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BERA: a biogeography-based energy saving routing architecture for wireless sensor networks

Authors
  • Lalwani, Praveen1
  • Banka, Haider1
  • Kumar, Chiranjeev1
  • 1 Indian Institute of Technology (ISM), Dhanbad, India , Dhanbad (India)
Type
Published Article
Journal
Soft Computing
Publisher
Springer-Verlag
Publication Date
Nov 16, 2016
Volume
22
Issue
5
Pages
1651–1667
Identifiers
DOI: 10.1007/s00500-016-2429-y
Source
Springer Nature
Keywords
License
Yellow

Abstract

Biogeography-based optimization (BBO) is a relatively new paradigm for optimization which is yet to be explored to solve complex optimization problems to prove its full potential. In wireless sensor networks (WSNs), optimal cluster head selection and routing are two well-known optimization problems. Researchers often use hierarchal cluster-based routing, in which power consumption of cluster heads (CHs) is very high due to its extra functionalities such as receiving and aggregating the data from its member sensor nodes and transmitting the aggregated data to the base station (BS). Therefore, proper care should be taken while selecting the CHs to enhance the life of the network. After formation of the clusters, data to be routed to the BS in inter-cluster fashion for further enhancing the life of WSNs. In this paper, a biogeography-based energy saving routing architecture (BERA) is proposed for CH selection and routing. The biogeography-based CH selection algorithm is proposed with an efficient encoding scheme of a habitat and by formulating a novel fitness function that uses residual energy and distance as its metrics. The BBO-based routing algorithm is also proposed. The efficient encoding scheme of a habitat is developed, and its fitness function considers the node degree in addition to residual energy and distance. To exhibit the performance of BERA, it is extensively tested with some existing routing algorithms such as DHCR, Hybrid routing, EADC and some bio-inspired algorithms, namely GA and PSO. Simulation results confirm the superiority/competitiveness of the proposed algorithm over existing techniques.

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