Multiple-source localization in binary sensor networks

Abstract

The binary sensor generates one bit of information of target: whether it detects the target or not. So it is a low-power and bandwidth-efficient solution for wireless sensor networks. Most of the multiple-source localization methods are focus on the signal strength. This paper investigates multiple-source localization using data from binary sensors. We firstly introduce a multiple sources detection model based on Neyman-Pearson criterion for binary sensor. Then an iterative fuzzy C-means (IFCM) algorithm is proposed to solve the multiple sources localization problem. We compare proposed algorithm with fuzzy C-means (FCM) algorithm in three deployment strategies. Simulation results show that our proposed IFCM algorithm outperforms the FCM algorithm.