Uncertainty-aware Energy Harvest Prediction and Management for IoT Devices

Abstract

Internet of things (IoT) devices are popular in several high-impact applications such as mobile healthcare and digital agriculture. However, IoT devices have limited operating lifetime due to their small form factor. Harvesting energy from ambient sources is an effective method to supplement the battery. Energy harvesting necessitates development of energy management policies to manage the harvested energy. Designing optimal policies for energy management is challenging for two key reasons: (1) ambient energy sources are highly stochastic; therefore, energy management policies must consider the associated uncertainty; (2) energy management policies must consider future energy availability while making decisions to ensure that sufficient energy is available when there is no ambient energy. Prior approaches typically consider energy in the immediate future (e.g., 1 hour) and do not account for the uncertainty in future energy harvest. This article proposes novel machine learning and dynamic optimization-based approaches to handle the two challenges. Specifically, we first develop a novel set of features and use it in a low-power neural network architecture to predict future energy availability and uncertainty. The energy predictions and uncertainty are used in a dynamic optimization algorithm to optimally allocate the harvested energy. Experiments on solar energy data over 5 years from Golden, Colorado, show that the proposed energy prediction model achieves 3.4 J mean absolute error while having a coverage of 80%. Moreover, our energy management algorithm provides energy allocations that are within 2.5 J of an optimal Oracle with 2.65 mJ to 36.54 mJ of energy overhead.