Task Offloading Optimization for AGVs with Fixed Routes in Industrial IoT Environment  

作　　者：Peng Liu Zifu Wu Hangguan Shan Fei Lin Qi Wang Qingshan Wang 

机构地区：[1]School of Computer Science and Technology,Hangzhou Dianzi University,Hangzhou 310018,China [2]HDU-ITMO Joint Institute,Hangzhou Dianzi University,Hangzhou 310018,China [3]College of Information Science and Electronic Engineering,Zhejiang University,Hangzhou 310027,China [4]Hefei University of Technology,School of Mathematics,Hefei 230009,China

出　　处：《China Communications》2023年第5期302-314,共13页中国通信（英文版）

基　　金：supported by National Natural Science Foundation of China(No.62172134).

摘　　要：In order to solve the delay requirements of computing intensive tasks in industrial Internet of things,edge computing is moving from theoretical research to practical applications.Edge servers(ESs)have been deployed in factories,and on-site auto guided vehicles(AGVs),besides doing their regular transportation tasks,can partly act as mobile collectors and distributors of computing data and tasks.Since AGVs may offload tasks to the same ES if they have overlapping path segments,resource allocation conflicts are inevitable.In this paper,we study the problem of efficient task offloading from AGVs to ESs,along their fixed trajectories.We propose a multi-AGV task offloading optimization algorithm(MATO),which first uses the weighted polling algorithm to preliminarily allocate tasks for individual AGVs based on load balancing,and then uses the Deep Q-Network(DQN)model to obtain the updated offloading strategy for the AGV group.The simulation results show that,compared with the existing methods,the proposed MATO algorithm can significantly reduce the maximum completion time of tasks and be stable under various parameter settings.

关 键 词：industrial Internet of Things task offloading optimization auto guided vehicles reinforcement learning 

分 类 号：TP23[自动化与计算机技术—检测技术与自动化装置] TN929.5[自动化与计算机技术—控制科学与工程] TP391.44[电子电信—通信与信息系统]