chengdu 611130 Peoples R China

In the fifth-generation (5G) wireless networks, Edge-Internet-of-Things (EIoT) devices are envisioned to generate huge amounts of data. Due to the limitation of computation capacity and battery life of devices, all tasks cannot be processed by these devices. However, mobile-edge computing (MEC) is a very promising solution enabling offloading of tasks to nearby MEC servers to improve quality of service. Also, during emergency situations in areas where network failure exists, unmanned aerial vehicles (UAVs) can be deployed to restore the network by acting as Aerial Base Stations and computational nodes for the edge network. In this article, we consider a central network controller who trains observations and broadcasts the trained data to a multi-UAV cluster network. Each UAV cluster head acts as an agent and autonomously allocates resources to EIoT devices in a decentralized fashion. We propose model-free deep reinforcement learning (DRL)-based collaborative computation offloading and resource allocation (CCORA-DRL) scheme in an aerial to ground (A2G) network for emergency situations, which can control the continuous action space. Each agent learns efficient computation offloading policies independently in the network and checks the statuses of the UAVs through Jain's Fairness index. The objective is minimizing task execution delay and energy consumption and acquiring an efficient solution by adaptive learning from the dynamic A2G network. Simulation results reveal that our scheme through deep deterministic policy gradient, effectively learns the optimal policy, outperforming A3C, deep Q-network and greedy-based offloading for local computation in stochastic dynamic environments.

关键词： collaborative computation offloading deep reinforcement learning (DRL) Edge Internet of Things (EIoT) IoT network multi-UAV network resource allocation

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USV Fleet-Assisted collaborative computation offloading for Smart Maritime Services: An Energy-Efficient Design

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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2024年第10期73卷 14718-14733页

作者： Zeng, Hui Su, Zhou Xu, Qichao Li, Ruidong Wang, Yuntao Dai, Minghui Luan, Tom H. Sun, Xin Liu, Donglan Shanghai Univ Sch Mechatron Engn & Automat Shanghai 200444 Peoples R China Xi An Jiao Tong Univ Sch Cyber Sci & Engn Xian 710049 Peoples R China Kanazawa Univ Inst Sci & Engn Kanazawa 9201192 Japan Univ Macau Macau State Key Lab Internet Things Smart City Macau Peoples R China Univ Macau Dept Comp & Informat Sci Macau Peoples R China State Grid Zhejiang Elect Power Res Inst Hangzhou 310011 Peoples R China State Grid Shandong Elect Power Res Inst Jinan 250003 Peoples R China

Unmanned aerial vehicles (UAVs) empowered with artificial intelligence (AI) have become a new paradigm for on-demand and intelligent marine monitoring. To enable diverse AI applications, numerous computation-intensive tasks (e.g., image recognition, video processing, path planning, etc.) that cannot be locally executed by UAVs need to be timely and effectively offloaded. Multiple unmanned surface vehicles (USVs) integrated into a USV fleet is appealingly advocated to provide abundant computation resources for computation tasks. In this paper, we propose an energy-efficient USV fleets-assisted collaborative computation offloading scheme for smart maritime services. Specifically, we first propose a collaborative computation offloading framework, where UAVs act as the requesters of computation offloading services, and USV fleets are the helpers. Then, the first-price sealed reverse auction with reserve price is utilized to incentivize USV fleets to assist in executing computation tasks of UAVs, where the reserve price guarantees the satisfied benefits of UAVs. Afterwards, to minimize the energy consumption of executing tasks within the USV fleet under the delay constraint, the joint allocation optimization scheme for computation subtasks and computation capacities is proposed based on the Block Coordinate Descent (BCD) and Alternating Direction Method of Multipliers (ADMM). Simulation results demonstrate that the proposed scheme improves the expected revenue and participation degree of the USV fleet and reduces the overall energy consumption of computation offloading compared to conventional schemes.

关键词： Task analysis Energy consumption Collaboration Optimization Resource management computational modeling Servers collaborative computation offloading energy consumption optimization smart maritime services unmanned aerial vehicle

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Independent tasks scheduling of collaborative computation offloading for SDN-powered MEC on 6G networks

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SOFT COMPUTING 2023年第14期27卷 9593-9617页

作者： Al-Hammadi, Ikhlas Li, Mingchu Islam, Sardar M. N. Dalian Univ Technol Sch Software Dalian 116620 Liaoning Peoples R China Victoria Univ Melbourne Australia

The implementation of mobile edge computing (MEC) and software-defined networking (SDN) over sixth-generation networks is a driving force in the future of cloud computing. It holds significant promise in addressing smart device (SD) resources and battery life limitations. To deal with the variety and resource constraints of SDs while making better use of network infrastructure, collaborative offloading has emerged as a viable technique for improving the ability to schedule independent tasks while alleviating the burden of restricted computation resources and network congestion. Network congestion is a common issue when a network node or link carries more data than it can manage. Existing works frequently overlook the impact of insufficient edge server capacity and network congestion. This paper primarily focuses on an SDN-powered MEC network that utilizes a full offloading policy, which completely offloads all tasks from the SD to the edge server and adopts a collaborative offloading of the MEC network when it is overloaded. The problem also takes into consideration when and to whom to offload the task. To bridge this gap, we first implement a collaborative offloading scheme among MEC servers based on the edge server's resources and neighbors' status to alleviate network congestion. It takes advantage of the computing capacities of edge servers deployed at the network's edge and the SDN controller's global view of the entire network. Then we devise a Deep Q-Network methodology to achieve near-optimal performance, and minimize the total execution time concerning deadline constraints. The experiments reveal that our proposed task scheduling of collaborative computation offloading algorithm can significantly minimize the total execution time more than the existing schemes.

