Neural Computing and Applications

, Volume 31, Supplement 1, pp 47–62 | Cite as

DECSA: hybrid dolphin echolocation and crow search optimization for cluster-based energy-aware routing in WSN

  • N. MaheshEmail author
  • S. Vijayachitra
S.I. : Machine Learning Applications for Self-Organized Wireless Networks


Data clustering in wireless sensor network (WSN) is a prominent research area that ensures effective communication through satisfying the energy constraint. The traditional methods engaged themselves in collecting the data from the remote area using WSNs and communicating the data in such a way to enhance the lifetime of the network. However, the energy constraints are not met by the available methods in the literature. The paper concentrates on the hybrid optimization algorithm to tackle the cluster head selection optimally so as to assure the effective communication and energy-aware routing in WSNs. The hybrid optimization algorithm, named dolphin echolocation-based crow search algorithm, is the integration of dolphin echolocation algorithm and crow search algorithm such that the hybrid optimization assures the selection of cluster heads based on the multi-constraints effectively and with high convergence rate. The energy-aware routing is initiated in WSN using the proposed algorithm. Simulation is progressed in the WSN environment using 50, 75, and 100 nodes, and the proposed algorithm offered a better network lifetime with energy remaining in the node to be 0.0476 with 33 alive nodes at the end of 200 rounds.


WSN Cluster head selection Energy-aware routing Hybrid optimization CSA 


Compliance with ethical standards

Conflict of interest

There are no conflicts of interest for authors to publish their article in the journal.


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Copyright information

© The Natural Computing Applications Forum 2018

Authors and Affiliations

  1. 1.Department of Electronics and Instrumentation EngineeringKongu Engineering CollegeErodeIndia

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