Mobility-Aware Offloading and Resource Allocation in a MEC-Enabled IoT Network With Energy Harvesting

Abstract

Mobile-edge computing (MEC)-enabled Internet of Things (IoT) networks have been deemed a promising paradigm to support massive energy-constrained and computation-limited IoT devices. Energy harvesting (EH) further enhances the operating capabilities of IoT devices that normally only possess very limited energy support. Nevertheless, many studies show that IoT devices using EH can experience uncertainty and unpredictability, which can complicate the EH-based IoT network design. Furthermore, with many new services in 5G and the forthcoming 6G eras, such as autonomous driving and vehicular communications, mobility consideration in IoT networks becomes more and more important. In this article, we study the computing offloading and resource allocation problems in an IoT network that supports both mobility and EH. The long-term average sum service cost of all the mobile IoT devices (MIDs) is minimized by optimizing the harvested energy, task-partition factors, the central process unit frequencies, the transmit power, and the association vector of MIDs. An online mobility-aware offloading and resource allocation (OMORA) algorithm is proposed based on the Lyapunov optimization and semidefinite programming (SDP). This online algorithm optimizes the offloading scheme without the need to have prior knowledge of the user mobility, EH model, and channel condition. Theoretical analysis shows that the proposed OMORA algorithm can achieve asymptotic optimality. Simulation results demonstrate that the proposed algorithm can effectively balance the system service cost and energy queue length, and outperform other offloading benchmark algorithms on the system service cost and packet losses.