Optimal Node Selection using Estimated Data Accuracy Model in Wireless Sensor Networks

Abstract: One of the major task of wireless sensor network is to sense accurate data from the physical environment. Hence in this paper, we develop an estimated data accuracy model for randomly deployed sensor nodes which can sense more accurate data from the physical environment. We compare our results with other information accuracy models and shows that our estimated data accuracy model performs better than the other models. Moreover we simulate our estimated data accuracy model under such situation when some of the sensor nodes become malicious due to extreme physical environment. Finally using our estimated data accuracy model we construct a probabilistic approach for selecting an optimal set of sensor nodes from the randomly deployed maximal set of sensor nodes in the network.