HMM-based predictive model for enhancing data quality in WSN

Abstract

ABSTRACTWireless sensor network (WSN) has been widely used in the areas such as health care and industrial monitoring. However, WSN systems still suffer from inevitable problems of communication interference and data failure. In this paper, an improved Hidden Markov Model (HMM) is proposed to enhance the quality of WSN sensor data. This model can be used to recover the missing data and predict the upcoming data in order to improve the data integrity and reliability ultimately. K-means clustering is firstly used to group sensor data series on the basis of different patterns. Next, Particle Swarm Optimization is applied for optimizing HMM parameters, which is enhanced by a hybrid mutation strategy. Experiments on two real data-sets show that the proposed approach can outperform the baseline models (Naïve Bayes, Grey System, BP-Neural Network and Traditional HMM) on precision of both single-step prediction and multiple-step prediction. The results also demonstrate that the proposed approach can improve data quality of WSN significantly. The proposed model can be further extended for time series prediction in other fields.