Multi-sensor spatio-temporal vector prediction history tree (V-PHT) model for error correction in Wireless Sensor Networks

Abstract

Wireless Sensor Networks (WSNs) have gained rapid popularity due to their deployment for critical applications such as defense, health care, agriculture, weather and tsunami monitoring etc. However, such sensor networks are fundamentally constrained by the data errors arising due to the harsh power constrained sensing environment. In this paper, we propose a novel multi-sensor vector prediction history tree (V-PHT) decision algorithm for error correction in a wireless sensor network (WSN). This scheme is based on the recently proposed prediction history tree (PHT) algorithm for model based error correction in WSNs. However, unlike the existing PHT model, which exclusively exploits the temporal correlation inherent in the narrowband sensor data, the proposed V-PHT model for sensor data correction exploits the joint spatial and temporal correlation in sensor data arising out of geographical proximity of the sensor nodes. Towards this end, an optimal multi-sensor spatio-temporal AR model is developed for predictive modeling of the sensor data. Further, employing the spatio-temporal correlation structure amongst the sensors, we develop a robust framework for optimal estimation of the multi-sensor AR predictor model. Simulation results obtained employing sensor data models available in literature demonstrate that the proposed spatio-temporal V-PHT model for error correction in a WSN results in a significant reduction in mean-squared error (MSE) compared to the existing PHT scheme which exploits only temporal correlation.