An Approach to Find Optimal Locations for Base Station to Achieve Energy Efficiency in Wireless Sensors Network

Authors

  • Maruthi H C Department of Electronics and Communication Engineering, BMS College of Engineering, Bengaluru, India
  • Poornima G Department of Electronics and Communication Engineering, BMS College of Engineering, Bengaluru, India

DOI:

https://doi.org/10.5281/zenodo.10628289

Keywords:

Cluster, Multiple Base Stations, Network Life- time, Relocation, Uneven Distribution, Wireless Sensors

Abstract

Due to the difficulty of recharging the batteries of Sensor Nodes (SNs), which have a finite amount of energyresources, energy efficiency is one of the crucial factors to take into account in Wireless Sensor Networks (WSNs). Base Station (BS) location in WSN plays a significant role as it is one of the deciding factor on network performance. Byplacing the BS at optimal location, it can increase the WSN’s energy efficiency and network coverage. Placement of theBS at optimal location is complex problem and it requires balance between coverage of the network and energy efficiency. In this work we have used Low Energy Adaptive Clustering Hierarchy (LEACH) protocol as routingalgorithm. We placed BS at the center of each of the quadrant of square shaped deployment area as well as at thecenter of entire deployment area. We evaluated network longevity as a measure of performance, and we observed how much energy was used for each round. Results indicate that location of the BS is optimal if the placement of BSconnects the farthest SN with shorter distance until First Node Dies (FND), stays near where there are more number of SNs situated until Half Node Dies (HND) and at the center of the deployment area in our case until Last Node Dies(LND) compared with other locations.

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Published

2024-02-08

How to Cite

Maruthi H C, & Poornima G. (2024). An Approach to Find Optimal Locations for Base Station to Achieve Energy Efficiency in Wireless Sensors Network. International Journal of Human Computations & Intelligence, 3(1), 309–317. https://doi.org/10.5281/zenodo.10628289