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- W3000818292 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." @default.
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- W3000818292 date "2020-03-03" @default.
- W3000818292 modified "2023-10-18" @default.
- W3000818292 title "Battery Health Estimation for IoT Devices using V-Edge Dynamics" @default.
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- W3000818292 doi "https://doi.org/10.1145/3376897.3377858" @default.
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