# Energy Loss Prediction in IoT Energy Services

arXiv: https://arxiv.org/abs/2305.10238 · HTML: https://mlsanity.com/paper/2305.10238/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 25 of its 45 references that are in the map.
- First posted on arXiv 16 May 2023.

## Builds on (most cited first, 25 of 25)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Distilling the Knowledge in a Neural Network](https://mlsanity.com/paper/1503.02531/) (2015)
- [Sequence to Sequence Learning with Neural Networks](https://mlsanity.com/paper/1409.3215/) (2014)
- [Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting](https://mlsanity.com/paper/2012.07436/) (2020)
- [On the Properties of Neural Machine Translation: Encoder-Decoder Approaches](https://mlsanity.com/paper/1409.1259/) (2014)
- [Dilated Residual Networks](https://mlsanity.com/paper/1705.09914/) (2017)
- [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)
- [Parameter estimation in softmax decision-making models with linear objective functions](https://mlsanity.com/paper/1502.04635/) (2015)
- [Wireless Charging Technologies: Fundamentals, Standards, and Network Applications](https://mlsanity.com/paper/1509.00940/) (2015)
- [Proactive Composition of Mobile IoT Energy Services](https://mlsanity.com/paper/2107.12519/) (2021)
- [Composing Energy Services in a Crowdsourced IoT Environment](https://mlsanity.com/paper/2111.07528/) (2021)
- [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)
- [Fluid Composition of Intermittent IoT Energy Services](https://mlsanity.com/paper/2008.00787/) (2020)
- [Elastic Composition of Crowdsourced IoT Energy Services](https://mlsanity.com/paper/2011.06771/) (2020)
- [Crowdsharing Wireless Energy Services](https://mlsanity.com/paper/2011.07650/) (2020)
- [Monitoring Efficiency of IoT Wireless Charging](https://mlsanity.com/paper/2303.05629/) (2023)
- [Incentive-Based Selection and Composition of IoT Energy Services](https://mlsanity.com/paper/2007.09985/) (2020)
- [Reliability Model for Incentive-Driven IoT Energy Services](https://mlsanity.com/paper/2011.06159/) (2020)
- [Multi-Use Trust in Crowdsourced IoT Services](https://mlsanity.com/paper/2203.07712/) (2022)
- [Activity-based Profiling for Energy Harvesting Estimation](https://mlsanity.com/paper/2305.08025/) (2023)

## Abstract (arXiv)

We propose a novel Energy Loss Prediction(ELP) framework that estimates the energy loss in sharing crowdsourced energy services. Crowdsourcing wireless energy services is a novel and convenient solution to enable the ubiquitous charging of nearby IoT devices. Therefore, capturing the wireless energy sharing loss is essential for the successful deployment of efficient energy service composition techniques. We propose Easeformer, a novel attention-based algorithm to predict the battery levels of IoT devices in a crowdsourced energy sharing environment. The predicted battery levels are used to estimate the energy loss. A set of experiments were conducted to demonstrate the feasibility and effectiveness of the proposed framework. We conducted extensive experiments on real wireless energy datasets to demonstrate that our framework significantly outperforms existing methods.