Affinity Propagation Algorithm Based Multi-Source Localization Method for Binary Detection

Abstract

SUMMARYWireless sensor network (WSN) has attracted many researchers to investigate it in recent years. It can be widely used in the areas of surveillances, health care and agriculture. The location information is very important for WSN applications such as geographic routing, data fusion and tracking. So the localization technology is one of the key technologies for WSN. Since the computational complexity of the traditional source localization is high, the localization method can not be used in the sensor node. In this paper, we firstly introduce the Neyman-Pearson criterion based detection model. This model considers the effect of false alarm and missing alarm rate, so it is more realistic than the binary and probability model. An affinity propagation algorithm based localization method is proposed. Simulation results show that the proposed method provides high localization accuracy.

Mean Shift-Based Multisource Localization Method in Wireless Binary Sensor Network

“…In [29], the authors formulate the multisource localization problem as the ML problem and employ a self-adaptive practical swarm optimization to solve. In [30], Wang et al use the affinity propagation (AP) algorithm to gain the cluster centers of the alarmed nodes. Then, these centers are merged as the final estimated locations of multiple sources.…”