Optimization Scheme of Trusted Task Offloading in IIoT Scenario Based on DQN  

作　　者：Xiaojuan Wang Zikui Lu Siyuan Sun Jingyue Wang Luona Song Merveille Nicolas 

机构地区：[1]Beijing University of Posts and Telecommunications,Beijing,100876,China [2]China United Network Communications Corporation Beijing Branch,100800,China [3]Beijing Information Science and Technology University,Beijing,100192,China [4]University of Quebec at Montreal,Montreal,H2X 3X2,Canada

出　　处：《Computers, Materials & Continua》2023年第1期2055-2071,共17页计算机、材料和连续体（英文）

基　　金：supported by the Projects of Software of Big Data Processing Tool(TC210804V-1);Big Data Risk Screening Model Procurement(No.S20200).

摘　　要：With the development of the Industrial Internet of Things(IIoT),end devices(EDs)are equipped with more functions to capture information.Therefore,a large amount of data is generated at the edge of the network and needs to be processed.However,no matter whether these computing tasks are offloaded to traditional central clusters or mobile edge computing(MEC)devices,the data is short of security and may be changed during transmission.In view of this challenge,this paper proposes a trusted task offloading optimization scheme that can offer low latency and high bandwidth services for IIoT with data security.Blockchain technology is adopted to ensure data consistency.Meanwhile,to reduce the impact of low throughput of blockchain on task offloading performance,we design the processes of consensus and offloading as a Markov decision process(MDP)by defining states,actions,and rewards.Deep reinforcement learning(DRL)algorithm is introduced to dynamically select offloading actions.To accelerate the optimization,we design a novel reward function for the DRL algorithm according to the scale and computational complexity of the task.Experiments demonstrate that compared with methods without optimization,our mechanism performs better when it comes to the number of task offloading and throughput of blockchain.

关 键 词：Task offloading blockchain industrial internet of things(IIoT) deep reinforcement learning(DRL)network mobile-edge computing(MEC) 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]