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International Journal of Advanced Computer Science and Applications(IJACSA), Volume 10 Issue 2, 2019.
Abstract: Wireless Sensor Networks (WSN) is becoming a crucial component of most of the fields of engineering. Heterogeneous WSN (HWSN) is characterized by wireless sensor nodes having link (communication), computation or energy heterogeneity for a specific application. WSN applications are constrained by the availability of power hence; conserving energy in a sensor network becomes a major challenge. Literature survey shows that node deployments can have good impact on energy conservation. Works show that self-adaptable nodes can significantly save energy as compared to other types of deployment. This work uses the concept of self-adaptation of nodes to conserve energy in a HWSN. A deployment strategy driven by some dynamic decision making capability can boost the overall performance of a WSN. The work presents an analysis of three types of deployments: like keeping all nodes fixed, all node moving and high energy nodes moving with respect to throughput, delay and energy consumption. Experimental results show that self-adaptable dynamic deployment gives 10% better throughput and 6% better energy conservation than static deployment strategies.
Umesh M. Kulkarni, Harish H. Kenchannavar and Umakant P. Kulkarni, “Self Adaptable Deployment for Heterogeneous Wireless Sensor Network” International Journal of Advanced Computer Science and Applications(IJACSA), 10(2), 2019. http://dx.doi.org/10.14569/IJACSA.2019.0100221
@article{Kulkarni2019,
title = {Self Adaptable Deployment for Heterogeneous Wireless Sensor Network},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2019.0100221},
url = {http://dx.doi.org/10.14569/IJACSA.2019.0100221},
year = {2019},
publisher = {The Science and Information Organization},
volume = {10},
number = {2},
author = {Umesh M. Kulkarni and Harish H. Kenchannavar and Umakant P. Kulkarni}
}
Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.