TY - GEN
T1 - Mobility-Aware Computation Offloading and Blockchain-based Handover in Vehicular Edge Computing Networks
AU - Lang, Ping
AU - Tian, Daxin
AU - Duan, Xuting
AU - Zhou, Jianshan
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - As a novel computing paradigm, vehicular edge computing (VEC) provides additional computing capacity to connected automated vehicles by deploying resources of computing and storage on base stations or roadside units. Vehicles migrate their tasks to VEC servers for execution through computation offloading (CO) to improve processing efficiency. However, the high-speed movement of vehicles causes handover among multiple VEC servers while raising the security issue of data sharing. In this paper, we design a mobility-aware CO and blockchain-based handover architecture to reduce the latency and improve the security of vehicular CO. A CO decision problem with models of mobility, CO, and blockchain-based handover is proposed to optimize the offloading decision of vehicles. Further, we transform this optimization into a Markov decision process (MDP) and construct a multi-agent deep reinforcement learning (MADRL) algorithm to solve it. The effectiveness and performance of the proposed method are verified by simulations.
AB - As a novel computing paradigm, vehicular edge computing (VEC) provides additional computing capacity to connected automated vehicles by deploying resources of computing and storage on base stations or roadside units. Vehicles migrate their tasks to VEC servers for execution through computation offloading (CO) to improve processing efficiency. However, the high-speed movement of vehicles causes handover among multiple VEC servers while raising the security issue of data sharing. In this paper, we design a mobility-aware CO and blockchain-based handover architecture to reduce the latency and improve the security of vehicular CO. A CO decision problem with models of mobility, CO, and blockchain-based handover is proposed to optimize the offloading decision of vehicles. Further, we transform this optimization into a Markov decision process (MDP) and construct a multi-agent deep reinforcement learning (MADRL) algorithm to solve it. The effectiveness and performance of the proposed method are verified by simulations.
UR - https://www.scopus.com/pages/publications/85141824266
U2 - 10.1109/ITSC55140.2022.9922357
DO - 10.1109/ITSC55140.2022.9922357
M3 - 会议稿件
AN - SCOPUS:85141824266
T3 - IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC
SP - 176
EP - 182
BT - 2022 IEEE 25th International Conference on Intelligent Transportation Systems, ITSC 2022
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 25th IEEE International Conference on Intelligent Transportation Systems, ITSC 2022
Y2 - 8 October 2022 through 12 October 2022
ER -