Data Accuracy Models under Spatio - Temporal Correlation with Adaptive Strategies in Wireless Sensor Networks

Wireless sensor nodes continuously observe and sense statistical data from the physical environment. But what degree of accurate data sensed by the sensor nodes collaboratively is a big issue for wireless sensor networks. Hence in this paper, we describe accuracy models of sensor networks for collecting accurate data from the physical environment under two conditions. First condition: we propose accuracy model which requires a priori knowledge of statistical data of the physical environment called Estimated Data Accuracy (EDA) model. Simulation results shows that EDA model can sense more accurate data from the physical environment than the other information accuracy models in the network. Moreover using EDA model, there exist an optimal set of sensor nodes which are adequate to perform approximately the same data accuracy level achieve by the network. Finally we simulate EDA model under the thread of malicious attacks in the network due to extreme physical environment. Second condition: we propose another accuracy model using Steepest Decent method called Adaptive Data Accuracy (ADA) model which doesn’t require any a priori information of input signal statistics. We also show that using ADA model, there exist an optimal set of sensor nodes which measures accurate data and are sufficient to perform the same data accuracy level achieve by the network. Further in ADA model, we can reduce the amount of data transmission for these optimal set of sensor nodes using a model called SpatioTemporal Data Prediction (STDP) model. STDP model captures the spatial and temporal correlation of sensing data to reduce the communication overhead under data reduction strategies. Finally using STDP model, we illustrate a mechanism to trace the malicious nodes in the network under extreme physical environment. Computer simulations illustrate the performance of EDA, ADA and STDP models respectively.