Privacy-Preserving Incentive Mechanism for Platoon Assisted Vehicular Edge Computing with Deep Reinforcement Learning  

作　　者：Xumin Huang Yupei Zhong Yuan Wu Peichun Li Rong Yu 

机构地区：[1]School of Automation,Guangdong University of Technology,Guangzhou 510006,China [2]The State Key Laboratory of Internet of Things for Smart City,University of Macao,Taipa,Macao,China [3]Department of Computer and Information Science,University of Macao,Taipa,Macao,China

出　　处：《China Communications》2022年第7期294-309,共16页中国通信（英文版）

基　　金：supported in part by National Key R&D Program of China under Grant 2020YFB1807802;in part by National Natural Science Foundation of China under Grants 62001125 and 61971148;in part by FDCT-MOST Joint Project under Grant 0066/2019/AMJ;in part by Science and Technology Development Fund of Macao SAR under Grant 0162/2019/A3;in part by FDCT SKL-IOTSC(UM)2021-2023;in part by Research Grant of University of Macao under Grant MYRG2020-00107-IOTSC;in part by Guangdong Basic and Applied Basic Research Foundation under Grant 2022A1515011287

摘　　要：Platoon assisted vehicular edge computing has been envisioned as a promising paradigm of implementing offloading services through platoon cooperation.In a platoon,a vehicle could play as a requester that employs another vehicles as performers for workload processing.An incentive mechanism is necessitated to stimulate the performers and enable decentralized decision making,which avoids the information collection from the performers and preserves their privacy.We model the interactions among the requester(leader)and multiple performers(followers)as a Stackelberg game.The requester incentivizes the performers to accept the workloads.We derive the Stackelberg equilibrium under complete information.Furthermore,deep reinforcement learning is proposed to tackle the incentive problem while keeping the performers’information private.Each game player becomes an agent that learns the optimal strategy by referring to the historical strategies of the others.Finally,numerical results are provided to demonstrate the effectiveness and efficiency of our scheme.

关 键 词：vehicular edge computing Stackelberg game deep reinforcement learning 

分 类 号：U463.6[机械工程—车辆工程] TP309[交通运输工程—载运工具运用工程] TP18[交通运输工程—道路与铁道工程]