dc.contributor.author | Rauniyar, Ashish | |
dc.contributor.author | Kunwar, Jeevan | |
dc.contributor.author | Haugerud, Hårek | |
dc.contributor.author | Yazidi, Anis | |
dc.contributor.author | Engelstad, Paal E. | |
dc.date.accessioned | 2020-02-21T10:47:41Z | |
dc.date.accessioned | 2020-02-24T14:44:47Z | |
dc.date.available | 2020-02-21T10:47:41Z | |
dc.date.available | 2020-02-24T14:44:47Z | |
dc.date.issued | 2019 | |
dc.identifier.citation | Rauniyar 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. 133-147 | en |
dc.identifier.isbn | 978-3-030-40130-6 | |
dc.identifier.issn | 1865-0929 | |
dc.identifier.issn | 1865-0929 | |
dc.identifier.issn | 1865-0937 | |
dc.identifier.uri | https://hdl.handle.net/10642/8165 | |
dc.description.abstract | Over 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.iso | en | en |
dc.publisher | Springer Verlag | en |
dc.relation.ispartof | Distributed Computing for Emerging Smart Networks;
First International Workshop, DiCES-N 2019, Hammamet, Tunisia, October 30, 2019, Revised Selected Papers | |
dc.relation.ispartofseries | Communications in Computer and Information Science;volume 1130 | |
dc.rights | This 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.subject | Wireless sensor networks | en |
dc.subject | Adaptive learning | en |
dc.subject | Learning automata | en |
dc.subject | Minimum active sensors | en |
dc.subject | Target coverage | en |
dc.subject | Energy efficiency | en |
dc.title | Energy Efficient Target Coverage in Wireless Sensor Networks Using Adaptive Learning | en |
dc.type | Journal article | en |
dc.type | Peer reviewed | en |
dc.type | Book chapter | |
dc.date.updated | 2020-02-21T10:47:41Z | |
dc.description.version | acceptedVersion | en |
dc.identifier.doi | https://dx.doi.org/10.1007/978-3-030-40131-3_9 | |
dc.identifier.cristin | 1786189 | |
dc.source.isbn | 978-3-030-40131-3 | |