Imperial College London

ProfessorJulieMcCann

Faculty of EngineeringDepartment of Computing

Professor of Computer Systems
 
 
 
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Contact

 

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

 
 
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Location

 

258ACE ExtensionSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@inproceedings{Jackson,
author = {Jackson, G and Qin, Z and mccann, J},
publisher = {IEEE},
title = {Long term sensing via battery health adaptation},
url = {http://hdl.handle.net/10044/1/47862},
}

RIS format (EndNote, RefMan)

TY  - CPAPER
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
PB - IEEE
TI - Long term sensing via battery health adaptation
UR - http://hdl.handle.net/10044/1/47862
ER -