Online detection and parameter estimation with correlated data in wireless sensor networks
We present an online algorithm for hypothesis testing from correlated observations obtained from a network of heterogeneous sensors and in the presence of model uncertainty. The correlated observations are modeled using copula theory. The batch-mode expectation maximization (EM) algorithm is first developed and then extended to an online algorithm for model parameter estimation and hypothesis testing. Using real-world as well as simulation data, we compare the detection accuracy of our method with other supervised and unsupervised methods and also with a model which ignores the correlation in the data.
Publication Source (Journal or Book title)
IEEE Wireless Communications and Networking Conference, WCNC
Sobhiyeh, S., & Naraghi-Pour, M. (2018). Online detection and parameter estimation with correlated data in wireless sensor networks. IEEE Wireless Communications and Networking Conference, WCNC, 2018-April, 1-6. https://doi.org/10.1109/WCNC.2018.8377342