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Design of Efficient Routing Paths Using Similarity Estimation Based Stochastic Gradient Descent in Wireless Sensor Network


Affiliations
1 Department of Computer Science and Engineering, Kings Engineering College, India
     

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Wireless Sensor Networks (WSNs) offer versatile deployment options, particularly in battery-powered scenarios, addressing energy consumption concerns among sensor nodes. However, the data-intensive nature of WSNs poses challenges in routing, particularly in maintaining balanced paths while accommodating rapid data acquisition. This paper presents an innovative approach called Similarity Estimation-Based Stochastic Gradient Descent (SESGD) routing for WSNs, designed to establish stable routing paths that align with the speed of data acquisition. Sensor nodes play a crucial role in data collection and acquisition, while WSNs facilitate data routing through multiple hops from source to sink nodes. SESGD effectively manages data routing, synchronizing it with data acquisition rates, thereby ensuring network stability. Simulation results assess key performance metrics, including average delay, throughput, and network energy efficiency. The findings demonstrate that the proposed machine learning method outperforms existing algorithms, achieving superior network throughput.

Keywords

Machine Learning, WSN, Stochastic Gradient, Routing, Energy Efficiency.
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  • R. Sabitha and V. Saravanan, “Network Based Detection of IoT Attack using AIS-IDS Model”, Wireless Personal Communications, Vol. 128, No. 3, pp. 1543-1566, 2023.

  • T. Karthikeyan and K. Praghash, “Improved Authentication in Secured Multicast Wireless Sensor Network (MWSN) using Opposition Frog Leaping Algorithm to Resist Man-in-Middle Attack”, Wireless Personal Communications, Vol. 123, No. 2, pp. 1715-1731, 2022.

  • K. Praghash and A.A. Stonier, “An Artificial Intelligence Based Sustainable Approaches-IoT Systems for Smart Cities”, Springer, 2023

  • H. AlAamri, M. Abolhasan and T. Wysocki, “On Optimizing Route Discovery in Absence of Previous Route Information in MANETs”, Proceedings of IEEE International Conference on Vehicular Technology, pp. 1-5, 2009.

  • X.M. Zhang, E.B. Wang, J.J. Xia and D.K. Sung, “An Estimated Distance based Routing Protocol for Mobile Ad Hoc Networks”, IEEE Transactions on Vehicular Technology, Vol. 60, No. 7, pp. 3473-3484, 2011.

  • Y. Liu, K. Ota, K. Zhang, M. Ma and N. Xiong, “QTSAC: An Energy-Efficient MAC Protocol for Delay Minimization in Wireless Sensor Networks”, IEEE Access, Vol. 6, pp. 8273-8291, 2018.

  • X. Deng, Z. Tang, L.T. Yang, M. Lin and B. Wang, “Confident Information Coverage Hole Healing in Hybrid Industrial Wireless Sensor Networks”, IEEE Transactions on Industrial Informatics, Vol. 14, No. 5, pp. 2220-2229, 2017.

  • L. Kong, J.S. Pan and V. Snasel, “An Energy-Aware Routing Protocol for Wireless Sensor Network based on Genetic Algorithm”, Telecommunication Systems, Vol. 67, No. 3, pp. 451-463, 2018.

  • A. Agrawal, V. Singh, S. Jain and R.K. Gupta, “GCRP: Grid-Cycle Routing Protocol for Wireless Sensor Network with Mobile Sink”, AEU-International Journal of Electronics and Communications, Vol. 94, No. 2, pp. 1-11, 2018.

  • T.E. Bogale and L. Vandendorpe, “Max-Min SNR Signal Energy based Spectrum Sensing Algorithms for Cognitive Radio Networks with Noise Variance Uncertainty”, IEEE Transactions on Wireless Communications, Vol. 13, No. 1, pp. 280-290, 2014.

  • D.M. Martinez and A.G. Andrade, “On the Reduction of the Noise Uncertainty Effects in Energy Detection for Spectrum Sensing in Cognitive Radios”, Proceedings of IEEE Annual International Symposium Personal, Indoor and Mobile Radio Communications, pp. 1975-1979, 2014.

  • K.M. Thilina, K.W. Choi, N. Saquib and E. Hossain, “Pattern Classification Techniques for Cooperative Spectrum Sensing in Cognitive Radio Networks: SVM and W-KNN Approaches”, Proceedings of IEEE International Conference on Global Communications, pp. 1260-1265, 2012.

  • Soumitra Das and Sanjeev Wagh, “Prolonging the Lifetime of the Wireless Sensor Network Based on Blending of Genetic Algorithm and Ant Colony Optimization”, Journal of Green Engineering, Vol. 4, No. 3, pp. 245-260, 2015.

  • Mehr Moslem Afrashteh, “Design and Implementation a New Energy Efficient Clustering Algorithm using Genetic Algorithm for Wireless Sensor Networks”, World Academy of Science, Engineering and Technology, Vol. 5, No. 4, pp. 373-376, 2011.


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  • Design of Efficient Routing Paths Using Similarity Estimation Based Stochastic Gradient Descent in Wireless Sensor Network

Abstract Views: 135  |  PDF Views: 1

Authors

S. Thumilvannan
Department of Computer Science and Engineering, Kings Engineering College, India
B. Yuvaraj
Department of Computer Science and Engineering, Kings Engineering College, India
C. Srivenkateswaran
Department of Computer Science and Engineering, Kings Engineering College, India
V. Balammal
Department of Computer Science and Engineering, Kings Engineering College, India

Abstract


Wireless Sensor Networks (WSNs) offer versatile deployment options, particularly in battery-powered scenarios, addressing energy consumption concerns among sensor nodes. However, the data-intensive nature of WSNs poses challenges in routing, particularly in maintaining balanced paths while accommodating rapid data acquisition. This paper presents an innovative approach called Similarity Estimation-Based Stochastic Gradient Descent (SESGD) routing for WSNs, designed to establish stable routing paths that align with the speed of data acquisition. Sensor nodes play a crucial role in data collection and acquisition, while WSNs facilitate data routing through multiple hops from source to sink nodes. SESGD effectively manages data routing, synchronizing it with data acquisition rates, thereby ensuring network stability. Simulation results assess key performance metrics, including average delay, throughput, and network energy efficiency. The findings demonstrate that the proposed machine learning method outperforms existing algorithms, achieving superior network throughput.

Keywords


Machine Learning, WSN, Stochastic Gradient, Routing, Energy Efficiency.

References