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:2017:10.1109/ICDCS.2017.136,
author = {Jackson, G and Qin, Z and mccann, J},
doi = {10.1109/ICDCS.2017.136},
pages = {2240--2245},
publisher = {IEEE},
title = {Long term sensing via battery health adaptation},
url = {http://dx.doi.org/10.1109/ICDCS.2017.136},
year = {2017}
}

RIS format (EndNote, RefMan)

TY  - CPAPER
AB - Energy Neutral Operation (ENO) has created the ability to continuously operate wireless sensor networks in areas such as environmental monitoring, hazard detection and industrial IoT applications. Current ENO approaches utilise techniques such as sample rate control, adaptive duty cycling and data reduction methods to balance energy generation, storage and consumption. However, the state of the art approaches makes a strong and unrealistic assumption that battery capacity is fixed throughout the deployment time of an application. This results in scenarios where ENO systems over allocate sensing tasks, therefore as battery capacity degrades it causes the system to no longer be energy neutral and then fail unexpectedly. In this paper, we formulate the problem to maximise the quality-of-service in terms of duty cycle and the battery capacity to extend the deployment lifetime of a sensing application. In addition, we develop a lightweight algorithm to solve the formulated problem. Moreover, we evaluate the proposed method using real sensor energy consumption data captured from micro-climate sensors deployed in Queen Elizabeth Olympic Park, London. Results show that a 307% extension of deployment lifetime can be achieved when compared to a traditional ENO solution without a reduction in the duty cycle of the sensor.
AU - Jackson,G
AU - Qin,Z
AU - mccann,J
DO - 10.1109/ICDCS.2017.136
EP - 2245
PB - IEEE
PY - 2017///
SP - 2240
TI - Long term sensing via battery health adaptation
UR - http://dx.doi.org/10.1109/ICDCS.2017.136
UR - http://hdl.handle.net/10044/1/47862
ER -