Indoor–Outdoor Energy Management for Wearable IoT Devices With Conformal Prediction and Rollout

Abstract

Internet of Things (IoT) devices have the potential to enable a wide range of applications, including smart health and agriculture. However, they are limited by their small battery capacities. Utilizing energy harvesting is a promising approach to augment the battery life of IoT devices. However, relying solely on harvested energy is insufficient due to the stochastic nature of ambient sources. Predicting and accounting for uncertainty in the energy harvest (EH) is critical for optimal energy management (EM) in wearable IoT devices. This article proposes a two-step uncertainty-aware EH prediction and management framework for wearable IoT devices. First, the framework employs an energy-efficient conformal prediction (CP) method to predict future EH and construct prediction intervals. Contrasting to prior CP approaches, we propose constructing the prediction intervals using a combination of residuals from previous hours and days. Second, the framework proposes a near-optimal EM approach that utilizes a rollout algorithm. The rollout algorithm efficiently simulates various energy allocation trajectories as a function of predicted EH bounds. Using results from the rollout, the proposed approach constructs energy allocation bounds that maximize application utility (quality of service) with a high probability. Evaluations using real-world energy data from ARAS and Mannheim datasets show that the proposed CP for EH prediction provides 93% coverage probability with an average width of 9.5 J and 1.9 J, respectively. Moreover, EM using the rollout algorithm provides energy allocation decisions that are within 1.9–2.9 J of the optimal with minimal overhead.