# Monitoring Efficiency of IoT Wireless Charging

arXiv: https://arxiv.org/abs/2303.05629 · HTML: https://mlsanity.com/paper/2303.05629/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 18 references that are in the map.
- First posted on arXiv 10 Mar 2023.

## Builds on (most cited first, 11 of 11)

- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [Fairness-aware Crowdsourcing of IoT Energy Services](https://mlsanity.com/paper/2111.06064/) (2021)
- [Wireless IoT Energy Sharing Platform](https://mlsanity.com/paper/2201.11275/) (2022)
- [Quality of Experience Optimization in IoT Energy Services](https://mlsanity.com/paper/2208.13506/) (2022)
- [Maximizing Consumer Satisfaction of IoT Energy Services](https://mlsanity.com/paper/2209.03619/) (2022)
- [Service-Based Wireless Energy Crowdsourcing](https://mlsanity.com/paper/2210.15494/) (2022)
- [Towards peer-to-peer sharing of wireless energy services](https://mlsanity.com/paper/2211.12464/) (2022)
- [Predicting Smartphone Battery Life based on Comprehensive and Real-time Usage Data](https://mlsanity.com/paper/1801.04069/) (2018)
- [Fair Scheduling in Resonant Beam Charging for IoT Devices](https://mlsanity.com/paper/1809.09379/) (2018)
- [Towards Optimal Kinetic Energy Harvesting for the Batteryless IoT](https://mlsanity.com/paper/2002.08887/) (2020)
- [Crowdsharing Wireless Energy Services](https://mlsanity.com/paper/2011.07650/) (2020)

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

- [Energy Loss Prediction in IoT Energy Services](https://mlsanity.com/paper/2305.10238/) (2023)
- [CrowdWeb: A Visualization Tool for Mobility Patterns in Smart Cities](https://mlsanity.com/paper/2305.12935/) (2023)

## Abstract (arXiv)

Crowdsourcing wireless energy is a novel and convenient solution to charge nearby IoT devices. Several applications have been proposed to enable peer-to-peer wireless energy charging. However, none of them considered the energy efficiency of the wireless transfer of energy. In this paper, we propose an energy estimation framework that predicts the actual received energy. Our framework uses two machine learning algorithms, namely XGBoost and Neural Network, to estimate the received energy. The result shows that the Neural Network model is better than XGBoost at predicting the received energy. We train and evaluate our models by collecting a real wireless energy dataset.