A DRL Driven Intelligent Joint Optimization Strategy for Computation Offloading and Resource Allocat

Intelligent computation offloading and resource allocation for mobile users (MUs) in ubiquitous edge Internet of Things (IoT) systems is a worthy research hotspot. To improve the latency and energy consumption of MUs in ubiquitous edge IoT systems, we propose a deep reinforcement learnin (DRL)-driven intelligent joint optimization strategy for computation offloading and resource allocation that includes relay selection, offloading decisions, and resource allocation. Specifically, according to the limited coverage of base stations and the extremely high deployment costs in actual environments, we introduce the virtual backbone architecture to provide users with efficient multi-hop offload services through a connected dominating set (CDS). Then, we propose a CDS-based deep reinforcement learning algorithm to search for the shortest path from the MUs to the multi-access edge computing (MEC) server. Furthermore, based on the highly coupled relationship between the offloading decision and resource allocation, we design a DLIO algorithm to solve for the joint optimization of computation offloading and resource allocation. The experimental results demonstrate that our proposed optimization algorithm outperforms the state-of-the-art methods in terms of total system cost, success rate, and acceptance number.