Presentation
Uncertainty-Aware Energy Management for Wearable IoT Devices with Conformal Prediction
DescriptionWearable internet of things (IoT) are transforming various healthcare applications, such as rehabilitation, vital symptom monitoring, and activity recognition. However, small form-factor of wearable devices constrains the battery capacity and the operating lifetime, thus requiring frequent recharging or battery replacements. Frequent recharging and battery replacement lead to lower quality of service and user satisfaction. Harvesting energy from ambient sources to augment the battery has emerged as an effective technique to improve the operating lifetime. However, ambient energy sources are highly stochastic making the energy management challenging. Prior approaches typically use point predictions for estimating future energy, which does not account for the uncertainty. In strong contrast to prior approaches, this paper presents a conformal prediction-based method for future energy harvest. The proposed method provides tight prediction regions while ensuring coverage guarantees. The predictions are then leveraged in an energy management algorithm that employs Monte Carlo sampling to evaluate multiple trajectories of decisions with varying energy harvest. The decisions from each trajectory are combined using a light-weight machine learning model to make an energy management decision that follows an optimal trajectory. Experiments with two diverse datasets with about 10 users show that the proposed approach achieves more than 90% coverage with tight prediction intervals. The energy management algorithm achieves decisions that are within 2 J of an optimal Oracle, thus showing its effectiveness is improving the quality of service.
Event Type
Research Manuscript
TimeMonday, June 235:00pm - 5:15pm PDT
Location3008, Level 3
Systems
SYS2: Design of Cyber-Physical Systems and IoT