Simulation of AI based PSO algorithm in WSN

  • Authors

    • Renuka C Herakal Sri Venkateshwara College of Engineering, Bangalore
    • Suresha Talanki
    2019-03-12
    https://doi.org/10.14419/ijet.v7i4.27407
  • Wireless Sensor Network, Computation Time, Particle Swarm Optimization, Energy Consumption, Artificial Intelligence.
  • Abstract

    In the evolving technologies, the Wireless Sensor Network (WSN) has perceived tremendous influences in the past two decades for rectifying the problems of energy immorality to certify the rate of energy consumption of nodes in dynamic deployment and the average computation time. It has been analyzed that, the Particle Swarm Optimization (PSO) algorithm is deliberated as one of the best suited algorithms for Dynamic Deployment of nodes in WSN. It is also examined that, the PSO algorithm doesn’t have dynamic support in sustaining the rate of energy consumption limit and moderate computation time. Hence, this work presents a novel idea where the rate of energy consumption in dynamic deployment of nodes and average computation time of information is reduced by applying the Artificial Intelligence (AI) technique to the existing PSO algorithm. This paper also discusses the complete algorithm and its architecture based on AI.

     

     

     


  • References

    1. [1] Renuka C. Herakal, et.all, “Simulation of AI Based ABC Algorithm forEnergy Efficiency In WSNâ€, International Journal of Pure and Applied Mathematics, Volume 120 No. 6 2018, 11579-11591, ISSN: 1314-3395 (on-line version).

      [2] C. Vimalarani,et. all, “An Enhanced PSO-Based Clustering Energy Optimization Algorithm for Wireless Sensor Networkâ€, Hindawi Publishing Corporation, The Scientific World Journal, Volume 2016, Article ID 8658760, 11 pages.

      [3]

      Dan Li et. all, “An Improved PSO Algorithm for Distributed Localization in Wireless Sensor Networksâ€, Hindawi Publishing Corporation International Journal of Distributed Sensor Networks, Volume 2015, Article ID 970272, 8 pages.

      [4] J. Kennedy and R. Eberhart, “Particle swarm optimization,†inProceedings of the IEEE International Conference on Neural Networks,vol. 4, 27 Nov.–1 Dec. 1995, pp. 1942–1948.

      [5] Yubin Xu et. all, “A Clustering Algorithm of Wireless Sensor Networks Based on PSOâ€, H. Deng et al. (Eds.): AICI 2011, Part I, LNAI 7002, pp. 187–194, 2011. © Springer-Verlag Berlin Heidelberg.

      [6] Raghavendra V. Kulkarni et. all, “Particle Swarm Optimization in Wireless-Sensor Networks: A Brief Surveyâ€, Article in IEEE Transactions on Systems Man and Cybernetics Part C (Applications and Reviews) · April 2011, https://doi.org/10.1109/TSMCC.2010.2054080.

      [7] Sang Jin Lee et. all, “A Threshold Determining Method for the Dynamic Filterâ€, IJCSNS International Journal of Computer Science and Network Security, VOL.8 No.4, April 2008.

      [8] Ran Bi et.all, “Optimizing Retransmission Threshold in WirelessSensor Networksâ€, An article in Sensors 2016, 16, 665; https://doi.org/10.3390/s16050665.

      [9] Li-Yu Hu,et.all,“The distance function effect on k-nearest neighbor classification for medical datasetsâ€, Springerplus. 2016; 5(1): 1304, Published online 2016 Aug 9. https://doi.org/10.1186/s40064-016-2941-7.

      [10] YacoubaOuattara,et.all,“Three Thresholds for the Efficiency in Energy Management in WSNâ€, Journal of Advances in Computer Networks, Vol. 3, No. 1, March 2015.

      [11] Eugene Shih, et.all,“Physical Layer Driven Protocol and Algorithm Design for Energy Efficient Wireless Sensor Networksâ€.

      [12] HosamRahhala, et.all, “A Novel Multi-Threshold Energy (MTE) Technique for WirelessSensor Networksâ€, Proceedings of International Conference on Communication, Management and Information Technology (ICCMIT 2015), ScienceDirect, Procedia Computer Science 65 (2015) 25 – 34.https://doi.org/10.1016/j.procs.2015.09.072.

      [13] Jun-Zhao Sun, et.all,“Multi-Threshold Based Data GatheringAlgorithms for Wireless Sensor Networksâ€, Journal Of Networks, Vol. 4, No. 1, February 2009.

      [14] Emad Alnawafa, et.all, “New Energy Efficient Multi-Hop Routing Techniques for Wireless Sensor Networks: Static and Dynamic Techniquesâ€, Sensors 2018, 18, 1863; https://doi.org/10.3390/s18061863.

      [15] https://www.quora.com/What-are-the-main-differences-between-artificial-intelligence-and-machine-learning-Is-machine-learning-a-part-of-artificial-intelligence.

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  • How to Cite

    Herakal, R. C., & Talanki, S. (2019). Simulation of AI based PSO algorithm in WSN. International Journal of Engineering & Technology, 7(4), 5132-5136. https://doi.org/10.14419/ijet.v7i4.27407

    Received date: 2019-02-13

    Accepted date: 2019-02-22

    Published date: 2019-03-12