Online detection and parameter estimation with correlated data in wireless sensor networks

Document Type

Conference Proceeding

Publication Date

6-8-2018

Abstract

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

First Page

1

Last Page

6

This document is currently not available here.

Plum Print visual indicator of research metrics
PlumX Metrics
  • Citations
    • Citation Indexes: 4
  • Usage
    • Abstract Views: 3
  • Captures
    • Readers: 2
see details

Share

COinS