Uncertainty-Aware Energy Management for Wearable IoT Devices with Conformal Prediction

Wearable internet of things (IoT) are transforming diverse healthcare applications including rehabilitation, vital symptom monitoring, and activity recognition. However, the small form-factor of wearable devices constrains the battery capacity and the operating lifetime, thus requiring frequent rech...

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Veröffentlicht in:2025 62nd ACM/IEEE Design Automation Conference (DAC) S. 1 - 7
Hauptverfasser: Hussein, Dina, Ugwu, Chibuike E., Bhat, Ganapati, Doppa, Janardhan Rao
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: IEEE 22.06.2025
Schlagworte:
Batteries
Biomedical monitoring
Conformal Prediction
Energy management
Internet of Things
IoT Devices
Monitoring
Prediction algorithms
Quality of service
Stochastic processes
Uncertainty
Wearable devices
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Zusammenfassung:Wearable internet of things (IoT) are transforming diverse healthcare applications including rehabilitation, vital symptom monitoring, and activity recognition. However, the 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 leads to lower quality of service and user satisfaction. Harvesting energy from ambient sources to augment the battery has emerged as an effective solution to improving its operating lifetime. However, ambient energy sources are highly stochastic, making energy management challenging. Prior approaches typically use point predictions for estimating future energy and do not explicitly account for the uncertainty. In strong contrast to prior approaches, this paper presents a conformal predictionbased method for future energy harvest that provides small uncertainty regions with provable coverage guarantees (true output is within the uncertainty region). The uncertainty regions over energy harvest are then leveraged in an energy management algorithm that employs Monte Carlo sampling to evaluate the quality of multiple decisions with varying energy harvests. The decisions are then combined using a lightweight machine learning model to make an energy management decision that is close to the optimal. Experiments on two diverse real-world datasets with about 10 users show that conformal prediction achieves more than 90% coverage with tight prediction intervals; and the energy management algorithm produces decisions that are on average within 2 J of an optimal Oracle, thus showing its effectiveness in improving the quality of service.
DOI:10.1109/DAC63849.2025.11132634