Cooperative vehicles‐assisted task offloading in vehicular networks. Issue 7 (22nd February 2022)

• Record Type: Journal Article
• Authors: Cui, Yaping; Du, Lijuan; He, Peng; Wu, Dapeng; Wang, Ruyan
• Is Part Of: Transactions on emerging telecommunications technologies. Volume 33:Issue 7(2022)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2022-02-22
• Subjects: double deep Q‐network (DDQN) -- parallel computing -- task offloading -- Vehicular networks; Telecommunication -- Periodicals; 384.05
• DOI: 10.1002/ett.4472
• Languages: English
• ISSNs: 2161-5748

Abstract

Abstract: Cooperative task offloading emerges a well‐received paradigm for mobile applications that are sensitive to computational power, while dynamic and real‐time characteristics of vehicular networks makes it challenging to guarantee the low delay requirements of vehicular computation offloading. Existing researches cannot satisfy the real‐time computation requests due to the sparse deployment of infrastructure constructions and constrained computing resources of edge servers. Motivated by these, we consider the idea of distributed vehicle‐to‐vehicle task offloading, which makes vehicles act as cooperative nodes to execute tasks. In this paper, we utilize parallel computing of multi‐vehicle cooperation, to provide low‐delay computation services without exceeding the energy constraint. Furthermore, a cooperative vehicles assisted task offloading strategy based on double deep Q‐network is proposed to obtain the optimal task offloading ratio after selecting cooperative vehicles. Simulation results indicate that our proposed strategy can effectively decrease the total system delay. For example, compared with the local execution strategy, the total system delay of the proposed strategy can be reduced by 69.4% on average. Abstract : In this paper, we utilize parallel computing of multivehicle cooperation, to provide low‐delay computation services without exceeding the energy constraint. Furthermore, a cooperative vehicles assisted task offloading strategy based on double deep Q‐network (DDQN) is proposed to obtain the optimal task offloading ratio after selecting cooperative vehicles. Simulation results indicate that our proposed strategy can effectively decrease the total system delay.