Abstract
Wireless Sensor Networks (WSNs) find broad range of adoration and wide spread uses in various applications. In such networks, the deployed sensors trap and transfer the data intellectually to the base station that is the ultimate destination. This paper briefs about the various applications of machine learning in sensor networks. The Principal Component Analysis (PCA) along with k-means clustering strategies, used in case of unsupervised learning, are discussed. A discussion on different functional challenges occurring in case of sensor networks is also presented.
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