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.