Weight-Based Clustering Decision Fusion Algorithm for Distributed Target Detection in Wireless Sensor Networks

Haiping Huang,Lei Chen,Ruchuan Wang,Xiao Cao,Qianyi Wang

doi:10.1155/2013/192675

Abstract

We use a great deal of wireless sensor nodes to detect target signal that is more accurate than the traditional single radar detection method. Each local sensor detects the target signal in the region of interests and collects relevant data, and then it sends the respective data to the data fusion center (DFC) for aggregation processing and judgment making whether the target signal exists or not. However, the current judgment fusion rules such as Counting Rule (CR) and Clustering-Counting Rule (C-CR) have the characteristics on high energy consumption and low detection precision. Consequently, this paper proposes a novel Weight-based Clustering Decision Fusion Algorithm (W-CDFA) to detect target signal in wireless sensor network. It first introduces the clustering method based on tree structure to establish the precursor-successor relationships among the clusters in the region of interests and then fuses the decision data along the direction from the precursor clusters to the successor clusters gradually, and DFC (i.e., tree root) makes final determination by overall judgment values from subclusters and ordinary nodes. Simulation experiments show that the fusion rule can obtain more satisfactory system level performance at the environment of low signal to noise compared with CR and C-CR methods.

Highlights

As we know, we can only use one sensor or radar to detect target signal of one event source in ideal environment
We assume that the decision threshold of the successor cluster is T0, and T0 is the decision threshold of the whole wireless sensor network monitoring system, the method of calculation is as same as that of the precursor-successor, so the decision fusion rule of data fusion center (DFC) is Λ⃛ = ∑ni=⃛ 1 u⃛i + ∑mi=⃛ 1 Tiui, where n⃛ is the number of ordinary node in the successor cluster, u⃛i is the judgment of ordinary node in the successor cluster, ui is the judgment of the successor subcluster head, and Ti is the decision threshold of the successor subcluster head
We assume that the region of interest is a square with length of edge 100, the target signal is in the place with the coordinate (50, 50), the coordinate of data fusion center is (50, 50), and DFC is the head of all clusters

Summary

Introduction

We can only use one sensor or radar to detect target signal of one event source in ideal environment. Niu and Varshney publish an algorithm based on ZeroOne decision fusion-Counting Rule [5] in ICASS’05 They set the detection probability and false alarm probability of each sensor node to be the same. Katenka et al put forward the fusion algorithm based on the Zero-One decision [6] of local vote He takes full advantage of the collaboration features of neighbor nodes in wireless sensor network. In [13], based on the mathematical model of multitarget detection, Ermis and Saligrama advance the method of target detection based on the process of Benjamin-Hochberg They analyze the algorithm performance in an ideal environment. This paper analyzes the related works about distributed target detection and puts forward a new method to improve the system detection efficiency on the base of consuming less energy in wireless sensor network.

System Model and Related Definitions

Decision Fusion Rule

Decision Threshold of Different Clusters

Experiments and Simulations

Conclusion and Discussion