Deep reinforcement learning based task offloading in blockchain enabled smart city

作     者：金凯琦 WU Wenjun GAO Yang YIN Yufen SI Pengbo JIN Kaiqi

作者机构：Faculty of Information TechnologyBeijing University of TechnologyBeijing 100124P.R.China 

出 版 物：《High Technology Letters》 (高技术通讯（英文版）)

年 卷 期：2023年第29卷第3期

页      面：295-304页

核心收录：

学科分类：12[管理学] 1204[管理学-公共管理] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 081104[工学-模式识别与智能系统] 08[工学] 081303[工学-城市规划与设计(含：风景园林规划与设计）] 0835[工学-软件工程] 0813[工学-建筑学] 0833[工学-城乡规划学] 0811[工学-控制科学与工程] 083302[工学-城乡规划与设计] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：Supported by the National Natural Science Foundation of China(No.62001011) the Natural Science Foundation of Beijing Municipality(No.L192002) 

主　　题：mobile edge computing(MEC) blockchain policy gradient task offloading 

摘      要：With the expansion of cities and emerging complicated application,smart city has become an in-telligent management *** order to guarantee the information security and quality of service(QoS)of the Internet of Thing(IoT)devices in the smart city,a mobile edge computing(MEC)en-abled blockchain system is considered as the smart city scenario where the offloading process of com-puting tasks is a key aspect infecting the system performance in terms of service profit and *** task offloading process is formulated as a Markov decision process(MDP)and the optimal goal is the cumulative profit for the offloading nodes considering task profit and service latency cost,under the restriction of system timeout as well as processing ***,a policy gradient based task of-floading(PG-TO)algorithm is proposed to solve the optimization ***,the numerical re-sult shows that the proposed PG-TO has better performance than the comparison algorithm,and the system performance as well as QoS is analyzed *** testing result indicates that the pro-posed method has good generalization.