关键词： collaborative computation offloading Mobile edge computing DQN 6G Software-defined networking Network congestion

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TD3-based collaborative computation offloading and Charging Scheduling in Multi-UAV-Assisted MEC Networks 25

TD3-based Collaborative Computation Offloading and Charging ...

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IEEE Wireless Communications and Networking Conference (IEEE WCNC)

作者： Zhao, Liang Yao, Yujun Zhou, Huan Wang, Hao Leung, Victor C. M. China Three Gorges Univ Coll Comp & Informat Technol Hubei Key Lab Intelligent Vision Based Monitoring Yichang Peoples R China Northwestern Polytech Univ Sch Comp Sci Xian Peoples R China Shenzhen Univ Coll Comp Sci & Software Engn Shenzhen Peoples R China

ISBN: (纸本)9798350303582;9798350303599

computation offloading, resource allocation, and endurance issues in unmanned aerial vehicle (UAV)-aided mobile edge computing (MEC) networks have always been a research focus. UAV-aided MEC allows mobile users (MUs)' tasks to be offloaded to drones for processing in special scenarios, such as natural disasters or military attacks. However, as the number and size of offloaded tasks increase, a single UAV is difficult to meet all computational demands, result in the decline of QoS. To address this issue, this paper presents a collaborative computation offloading scheme where multiple UAVs can cooperate to handle massive tasks. Firstly, considering that battery-limited UAVs cannot complete all tasks and sustain flight without charging, we incorporate charging stations (CS) into multi-UAV-assisted MEC networks. Subsequently, we design a price-based incentive mechanism to maximize the total revenue obtained from UAVs' collaborative computation. Then, we formulate the joint optimization problem of computation offloading, resource allocation and charging scheduling as a Markov Decision Process (MDP), and propose a Twin Delayed Deep Deterministic policy gradient (TD3) algorithm to find optimal strategies. Finally, extensive simulations demonstrate that the proposed TD3 algorithm outperforms other benchmark methods, achieving the highest overall system utility under different scenarios.

关键词： collaborative computation offloading resource allocation charging scheduling MEC TD3 UAV

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A many-objective evolutionary algorithm based on constraints for collaborative computation offloading

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SWARM AND EVOLUTIONARY computation 2023年 77卷

作者： Cui, Zhihua Xue, Zhaoyu Fan, Tian Cai, Xingjuan Zhang, Wensheng Taiyuan Univ Sci & Technol Sch Comp Sci & Technol Taiyuan Peoples R China Chinese Acad Sci Inst Automat State Key Lab Intelligent Control & Management Com Beijing Peoples R China

This paper designs a many-objective optimized edge and terminal collaborative computation offloading (MaOETCCO) model to give full play to the performance advantages of collaboration and resource sharing be-tween edge server (ES) and intelligent terminal equipment (ITE) for computation offloading, which considers three offloading levels and four objectives: offloading delay minimization, application execution time minimi-zation, energy consumption minimization, and most efficient load balancing. At the same time, a many-objective evolutionary algorithm with load constraints (MaOEA-LC) is designed to solve the model by obtaining Pareto optimal solutions. It consists of a two-layer tournament mating selection based on load constraints, an envi-ronment selection method based on objective value domination, and an evolutionary method of double popu-lation crossover and multipoint mutation. In simulations, the values of the convergence performance evaluation index GD and diversity performance evaluation index Spa are the best in the comparison with other evolutionary algorithms. With optimization of MaOEA-LC, the MaOETCCO model is solved faster with more diverse solutions, and the objective values in the model have been optimized by 31%, 8%, 16%, and 30%, respectively. In addition, the rank value and adjusted p-value of the Friedman statistical test are used to prove the significant advantage of MaOEA-LC.

关键词： Load balancing Many-objective evolutionary algorithm collaborative computation offloading Edge computing

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A Truthful Online Mechanism for collaborative computation offloading in Mobile Edge Computing

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IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 2020年第7期16卷 4832-4841页

作者： He, Junyi Zhang, Di Zhou, Yuezhi Zhang, Yaoxue Tsinghua Univ Dept Comp Sci & Technol Beijing 100084 Peoples R China Beijing Jiaotong Univ Sch Software Engn Beijing 100044 Peoples R China

collaborative computation offloading in mobile edge computing where edge users offload tasks opportunistically to resourceful neighboring mobile devices (MDs), offers a promising solution to satisfy low-latency requirements. However, most existing works assume that those MDs volunteer to help edge users without an incentive mechanism. In this article, we propose an auction-based incentive mechanism, where users and MDs participate in the system dynamically. Our auction mechanism runs in the online fashion and optimizes the long-term system welfare without knowledge of future information, e.g., task start time, task length, resource demand, and valuation, etc. We prove that the proposed online mechanism achieves the desired properties, including individual rationality, truthfulness, and computational tractability. Moreover, the theoretical competitive ratio shows that our online mechanism achieves near-optimal long-term social welfare close to the offline optimum. Extensive experiments based on real-world traces demonstrate the efficiency of the proposed online mechanism.

关键词： collaborative computation offloading social welfare maximization (SWM) truthful online mechanism

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