Imperial College London
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ProfessorJulieMcCann

Faculty of Engineering, Department of Computing

Vice-Dean (Research) for the Faculty of Engineering
 
 
 
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Contact

 

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

 
 
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Assistant

 

Miss Teresa Ng +44 (0)20 7594 8300

 
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Location

 

260ACE ExtensionSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@inproceedings{Jackson:2018:10.1109/GLOCOM.2017.8254851,
author = {Jackson, G and Ciocoiu, S and McCann, JA},
doi = {10.1109/GLOCOM.2017.8254851},
publisher = {IEEE},
title = {Solar Energy Harvesting Optimization for Wireless Sensor Networks},
url = {http://dx.doi.org/10.1109/GLOCOM.2017.8254851},
year = {2018}
}
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RIS format (EndNote, RefMan)

TY  - CPAPER
AB  - The energy optimization of resource constrained energy harvesting Wireless Sensor Networks (WSN) have constituted a major research topic in recent years in areas such as environmental monitoring, hazard detection and industrial applications. Current approaches leverage techniques such as adaptive duty cycling, transmission power adaptation, and data reduction methods to minimize energy consumption. However, the majority of the state of the art approaches with WSN research assume that energy generation, although variable, is not controllable in-situ to optimize energy generation. In this paper, we design a low power, low cost, open source solar tracking mechanism for energy harvesting wireless sensors. Furthermore, we formulate the dynamic energy generation system as an optimization problem and from this design an adaptive, lightweight, distributed, prediction free algorithm to maximize the energy generation of the system. Moreover, we evaluate the proposed method using a combination of real trace-driven real solar data based simulation, comparison to a centralized globally optimum solution and real world experimentation. From our evaluation, an improvement of up to 165% in energy generation has been seen when compared to traditional tracking methodologies and that the lightweight distributed implementation is, on average, 99.1% as efficient as the globally optimum solution across 28 distinct testing scenarios.
AU  - Jackson,G
AU  - Ciocoiu,S
AU  - McCann,JA
DO  - 10.1109/GLOCOM.2017.8254851
PB  - IEEE
PY  - 2018///
SN  - 2334-0983
TI  - Solar Energy Harvesting Optimization for Wireless Sensor Networks
UR  - http://dx.doi.org/10.1109/GLOCOM.2017.8254851
UR  - http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000428054304123&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=1ba7043ffcc86c417c072aa74d649202
UR  - http://hdl.handle.net/10044/1/62253
ER  -
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