Energy Status Recovery Using Recurrent SVR Framework With Data Loss Conditions

To address the short-lived battery lifetime of Bluetooth low energy (BLE) beacons, researchers proposed solar-powered designs, equipped with rechargeable energy storage such as a supercapacitor. However, accurately monitoring the energy status - an essential step for device maintenance - has shown to be a major concern. Existing energy status monitoring methods, which are either crowd-assisted or require on-site data collection, suffer from severe losses of energy status information. This paper presents an energy status recovery framework with support vector regression (SVR) to address this issue. The proposed framework leverages recurrence training of SVR with lost energy status information to capture features from discharge behavior, achieving high accuracy while minimizing 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 98% accuracy under a data loss rate of up to 99%.