An energy optimization in wireless sensor networks by using genetic algorithm

Sunil Kr. Jha () and Egbe Michael Eyong
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Sunil Kr. Jha: University of Information Technology and Management in Rzeszow
Egbe Michael Eyong: University of Information Science and Technology “St. Paul the Apostle”

Telecommunication Systems: Modelling, Analysis, Design and Management, 2018, vol. 67, issue 1, No 9, 113-121

Abstract: Abstract Wireless sensor networks (WSNs) are used for several commercial and military applications, by collecting, processing and distributing a wide range of data. Maximizing the battery life of WSNs is crucial in improving the performance of WSN. In the present study, different variations of genetic algorithm (GA) method have been implemented independently on energy models for data communication of WSNs with the objective to find out the optimal energy $$\hbox {(E)}$$ (E) consumption conditions. Each of the GA methods results in an optimal set of parameters for minimum energy consumption in WSN related to the type of selected energy model for data communication, while the best performance of the GA method [energy consumption $$(\hbox {E}=3.49\times 10^{-4}\,\hbox {J})$$ ( E = 3.49 × 10 - 4 J ) ] is obtained in WSN for communication distance (d) $${\ge }87\,\hbox {m}$$ ≥ 87 m in between the sensor cluster head and a base station.

Keywords: Wireless sensor network; Genetic algorithm; Data communication; Energy optimization (search for similar items in EconPapers)
Date: 2018
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Citations: View citations in EconPapers (4)

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DOI: 10.1007/s11235-017-0324-1

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