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dc.contributor.advisorJamadagni, H S
dc.contributor.authorKarjee, Jyotirmoy
dc.date.accessioned2018-02-10T15:53:44Z
dc.date.accessioned2018-07-31T04:34:45Z
dc.date.available2018-02-10T15:53:44Z
dc.date.available2018-07-31T04:34:45Z
dc.date.issued2018-02-10
dc.date.submitted2013
dc.identifier.urihttp://etd.iisc.ac.in/handle/2005/3087
dc.identifier.abstracthttp://etd.iisc.ac.in/static/etd/abstracts/3952/G26391-Abs.pdfen_US
dc.description.abstractOne 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.en_US
dc.language.isoen_USen_US
dc.relation.ispartofseriesG26391en_US
dc.subjectWireless Sensor Networksen_US
dc.subjectSensor Network Modelsen_US
dc.subjectData Accuracy Estimation Modelsen_US
dc.subjectSpatio-Temporal Data Estimationen_US
dc.subjectDeterministic Distributed Clustering (DDC) Algorithmen_US
dc.subjectAdaptive Data Accuracy (ADA) Modelen_US
dc.subjectSpatial Data Correlation Distributed Clustering (SDCDC) Algorithmen_US
dc.subjectSpatially Correlated Data Accuracy Estimation Modelsen_US
dc.subjectWireless Sensor Networks - Clustering Algorithmsen_US
dc.subjectEstimated Data Accuracy Model (EDA)en_US
dc.subjectCluster Data Accuracy Model (CDA)en_US
dc.subjectDistributed Cluster Data Accuracy Model (DCDA)en_US
dc.subjectWireless Sensor Nodes - Spatial Data Correlationen_US
dc.subjectDistributed Clusteringen_US
dc.subjectDistributed Clusters - Data Estimationen_US
dc.subject.classificationCommunication Engineeringen_US
dc.titleSpatially Correlated Data Accuracy Estimation Models in Wireless Sensor Networksen_US
dc.typeThesisen_US
dc.degree.namePhDen_US
dc.degree.levelDoctoralen_US
dc.degree.disciplineFaculty of Engineeringen_US


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