Near-Optimal Energy Management for Energy Harvesting IoT Devices Using Imitation Learning

Abstract

Internet of Things (IoT) devices are becoming popular in a number of transformative applications, including smart health, digital agriculture, and wide area sensing. However, small battery capacities and the need for frequent battery replacements or recharging have hindered their widespread adoption. Energy harvesting (EH) and management present a promising opportunity to enable long-term recharge free operation of IoT devices. State-of-the-art energy management approaches employ dynamic optimization methods to manage the harvested energy. However, the dynamic optimization methods are typically computationally intensive and lead to significant energy overhead for energy-constrained IoT devices. In contrast, this article proposes an imitation learning (IL)-based energy management algorithm that provides the energy budget or allocation for each decision interval without the need for dynamic optimization. We present an efficient approach to design Oracle policies that optimize the energy allocation of the IoT device to enable self-powered operation while maximizing the utility to the application. Then, we leverage the Oracle policy to train an online policy that performs near-optimal energy allocation at runtime. Our experiments with solar EH data for six years from three locations show that the proposed IL policies achieve allocation that is, on average, within 2.5 J of the Oracle, while having an energy consumption overhead of 154 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mu \text{J}$ </tex-math></inline-formula> .