Abstract

To address the short-lived battery lifetime of Bluetooth low energy (BLE) beacons, solar-powered designs were proposed, equipped with rechargeable energy storage such as a supercapacitor. However, energy status monitoring, which is essential for device maintenance, proved to be a major concern as the energy status of energy harvesting devices can change quickly due to charging and discharging behaviours. Existing energy status monitoring methods performed in a crowd-assisted manner or by demanding on-site data collections are accompanied by severe loss of energy status information. This paper presents an accurate energy status recovery framework with SVR to address this issue. The proposed framework leverages recurrent training of SVR with lost energy status information to capture features from discharge behaviour to achieve high accuracy while minimizing the training and prediction time. Multiple real-life BLE beacon energy level records are evaluated to demonstrate that our proposed framework can recover the energy information with at least 90% accuracy under a data loss rate of up to 99%.