Energy Loss Prediction in IoT Energy Services
This addresses energy efficiency in IoT charging, but appears incremental as it builds on existing energy service composition techniques.
The paper tackles the problem of predicting energy loss in crowdsourced wireless energy services for IoT devices, proposing an attention-based algorithm called Easeformer that significantly outperforms existing methods on real datasets.
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.