Associative tasks computing offloading scheme in Internet of medical things with deep reinforcement learning

Jiang Fan, Junwei Qin, Liu Lei, Tian Hui
{"title":"Associative tasks computing offloading scheme in Internet of medical things with deep reinforcement learning","authors":"Jiang Fan, Junwei Qin, Liu Lei, Tian Hui","doi":"10.23919/JCC.fa.2023-0518.202404","DOIUrl":null,"url":null,"abstract":"The Internet of Medical Things (IoMT) is regarded as a critical technology for intelligent healthcare in the foreseeable 6G era. Nevertheless, due to the limited computing power capability of edge devices and task-related coupling relationships, IoMT faces unprecedented challenges. Considering the associative connections among tasks, this paper proposes a computing offloading policy for multiple-user devices (UDs) considering device-to-device (D2D) communication and a multi-access edge computing (MEC) technique under the scenario of IoMT. Specifically, to minimize the total delay and energy consumption concerning the requirement of IoMT, we first analyze and model the detailed local execution, MEC execution, D2D execution, and associated tasks offloading exchange model. Consequently, the associated tasks' offloading scheme of multi-UDs is formulated as a mixed-integer nonconvex optimization problem. Considering the advantages of deep reinforcement learning (DRL) in processing tasks related to coupling relationships, a Double DQN based associative tasks computing offloading (DDATO) algorithm is then proposed to obtain the optimal solution, which can make the best offloading decision under the condition that tasks of UDs are associative. Furthermore, to reduce the complexity of the DDATO algorithm, the cache-aided procedure is intentionally introduced before the data training process. This avoids redundant offloading and computing procedures concerning tasks that previously have already been cached by other UDs. In addition, we use a dynamic ε — greedy strategy in the action selection section of the algorithm, thus preventing the algorithm from falling into a locally optimal solution. Simulation results demonstrate that compared with other existing methods for associative task models concerning different structures in the IoMT network, the proposed algorithm can lower the total cost more effectively and efficiently while also providing a tradeoff between delay and energy consumption tolerance.","PeriodicalId":504777,"journal":{"name":"China Communications","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"China Communications","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.23919/JCC.fa.2023-0518.202404","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

Abstract

The Internet of Medical Things (IoMT) is regarded as a critical technology for intelligent healthcare in the foreseeable 6G era. Nevertheless, due to the limited computing power capability of edge devices and task-related coupling relationships, IoMT faces unprecedented challenges. Considering the associative connections among tasks, this paper proposes a computing offloading policy for multiple-user devices (UDs) considering device-to-device (D2D) communication and a multi-access edge computing (MEC) technique under the scenario of IoMT. Specifically, to minimize the total delay and energy consumption concerning the requirement of IoMT, we first analyze and model the detailed local execution, MEC execution, D2D execution, and associated tasks offloading exchange model. Consequently, the associated tasks' offloading scheme of multi-UDs is formulated as a mixed-integer nonconvex optimization problem. Considering the advantages of deep reinforcement learning (DRL) in processing tasks related to coupling relationships, a Double DQN based associative tasks computing offloading (DDATO) algorithm is then proposed to obtain the optimal solution, which can make the best offloading decision under the condition that tasks of UDs are associative. Furthermore, to reduce the complexity of the DDATO algorithm, the cache-aided procedure is intentionally introduced before the data training process. This avoids redundant offloading and computing procedures concerning tasks that previously have already been cached by other UDs. In addition, we use a dynamic ε — greedy strategy in the action selection section of the algorithm, thus preventing the algorithm from falling into a locally optimal solution. Simulation results demonstrate that compared with other existing methods for associative task models concerning different structures in the IoMT network, the proposed algorithm can lower the total cost more effectively and efficiently while also providing a tradeoff between delay and energy consumption tolerance.
利用深度强化学习的医疗物联网关联任务计算卸载方案
在可预见的 6G 时代,医疗物联网(IoMT)被视为智能医疗的关键技术。然而,由于边缘设备的计算能力有限以及与任务相关的耦合关系,IoMT 面临着前所未有的挑战。考虑到任务之间的关联关系,本文提出了一种考虑到设备到设备(D2D)通信的多用户设备(UDs)计算卸载策略,以及 IoMT 场景下的多访问边缘计算(MEC)技术。具体来说,为了最大限度地减少 IoMT 要求的总延迟和能耗,我们首先分析和模拟了详细的本地执行、MEC 执行、D2D 执行和相关任务卸载交换模型。因此,多 UD 的关联任务卸载方案被表述为一个混合整数非凸优化问题。考虑到深度强化学习(DRL)在处理耦合关系相关任务方面的优势,提出了一种基于双DQN的关联任务计算卸载(DDATO)算法,以获得最优解,从而在UD任务具有关联性的条件下做出最佳卸载决策。此外,为了降低 DDATO 算法的复杂性,有意在数据训练过程之前引入了缓存辅助程序。这就避免了多余的卸载和计算程序,而这些程序涉及的任务之前已被其他 UD 缓存。此外,我们在算法的行动选择部分使用了动态ε-贪婪策略,从而防止算法陷入局部最优解。仿真结果表明,与针对 IoMT 网络中不同结构的关联任务模型的其他现有方法相比,所提出的算法能更有效、更高效地降低总成本,同时还能在延迟和能耗容忍度之间进行权衡。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信