Abstract
The network is composed of wireless distributed sensor nodes with data computational capabilities. In each cluster, the cluster member nodes are used to send the sensed data to their respective cluster head to aggregate and classify the data effectively. In this work, the algorithm for cluster-based aggregation of data using the Naive Bayesian Classifier is proposed. The proposed scheme provides better performance rather than existing algorithms with accuracy, efficient energy utilization, and computation overhead.
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Bhajantri, L.B., Kumbar, B.G. (2021). Cluster-Based Data Aggregation in Wireless Sensor Networks: A Bayesian Classifier Approach. In: Suma, V., Bouhmala, N., Wang, H. (eds) Evolutionary Computing and Mobile Sustainable Networks. Lecture Notes on Data Engineering and Communications Technologies, vol 53. Springer, Singapore. https://doi.org/10.1007/978-981-15-5258-8_89
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DOI: https://doi.org/10.1007/978-981-15-5258-8_89
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