Applications of Hidden Markov Model in Wireless Sensor Network

WSN is now widely used, due to its cost effectiveness and ease of deployment. Traditional sensor network approaches fail to respond dynamically; hence machine learning can be new techniques to be explored. The benefit of machine learning is that they can self learn, without human intervention or reprogramming. A concise introduction of classification of Machine learning algorithm is discussed. A review of one of the most promising technique Hidden Markov Model is covered. In recent decades, Hidden Markov model is being explored as the new technique for Wireless Sensor Networks. Hidden Markov model can efficiently perform time series prediction with generalization. This work emphasizes on recent applications of Hidden Markov Model to solve various issues and challenges in Wireless Sensor Networks.