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Decentralized Detection in Energy Harvesting Wireless Sensor Networks
KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre. KTH, School of Electrical Engineering (EES), Signal Processing.ORCID iD: 0000-0003-1569-3527
KTH, School of Electrical Engineering (EES), Communication Theory. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.ORCID iD: 0000-0001-6682-6559
KTH, School of Electrical Engineering (EES), Signal Processing. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.ORCID iD: 0000-0001-6630-243X
2016 (English)In: 2016 24TH EUROPEAN SIGNAL PROCESSING CONFERENCE (EUSIPCO), IEEE conference proceedings, 2016, p. 567-571Conference paper, Published paper (Refereed)
Abstract [en]

We consider a decentralized hypothesis testing problem in which several peripheral energy harvesting sensors are arranged in parallel. Each sensor makes a noisy observation of a time varying phenomenon, and sends a message about the present hypothesis towards a fusion center at each time instance t. The fusion center, using the aggregate of the received messages during the time instance t, makes a decision about the state of the present hypothesis. We assume that each sensor is an energy harvesting device and is capable of harvesting all the energy it needs to communicate from its environment. Our contribution is to formulate and analyze the decentralized detection problem when the energy harvesting sensors are allowed to form a long term energy usage policy. Our analysis is based on a queuing-theoretic model for the battery. Then, by using numerical simulations, we show how the resulting performance differs from the energy-unconstrained case.

Place, publisher, year, edition, pages
IEEE conference proceedings, 2016. p. 567-571
Series
European Signal Processing Conference, ISSN 2076-1465
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
URN: urn:nbn:se:kth:diva-201278DOI: 10.1109/EUSIPCO.2016.7760312ISI: 000391891900111Scopus ID: 2-s2.0-85005959803ISBN: 978-0-9928-6265-7 (print)OAI: oai:DiVA.org:kth-201278DiVA, id: diva2:1074245
Conference
24th European Signal Processing Conference (EUSIPCO), AUG 28-SEP 02, 2016, Budapest, HUNGARY
Note

QC 20170215

Available from: 2017-02-15 Created: 2017-02-15 Last updated: 2017-02-15Bibliographically approved

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Tarighati, AllaGross, JamesJaldén, Joakim

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