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Energy Efficient Intelligent Routing in WSN using Dominant Genetic Algorithm

International Journal of Electrical and Computer Engineering (IJECE) · Vol. 10 No. 1 (2020)

DOI
10.11591/ijece.v10i1.pp500-511
ISSN
2088-8708, 2088-8708
Published
2020-02-01
Publisher
Institute of Advanced Engineering and Science
License
creativecommons.org/licenses/by-nc/4.0

Abstract

In the current era of wireless sensor network development, among the various challenging issues, the life enhancement has obtained the prime interest. Reason is clear and straight: the battery operated sensors do have limited period of life hence to keep the network active as much as possible, life of network should be larger. To enhance the life of the network, at different level different approaches has been applied, broadly defining the proper scheduling of sensors and defining the energy efficient communication. In this paper heuristic based energy efficient communication approch has applied. A new development in the Genetic algorithm has presented and called as Dominant Genetic algorithm to determine the optimum energy efficient routing path between sensor nodes and to define the optimal energy efficient trajectory for mobile data gathering node. Dominancy of high fitness solution has included in the Genetic algorithm because of its natural existence. The proposed solution has applied the connection oriented crossover and mutation operator to maintain the feasibility of generated solution. With various simulation experiments it has observed that proposed method not only has delivered the better solution but also very less number of iterations required as compared to conventional form of Genetic algorithm.

Collaboration network of the authors

No co-authors found in Melola yet.

This record comes from Crossref (what the publisher deposited), because the journal's own OAI-PMH endpoint could not be harvested.

Metadata source: Crossref API (cadangan, berdasarkan ISSN) https://api.crossref.org/journals/2088-8708/works, first harvested 27 Okt 2026, last seen 1 Nov 2026.
Metadata as oai_dc, converted from Crossref
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