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 distributed networks. Design Procedure/Approach: 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. We develop a probabilistic model for each distributed cluster to perform data accuracy and energy consumption model in the network. Finally we find a trade off between data accuracy and energy consumption model to select an optimal number of sensor nodes in each distributed cluster. We also compare the performance for our data accuracy estimation model with information accuracy model for each distributed cluster in the network. Practical Implementation: Measuring temperature in physical environment and measuring moisture content in agricultural field. Inventive /Novel Idea: Optimal node selection in probabilistic approach using the trade of between data accuracy and energy consumption in cluster-based distributed network.Comment: 11 page

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, 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.Comment: 6 page

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

One of the major task of sensor nodes in wireless sensor networks is to transmit a subset of sensor readings to the sink node estimating a desired data accuracy. Therefore in this paper, we propose an accuracy model using Steepest Decent method called Adaptive Data Accuracy (ADA) model which doesn't require any a priori information of input signal statistics to select an optimal set of sensor nodes in the network. Moreover we develop another model using LMS filter called Spatio-Temporal Data Prediction (STDP) model which 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 ADA and STDP models respectively.Comment: 9 page

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. Design Procedure/Approach: 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. We develop a probabilistic model for each distributed cluster to perform data accuracy and energy consumption model in the network. Finally we find a trade off between data accuracy and energy consumption model to select an optimal number of sensor nodes in each distributed cluster. We also compare the performance for our data accuracy estimation model with information accuracy model for each distributed cluster in the network. Practical Implementation: Measuring temperature in physical environment and measuring moisture content in agricultural field. Inventive /Novel Idea: Optimal node selection in probabilistic approach using the trade of between data accuracy and energy consumption in a cluster-based distributed network