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Please use this identifier to cite or link to this item: http://hdl.handle.net/2005/3087

Title: Spatially Correlated Data Accuracy Estimation Models in Wireless Sensor Networks
Authors: Karjee, Jyotirmoy
Advisors: Jamadagni, H S
Keywords: Wireless Sensor Networks
Sensor Network Models
Data Accuracy Estimation Models
Spatio-Temporal Data Estimation
Deterministic Distributed Clustering (DDC) Algorithm
Adaptive Data Accuracy (ADA) Model
Spatial Data Correlation Distributed Clustering (SDCDC) Algorithm
Spatially Correlated Data Accuracy Estimation Models
Wireless Sensor Networks - Clustering Algorithms
Estimated Data Accuracy Model (EDA)
Cluster Data Accuracy Model (CDA)
Distributed Cluster Data Accuracy Model (DCDA)
Wireless Sensor Nodes - Spatial Data Correlation
Distributed Clustering
Distributed Clusters - Data Estimation
Submitted Date: 2013
Series/Report no.: G26391
Abstract: One of the major applications of wireless sensor networks is to sense accurate and reliable data from the physical environment with or without a priori knowledge of data statistics. To extract accurate data from the physical environment, we investigate spatial data correlation among sensor nodes to develop data accuracy models. We propose three data accuracy models namely Estimated Data Accuracy (EDA) model, Cluster based Data Accuracy (CDA) model and Distributed Cluster based Data Accuracy (DCDA) model with a priori knowledge of data statistics. Due to the deployment of high density of sensor nodes, observed data are highly correlated among sensor nodes which form distributed clusters in space. We describe two clustering algorithms called Deterministic Distributed Clustering (DDC) algorithm and Spatial Data Correlation based Distributed Clustering (SDCDC) algorithm implemented under CDA model and DCDA model respectively. Moreover, due to data correlation in the network, it has redundancy in data collected by sensor nodes. Hence, it is not necessary for all sensor nodes to transmit their highly correlated data to the central node (sink node or cluster head node). Even an optimal set of sensor nodes are capable of measuring accurate data and transmitting the accurate, precise data to the central node. This reduces data redundancy, energy consumption and data transmission cost to increase the lifetime of sensor networks. Finally, we propose a fourth accuracy model called Adaptive Data Accuracy (ADA) model that doesn't require any a priori knowledge of data statistics. ADA model can sense continuous data stream at regular time intervals to estimate accurate data from the environment and select an optimal set of sensor nodes for data transmission to the network. Data transmission can be further reduced for these optimal sensor nodes by transmitting a subset of sensor data using a methodology called Spatio-Temporal Data Prediction (STDP) model under data reduction strategies. Furthermore, we implement data accuracy model when the network is under a threat of malicious attack.
Abstract file URL: http://etd.ncsi.iisc.ernet.in/abstracts/3952/G26391-Abs.pdf
URI: http://hdl.handle.net/2005/3087
Appears in Collections:Department of Electronic Systems Engineering (dese)

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