Energy Aware Node Selection for Cluster-based Data Accuracy Estimation in Wireless Sensor Networks

The main objective of this paper is to reduce the number of sensor nodes by estimating a trade off between data accuracy and energy consumption for selecting nodes in probabilistic approach in a distributed network. Observed data are highly correlated among sensor nodes in the spatial domain due to deployment of high density of sensor nodes. These sensor nodes form non-overlapping distributed clusters due to high data correlation among them. The authors develop a probabilistic model for each distributed cluster to perform data accuracy and energy consumption model in the network. Finally they find a trade off between data accuracy and energy consumption model to select an optimal number of sensor nodes in each distributed cluster. The authors also compare the performance for data accuracy estimation model with information accuracy model for each distributed cluster in the network. The authors conclude that they find the optimal number of sensor nodes using the trade off between data accuracy and energy consumption in probabilistic approach in each distributed cluster.