Associative Tasks Computing Offloading Scheme in Internet of Medical Things with Deep Reinforcement Learning  

作　　者：Jiang Fan Qin Junwei Liu Lei Tian Hui 

机构地区：[1]School of Communications and Information Engineering,Xi’an University of Posts and Telecommunications,Xi’an 710121,China [2]State Key Laboratory of Networking and Switching Technology,Beijing University of Posts and Telecommunications,Beijing 100876,China

出　　处：《China Communications》2024年第4期38-52,共15页中国通信（英文版）

基　　金：supported by National Natural Science Foundation of China(Grant No.62071377,62101442,62201456);Natural Science Foundation of Shaanxi Province(Grant No.2023-YBGY-036,2022JQ-687);The Graduate Student Innovation Foundation Project of Xi’an University of Posts and Telecommunications under Grant CXJJDL2022003.

摘　　要：The Internet of Medical Things(Io MT) 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, Io MT 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 Io MT. Specifically,to minimize the total delay and energy consumption concerning the requirement of Io MT, 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 cacheaided 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 Io MT network, the proposed algorithm can lower the total cost more effectively and efficiently while also providing a tradeoff between delay and energy consumption tolerance.

关 键 词：associative tasks cache-aided procedure double deep Q-network Internet of Medical Things(IoMT) multi-access edge computing(MEC) 

分 类 号：R-05[医药卫生] TN929.5[电子电信—通信与信息系统] TP18[电子电信—信息与通信工程]