An algorithm based on logistic regression with data fusion in wireless sensor networks


A decision fusion rule using the total number of detections reported by the local sensors for hypothesis testing and the total number of detections that report “1”to the fusion center (FC) is studied for a wireless sensor network (WSN) with distributed sensors. A logistic regression fusion rule (LRFR) is formulated.

We propose the logistic regression fusion algorithm (LRFA), in which we train the coefficients of the LRFR, and then use the LRFR to make a global decision about the presence/absence of the target. Both the fixed and variable numbers of decisions received by the FC are examined.

The fusion rule of K out of N and the counting rule are compared with the LRFR. The LRFA does not depend on the signal model and the priori knowledge of the local sensors’detection probabilities and false alarm rate.

The numerical simulations are conducted, and the results show that the LRFR improves the performance of the system with low computational complexity.



Published on: 2017-01-08

Made available by EUPB via SpringerOpen / BioMedCentral. Please make sure to read our disclaimer prior to contacting 7thSpace Interactive. To contact our editors, visit our online helpdesk. To submit your press release click here. The full research and author details are available at http://jwcn.eurasipjournals.com/content/2017/1/10

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