# ECO: Enabling Energy-Neutral IoT Devices through Runtime Allocation of Harvested Energy

arXiv: https://arxiv.org/abs/2102.13605 · HTML: https://mlsanity.com/paper/2102.13605/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 56 references that are in the map.
- First posted on arXiv 26 Feb 2021.

## Builds on (most cited first, 3 of 3)

- [A Survey of Data Fusion in Smart City Applications](https://mlsanity.com/paper/1905.11933/) (2019)
- [Data-Driven Stochastic Models and Policies for Energy Harvesting Sensor Communications](https://mlsanity.com/paper/1405.7254/) (2014)
- [REAP: Runtime Energy-Accuracy Optimization for Energy Harvesting IoT Devices](https://mlsanity.com/paper/1902.02639/) (2019)

## Built on by (most cited first, 3 of 3)

- [Adaptive Energy Management for Self-Sustainable Wearables in Mobile Health](https://mlsanity.com/paper/2201.07888/) (2022)
- [tinyMAN: Lightweight Energy Manager using Reinforcement Learning for Energy Harvesting Wearable IoT Devices](https://mlsanity.com/paper/2202.09297/) (2022)
- [Intelligent Duty Cycling Management and Wake-up for Energy Harvesting IoT Networks with Correlated Activity](https://mlsanity.com/paper/2405.06372/) (2024)

## Abstract (arXiv)

Energy harvesting offers an attractive and promising mechanism to power low-energy devices. However, it alone is insufficient to enable an energy-neutral operation, which can eliminate tedious battery charging and replacement requirements. Achieving an energy-neutral operation is challenging since the uncertainties in harvested energy undermine the quality of service requirements. To address this challenge, we present a runtime energy-allocation framework that optimizes the utility of the target device under energy constraints using a rollout algorithm, which is a sequential approach to solve dynamic optimization problems. The proposed framework uses an efficient iterative algorithm to compute initial energy allocations at the beginning of a day. The initial allocations are then corrected at every interval to compensate for the deviations from the expected energy harvesting pattern. We evaluate this framework using solar and motion energy harvesting modalities and American Time Use Survey data from 4772 different users. Compared to prior techniques, the proposed framework achieves up to 35% higher utility even under energy-limited scenarios. Moreover, measurements on a wearable device prototype show that the proposed framework has 1000x smaller energy overhead than iterative approaches with a negligible loss in utility.