Abstract
A Wireless Sensor Network (WSN) comprises a collection of nodes, which has the characteristic of limited resources. Clustering is a strategy for introducing spatial bandwidth reuse and controlling routing issues. The network re-cluster and maintains the current information if each node is unavailable due to a malfunction. In this paper, we propose a Routing-based Restricted Boltzmann Machine learning and Clustering Algorithm (RBMCA). It is the type of clustering algorithm that extends the network lifetime by electing the cluster head selection. The data redundancy is reduced using a hidden and visible layer. Additionally, the reward points are based on the residual energies and the dynamic monitoring of their energy consumption that diminishes their count of cluster members or gives up their role. The performance evaluation for the proposed clustering model and compared results for the related clustering approach Improved Particle Swarm Optimization (IPSO) and Energy-Efficient Centroid-based Routing Protocol (EECRP) algorithms and prove the proposed method gives the best performance.
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Revathi, A., Santhi, S.G. (2023). Routing-Based Restricted Boltzmann Machine Learning and Clustering Algorithm in Wireless Sensor Network. In: Noor, A., Saroha, K., Pricop, E., Sen, A., Trivedi, G. (eds) Proceedings of Emerging Trends and Technologies on Intelligent Systems. Advances in Intelligent Systems and Computing, vol 1414. Springer, Singapore. https://doi.org/10.1007/978-981-19-4182-5_28
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DOI: https://doi.org/10.1007/978-981-19-4182-5_28
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