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An energy optimization in wireless sensor networks by using genetic algorithm

Author

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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”)

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.

Suggested Citation

  • Sunil Kr. Jha & Egbe Michael Eyong, 2018. "An energy optimization in wireless sensor networks by using genetic algorithm," Telecommunication Systems: Modelling, Analysis, Design and Management, Springer, vol. 67(1), pages 113-121, January.
    • Handle: RePEc:spr:telsys:v:67:y:2018:i:1:d:10.1007_s11235-017-0324-1
    DOI: 10.1007/s11235-017-0324-1
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    References listed on IDEAS

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    1. Shaikh, Faisal Karim & Zeadally, Sherali, 2016. "Energy harvesting in wireless sensor networks: A comprehensive review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 55(C), pages 1041-1054.
    2. Gaddafi Abdul-Salaam & Abdul Hanan Abdullah & Mohammad Hossein Anisi & Abdullah Gani & Abdulhameed Alelaiwi, 2016. "A comparative analysis of energy conservation approaches in hybrid wireless sensor networks data collection protocols," Telecommunication Systems: Modelling, Analysis, Design and Management, Springer, vol. 61(1), pages 159-179, January.
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    Cited by:

    1. Sudhanshu Tiwari & Gaurav Kumar & Ayush Raj & Prateek & Rajeev Arya, 0. "Water cycle algorithm perspective on energy constraints in WSN," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 0, pages 1-8.
    2. Chandra Naik & Pushparaj D. Shetty, 2022. "FLAG: fuzzy logic augmented game theoretic hybrid hierarchical clustering algorithm for wireless sensor networks," Telecommunication Systems: Modelling, Analysis, Design and Management, Springer, vol. 79(4), pages 559-571, April.
    3. Hilary I. Okagbue & Muminu O. Adamu & Timothy A. Anake & Ashiribo S. Wusu, 2019. "Nature inspired quantile estimates of the Nakagami distribution," Telecommunication Systems: Modelling, Analysis, Design and Management, Springer, vol. 72(4), pages 517-541, December.
    4. Sudhanshu Tiwari & Gaurav Kumar & Ayush Raj & Prateek & Rajeev Arya, 2020. "Water cycle algorithm perspective on energy constraints in WSN," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 11(2), pages 253-260, April.

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