Imperial College London


Faculty of EngineeringDepartment of Computing

Professor of Computer Systems



+44 (0)20 7594 8375j.mccann Website




258ACE ExtensionSouth Kensington Campus






BibTex format

author = {Jackson, G and Qin, Z and mccann, J},
publisher = {IEEE},
title = {Long term sensing via battery health adaptation},
url = {},

RIS format (EndNote, RefMan)

AB - Energy Neutral Operation (ENO) has created theability to continuously operate wireless sensor networks inareas such as environmental monitoring, hazard detection andindustrial IoT applications. Current ENO approaches utilisetechniques such as sample rate control, adaptive duty cycling anddata reduction methods to balance energy generation, storage andconsumption. However, the state of the art approaches makes astrong and unrealistic assumption that battery capacity is fixedthroughout the deployment time of an application. This resultsin scenarios where ENO systems over allocate sensing tasks,therefore as battery capacity degrades it causes the system tono longer be energy neutral and then fail unexpectedly. In thispaper, we formulate the problem to maximise the quality-ofservicein terms of duty cycle and the battery capacity to extendthe deployment lifetime of a sensing application. In addition, wedevelop a lightweight algorithm to solve the formulated problem.Moreover, we evaluate the proposed method using real sensorenergy consumption data captured from micro-climate sensorsdeployed in Queen Elizabeth Olympic Park, London. Resultsshow that a 307% extension of deployment lifetime can beachieved when compared to a traditional ENO solution withouta reduction in the duty cycle of the sensor.
AU - Jackson,G
AU - Qin,Z
AU - mccann,J
TI - Long term sensing via battery health adaptation
UR -
ER -