Everyone-Centric Heterogeneous Multi-Server Computation Offloading in ITS with Pervasive AI

Abstract

The challenges of everyone-centric customized endto- end (E2E) service experience are spurring the worldwide interests on the sixth-generation (6G) networks. Nevertheless, in current intelligent transportation systems (ITS), fixed roadside units (RSUs) based multi-access edge computing (MEC) has the defects of poor flexibility, unbalanced utilization and high deployment cost, which cannot meet the diverse requirements for computation-intensive and latency-sensitive Internet of vehicles (IoV) services. In this article, we propose a novel MEC architecture with multiple heterogeneous servers and pervasive intelligence to provide computation offloading cooperatively for customized requirements of task vehicle users (TVUs). A potential game theory and federated deep reinforcement learning based approach, namely GT-FDRL, is proposed to decouple the offloading decision and resource allocation. First, a binary offloading model based on game theory is utilized to make offloading decisions. Secondly, horizontal federated learning is introduced to multiagent double deep Q-network (DDQN) to optimize the channel assignment and power allocation. Furthermore, the client-server architecture and federated aggregation are used to maintain the global model. A case study is presented and the simulation results demonstrate the effectiveness and robustness of our proposed approach.