Design and Simulation of AI Based Dynamic Deployment Algorithm for WSN using NS3
Renuka C. Herakal1, Suresha Talanki2
1Renuka C. Herakal*, Department of Computer Science & Engineering, Sri Venkateshwara College of Engineering, Bangalore, Karnataka, India.
2Suresha Talanki, Principal, Sri Venkateshwara College of Engineering, Bangalore, Karnataka, India.
Manuscript received on January 05, 2020. | Revised Manuscript received on January 25, 2020. | Manuscript published on January 30, 2020. | PP: 4777-4780 | Volume-8 Issue-5, January 2020. | Retrieval Number: E6809018520/2020©BEIESP | DOI: 10.35940/ijrte.E6809.018520
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Currently the Wireless Sensor Network (WSN) is considered as one among the interesting and emerging research domains. The software and hardware capabilities of sensor nodes in WSN have improved the technology and supported to recognise WSN as one of the motivating and stimulating domains. The implementation and adoption of best dynamic deployment techniques in the application of WSNs have been specified as efficient and well-organised solutions in order to enhance the performance of WSN. The existing dynamic deployment algorithms are reviewed and ensured that there is more scope for the enhancement and improvement in terms of solving the constraints related with the rate of energy consumption and performance of WSN. In this research work, a new dynamic deployment algorithm based on Machine Learning (ML) concepts named as Cluster Head Energy Optimizer (CHEO) is designed and implemented in an urge of enhancing the performance of WSN. The results and conclusion of this research validates the performance of WSN by considering the parameters such as, energy efficiency, area of coverage, rate of data transmission and number of deployed nodes in the selected area of application. The result is compared with the existing dynamic deployment algorithms and concludes that, new algorithm yields the better result than those of existing deployment algorithms.
Keywords: Wireless Sensor Network, Dynamic deployment, Machine Learning, energy efficiency, Area of coverage, Data transmission.
Scope of the Article: Energy Harvesting and Transfer for Wireless Sensor Networks.