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DC Field | Value | Language |
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dc.contributor.author | Kumar, P. | |
dc.contributor.author | Chaturvedi, A. | |
dc.date.accessioned | 2020-03-31T08:46:01Z | - |
dc.date.available | 2020-03-31T08:46:01Z | - |
dc.date.issued | 2015 | |
dc.identifier.citation | AEU - International Journal of Electronics and Communications, 2015, Vol.69, 7, pp.1058-1069 | en_US |
dc.identifier.uri | http://idr.nitk.ac.in/jspui/handle/123456789/13490 | - |
dc.description.abstract | Rapid advances and the development, compactness and economic viability; in IC technology, network hardware components and associated software have completely change the networking paradigm. The wireless sensor networks (WSNs) have also been not isolated from this unexpected changeover. This paper addresses three principal aspects that have been of interest in the WSN researcher community. These are investigating the suitable cluster formation scheme from some prominent scheme, incorporating the Spatio-temporal aspects of random query generation and subsequently model it using appropriate and extensively used probabilistic distribution functions, and exploring the importance of sink node(s) attributes towards much better energy profile of the WSN, as the energy consumption have been a vital component in deciding the overall network service conditions. The integration of these three aspects led to various case studies, which principally involves, uses of SKM, SFCM, DKM and DFCM as clustering schemes, uniform and Poisson probability mass functions uses to mathematically model the Spatio-temporal dependence of query distribution pattern, and the network surveillance by a single stationary sink, a moveable sink and four stationary sinks. The simulation results of various case studies are analyzed and compared. 2015 Elsevier GmbH. | en_US |
dc.title | Sink attributes analysis for energy efficient operations of wireless sensor networks under randomly varying temporal and spatial aspects of query generation | en_US |
dc.type | Article | en_US |
Appears in Collections: | 1. Journal Articles |
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