TLCO: Topological Link-Aware Task Co-Offloading Method for Joint V2V and V2I System

Huijun Tang, Ming Du, Huaming Wu, Pengfei Jiao, Ruidong Li

Abstract: Joint Vehicle-to-vehicle (V2V) and Vehicle-to-Infrastructure (V2I) offloading presents an efficient approach to leverage surplus computing resources from neighboring devices, thereby expanding the coverage of computing resources supply in the context of the Internet of Vehicles. However, many studies overlook the significance of topological communications caused by the rapid movement of vehicles, privacy, and communication intentions. To achieve efficient task offloading when facing various topological link structures, we first propose a novel topological link-aware task co-offloading (TLCO) method designed for partially offloading in the joint V2V and V2I system. Next, we model the sequential subtasks offloading process as the Markov Decision Process (MDP) and utilize the Double Deep Q-Network (DDQN) algorithm to optimize the total delay of the proposed system. Additionally, we put forth a prediction framework named Sliding Time Windows and TLCO algorithm (STW-TLCO) to accurately forecast the computation load at various time windows using pulsed parameters. Extensive experimental results demonstrate the effectiveness and superiority of the proposed TLCO-DDQN algorithm in comparison to other Deep Reiforcement Learning (DRL)-based and Greedy-based approaches. Furthermore, the STW-TLCO algorithm exhibits high accuracy, with an R-squared value exceeding 96%, confirming its predictive capabilities.

External IDs:dblp:journals/tits/TangDWJL25