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dc.contributor.authorRauniyar, Ashish
dc.contributor.authorKunwar, Jeevan
dc.contributor.authorHaugerud, Hårek
dc.contributor.authorYazidi, Anis
dc.contributor.authorEngelstad, Paal E.
dc.date.accessioned2020-02-21T10:47:41Z
dc.date.accessioned2020-02-24T14:44:47Z
dc.date.available2020-02-21T10:47:41Z
dc.date.available2020-02-24T14:44:47Z
dc.date.issued2019
dc.identifier.citationRauniyar A, Kunwar J, Haugerud H, Yazidi A, Engelstad P.E.: Energy Efficient Target Coverage in Wireless Sensor Networks Using Adaptive Learning. In: Jemili, Mosbah. Distributed Computing for Emerging Smart Networks, 2019. Springer Nature p. 133-147en
dc.identifier.isbn978-3-030-40130-6
dc.identifier.issn1865-0929
dc.identifier.issn1865-0929
dc.identifier.issn1865-0937
dc.identifier.urihttps://hdl.handle.net/10642/8165
dc.description.abstractOver the past few years, innovation in the development of Wireless Sensor Networks (WSNs) has evolved rapidly. WSNs are being used in many application fields such as target coverage, battlefield surveillance, home security, health care supervision, and many more. However, power usage in WSNs remains a challenging issue due to the low capacity of batteries and the difficulty of replacing or charging them, especially in harsh environments. Therefore, this has led to the development of various architectures and algorithms to deal with optimizing the energy usage of WSNs. In particular, extending the lifetime of the WSNin the context of target coverage problems by resorting to intelligent scheduling has received a lot of research attention. In this paper, we propose a scheduling technique for WSN based on a novel concept within the theory of Learning Automata (LA) called pursuit LA. Each sensor node in the WSN is equipped with an LA so that it can autonomously select its proper state, i.e., either sleep or active with the aim to coverall targets with the lowest energy cost. Through comprehensive experimental testing, we verify the efficiency of our algorithm and its ability to yield a near-optimal solution. The results are promising, given the low computational footprint of the algorithm.en
dc.language.isoenen
dc.publisherSpringer Verlagen
dc.relation.ispartofDistributed Computing for Emerging Smart Networks; First International Workshop, DiCES-N 2019, Hammamet, Tunisia, October 30, 2019, Revised Selected Papers
dc.relation.ispartofseriesCommunications in Computer and Information Science;volume 1130
dc.rightsThis is a post-peer-review, pre-copyedit version of a book chapter published in Distributed Computing for Emerging Smart Networks; First International Workshop, DiCES-N 2019, Hammamet, Tunisia, October 30, 2019, Revised Selected Papers, which is part of the Communications in Computer and Information Science book series (CCIS, volume 1130). The final authenticated version is available online at: https://dx.doi.org/10.1007/978-3-030-40131-3_9repository.en
dc.subjectWireless sensor networksen
dc.subjectAdaptive learningen
dc.subjectLearning automataen
dc.subjectMinimum active sensorsen
dc.subjectTarget coverageen
dc.subjectEnergy efficiencyen
dc.titleEnergy Efficient Target Coverage in Wireless Sensor Networks Using Adaptive Learningen
dc.typeJournal articleen
dc.typePeer revieweden
dc.typeBook chapter
dc.date.updated2020-02-21T10:47:41Z
dc.description.versionacceptedVersionen
dc.identifier.doihttps://dx.doi.org/10.1007/978-3-030-40131-3_9
dc.identifier.cristin1786189
dc.source.isbn978-3-030-40131-3


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