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Clustering and Data Aggregation in Wireless Sensor Networks Using Machine Learning Algorithms

Wireless Sensor Networks (WSN) are resource constrained. Clustering and data aggregations are used to reduce the energy consumption in the network by decreasing the amount of data transmission. Machine Learning algorithms such as swarm intelligence, reinforcement learning, neural networks significantly reduce the amount of data transmission and use the distributive characteristics of the network. It provides a comparative analysis of the performance of different methods to help the designers for designing appropriate machine learning based solutions for clustering and data aggregation applications. This paper presents a literature review of different machine learning based methods which are used for clustering and data aggregation in WSN and proposes an improved similarity based clustering and data aggregation, which uses Independent Component Analysis (ICA).