Paper Info
Title
Battery Health Estimation for IoT Devices using V-Edge Dynamics
Abstract
Deployments of battery-powered IoT devices have become ubiquitous, monitoring everything from environmental conditions in smart cities to wildlife movements in remote areas. How to manage the life-cycle of sensors in such large-scale deployments is currently an open issue. Indeed, most deployments let sensors operate until they fail and fix or replace the sensors post-hoc. In this paper, we contribute by developing a new approach for facilitating the life-cycle management of large-scale sensor deployments through online estimation of battery health. Our approach relies on so-called V-edge dynamics which capture and characterize instantaneous voltage drops. Experiments carried out on a dataset of battery discharge measurements demonstrate that our approach is capable of estimating battery health with up to $80%$ accuracy, depending on the characteristics of the devices and the processing load they undergo. Our method is particularly well-suited for the sensor devices, operating dedicated tasks, that they have constant discharge during their operation.
Year
DOI
Venue
2020
10.1145/3376897.3377858
HotMobile '20: The 21st International Workshop on Mobile Computing Systems and Applications Austin TX USA March, 2020
Keywords
DocType
ISBN
Lithium Battery, Power Models, Battery Health, Battery Capacity, Internet of Things
Conference
978-1-4503-7116-2
Citations 
PageRank 
References 
0
0.34
0
Authors
6
Name
Order
Citations
PageRank
Arjun Kumar100.34
Mohammad Asharful Hoque2836.72
Petteri Nurmi362157.08
Michael G. Pecht400.34
Sasu Tarkoma51312125.76
Junehwa Song61384105.08