Enabling Mobile Edge Computing for Battery-less Intermittent IoT Devices

Intermittent computing enables battery-less systems to support complex tasks such as face recognition through energy harvesting, but without an installed battery. Nevertheless, the latency may not be satisfied due to the limited computing power. Integrating mobile edge computing (MEC) with intermittent computing would be the desired solution to reduce latency and increase computation efficiency. In this work, we investigate the joint optimization problem of bandwidth allocation and the computation offloading with multiple battery-less intermittent devices in a wireless MEC network. We provide a comprehensive analysis of the expected offloading efficiency, and then propose Greedy Adaptive Balanced Allocation and Offloading (GABAO) algorithm considering the energy arrival distributions, remaining task load, and available computing/communication resources. Simulation results show that the proposed system can significantly reduce the latency in a multi-user MEC network with battery-less devices.