Energy Loss Prediction in IoT Energy Services
Pengwei Yang Amani Abusafia Abdallah Lakhdari Athman Bouguettaya
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.