# REAP: Runtime Energy-Accuracy Optimization for Energy Harvesting IoT Devices

arXiv: https://arxiv.org/abs/1902.02639 · HTML: https://mlsanity.com/paper/1902.02639/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Feb 2019.

## Built on by (most cited first, 2 of 2)

- [ECO: Enabling Energy-Neutral IoT Devices through Runtime Allocation of Harvested Energy](https://mlsanity.com/paper/2102.13605/) (2021)
- [Adaptive Energy Management for Self-Sustainable Wearables in Mobile Health](https://mlsanity.com/paper/2201.07888/) (2022)

## Abstract (arXiv)

The use of wearable and mobile devices for health monitoring and activity recognition applications is increasing rapidly. These devices need to maximize their accuracy and active time under a tight energy budget imposed by battery and small form-factor constraints. This paper considers energy harvesting devices that run on a limited energy budget to recognize user activities over a given period. We propose a technique to co-optimize the accuracy and active time by utilizing multiple design points with different energy-accuracy trade-offs. The proposed technique switches between these design points at runtime to maximize a generalized objective function under tight harvested energy budget constraints. We evaluate the proposed approach experimentally using a custom hardware prototype and fourteen user studies. The proposed approach achieves both 46% higher expected accuracy and 66% longer active time compared to the highest performance design point.