Online Distributed Offloading and Computing Resource Management With Energy Harvesting for Heterogeneous MEC-Enabled IoT

TLDR

Mobile Internet and IoT convergence has spurred computing‑intensive, delay‑sensitive applications, and while MEC and energy harvesting can enhance performance and sustainability, centralized resource allocation requires precise system state knowledge that is often impractical. The study aims to develop flexible, on‑demand edge‑cloud computing resource allocation and heterogeneous task offloading strategies that incorporate energy harvesting. We propose an online distributed optimization algorithm, grounded in game theory and perturbed Lyapunov optimization, that jointly determines task offloading, computing resource allocation, and battery energy management, and includes a pre‑screening criterion to reduce communication overhead by balancing battery level, latency, and revenue, validated by extensive simulations.

Abstract

With the rapid development and convergence of the mobile Internet and the Internet of Things (IoT), computing-intensive and delay-sensitive IoT applications (APPs) are proliferating with an unprecedented speed in recent years. Mobile edge computing (MEC) and energy harvesting (EH) technologies can significantly improve the user experience by offloading computation tasks to edge-cloud servers as well as achieving green and durable operation. Traditional centralized strategies require precise information of system states, which may not be feasible in the era of big data and artificial intelligence. To this end, how to allocate limited edge-cloud computing resource on demand, and how to develop heterogeneous task offloading strategies with EH in a more flexible manner are remaining challenges. In this paper, we investigate an EH-enabled MEC offloading system, and propose an online distributed optimization algorithm based on game theory and perturbed Lyapunov optimization theory. The proposed algorithm works online and jointly determines heterogeneous task offloading, on-demand computing resource allocation, and battery energy management. Furthermore, to reduce the unnecessary communication overhead and improve the processing efficiency, an offloading pre-screening criterion is designed by balancing battery energy level, latency, and revenue. Extensive simulations are carried out to validate the effectiveness and rationality of the proposed approach.