On Predicting the Battery Lifetime of IoT Devices

Xenofon Fafoutis, Atis Elsts, Antonis Vafeas, George Oikonomou, Robert J. Piechocki

2018 · 30 citations · 22 references

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TL;DR

Deploying IoT battery‑powered sensors is challenged by the need to manage battery maintenance. This study aims to compare real‑world battery lifetimes and discharge patterns with predictions made during system development. The authors use prediction techniques and analyze long‑term residential deployments to contrast actual lifetimes against development‑stage forecasts. The comparison exposes the difficulty of accurate lifetime prediction and identifies lessons that could speed future large‑scale IoT deployments.

Abstract

One of the challenges of deploying IoT battery-powered sensing systems is managing the maintenance of batteries. To that end, practitioners often employ prediction techniques to approximate the battery lifetime of the deployed devices. Following a series of long-term residential deployments in the wild, this paper contrasts real-world battery lifetimes and discharge patterns against battery lifetime predictions that were conducted during the development of the deployed system. The comparison highlights the challenges of making battery lifetime predictions, in an attempt to motivate further research on the matter. Moreover, this paper summarises key lessons learned that could potentially accelerate future IoT deployments of similar scale and nature.

References

22