Optimal Node Selection Using Estimated Data Accuracy Model in Wireless Sensor Networks
One of the major task of wireless sensor network is to sense accurate data from the physical environment. Hence in this paper, the authors develop an estimated data accuracy model for randomly deployed sensor nodes which can sense more accurate data from the physical environment. They compare their results with other information accuracy models and shows that their estimated data accuracy model performs better than the other models. Moreover, they simulate their estimated data accuracy model under such situation when some of the sensor nodes become malicious due to extreme physical environment. Finally using their estimated data accuracy model they construct a probabilistic approach for selecting an optimal set of sensor nodes from the randomly deployed maximal set of sensor nodes in the network.