Energy efficient clustering and routing algorithms for wireless sensor networks: Particle swarm optimization approach

Energy efficient clustering and routing are two well known optimization problems which have been studied widely to extend lifetime of wireless sensor networks (WSNs). This paper presents Linear/Nonlinear Programming (LP/NLP) formulations of these problems followed by two proposed algorithms for the...

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Vydáno v:Engineering applications of artificial intelligence Ročník 33; s. 127 - 140
Hlavní autoři: Kuila, Pratyay, Jana, Prasanta K.
Médium: Journal Article
Jazyk:angličtina
Vydáno: Elsevier Ltd 01.08.2014
Témata:
Algorithms
Clustering
Computer networks
Energy management
Gateways
Network life
Particle swarm optimization
Remote sensors
Routing
Routing (telecommunications)
Swarm intelligence
Wireless networks
Wireless sensor networks
Network life
Wireless sensor networks
Routing
Clustering
Particle swarm optimization
Gateways
ISSN:0952-1976, 1873-6769
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Shrnutí:Energy efficient clustering and routing are two well known optimization problems which have been studied widely to extend lifetime of wireless sensor networks (WSNs). This paper presents Linear/Nonlinear Programming (LP/NLP) formulations of these problems followed by two proposed algorithms for the same based on particle swarm optimization (PSO). The routing algorithm is developed with an efficient particle encoding scheme and multi-objective fitness function. The clustering algorithm is presented by considering energy conservation of the nodes through load balancing. The proposed algorithms are experimented extensively and the results are compared with the existing algorithms to demonstrate their superiority in terms of network life, energy consumption, dead sensor nodes and delivery of total data packets to the base station. •Two algorithms are presented, i.e., PSO based routing and clustering for wireless sensor networks.•The former builds a trade-off between transmission distance and hop-count.•The clustering algorithm balances energy consumption of the CHs.•Experimental results demonstrate the superiority over existing algorithms.
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ISSN:0952-1976
1873-6769
DOI:10.1016/j.engappai.2014.04.009