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Soft Computing
Erschienen in: Soft Computing 22/2017

16.06.2016 | Methodologies and Application

Artificial bee colony metaheuristic for energy-efficient clustering and routing in wireless sensor networks

verfasst von: Palvinder Singh Mann, Satvir Singh

Erschienen in: Soft Computing | Ausgabe 22/2017

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Abstract

Swarm intelligence (SI)-based metaheuristics are well applied to solve real-time optimization problems of efficient node clustering and energy-aware data routing in wireless sensor networks. This paper presents another superior approach for these optimization problems based on an artificial bee colony metaheuristic. The proposed clustering algorithm presents an efficient cluster formation mechanism with improved cluster head selection criteria based on a multi-objective fitness function, whereas the routing algorithm is devised to consume minimum energy with least hop-count for data transmission. Extensive evaluation and comparison of the proposed approach with existing well-known SI-based algorithms demonstrate its superiority over others in terms of packet delivery ratio, average energy consumed, average throughput and network life.
Vorheriger Artikel Different distance measures for fuzzy linear regression with Monte Carlo methods
Nächster Artikel A comparative study on swarm intelligence for structure learning of Bayesian networks

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Metadaten
Titel
Artificial bee colony metaheuristic for energy-efficient clustering and routing in wireless sensor networks
verfasst von
Palvinder Singh Mann
Satvir Singh
Publikationsdatum
16.06.2016
Verlag
Springer Berlin Heidelberg
Erschienen in
Soft Computing / Ausgabe 22/2017
Print ISSN: 1432-7643
Elektronische ISSN: 1433-7479
DOI
https://doi.org/10.1007/s00500-016-2220-0

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