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Tri-Variate Copula Modeling for Spatially Correlated Observations in Wireless Sensor Networks

Sunayana. Jadhav and Rohin Daruwala
Dept. of Electronics Engg., VJTI, Mumbai, India
Abstract—Correlated Observations arise in Wireless Sensor Networks (WSNs) comprising of crowded sensor nodes monitoring a common physical phenomenon. Correlation exists both in spatial and time domain, numerous models have addressed linear dependency in sensor observations. However, Copulas model both linear as well as non-linear dependency in spatial domain. In this paper we have proposed a fusion model for generalized case using Copulas and evaluated it for a tri-variate case. A 3D Copula model previously introduced is computed and analyzed based on Neyman-Pearson framework. Gaussian and Student-t Copulas demonstrate a superior performance for spatially correlated observations as compared to Chair-Varshney rule for independent observations.


Index Terms—Wireless sensor networks, distributed detection, spatial correlation, copula, fusion, Tri-variate.

Cite: Sunayana. Jadhav and Rohin Daruwala, Tri-Variate Copula Modeling for Spatially Correlated Observations in Wireless Sensor Networks,” vol. 14, no. 10, pp. 951-957, 2019. Doi: 10.12720/jcm.14.10.951-957.

 
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