A scalable, data-driven approach for estimating battery health degradation of IoT devices: Poster abstract

Abstract

Life cycle management of battery powered-IoT devices in large scale deployments is difficult due to the non-existence of a compatible approach to estimate their battery health. Most existing approaches require either battery parameters, determination of which is beyond IoT devices' capability due to hardware limitation, or special applicable conditions that do not always hold due to devices' dynamic operating environments. In this paper, we propose a novel approach for facilitating the life cycle management of large-scale deployments through online estimation of battery health. Our approach is based on V-edge dynamics which capture and characterize instantaneous voltage drops. Our evaluation carried out on a dataset of battery discharge measurements demonstrate that our approach is capable of estimating the battery health up to 80% accuracy.