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

2013 ◽  
Vol 9 (3) ◽  
pp. 192675 ◽  
Author(s):  
Haiping Huang ◽  
Lei Chen ◽  
Xiao Cao ◽  
Ruchuan Wang ◽  
Qianyi Wang
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Target Detection ◽  
Decision Fusion ◽  
Wireless Sensor ◽  
Fusion Algorithm ◽  
Distributed Target

scholarly journals Wireless Sensor Networks Energy Effectively Distributed Target Detection

2014 ◽  
Vol 10 (7) ◽  
pp. 763918 ◽  
Author(s):  
Xiaogang Qi ◽  
Peng Wei ◽  
Lifang Liu ◽  
Mande Xie ◽  
Guoyong Cai
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Target Detection ◽  
Wireless Sensor ◽  
Distributed Target

scholarly journals Spatiotemporal Correlation Based Fault-Tolerant Event Detection in Wireless Sensor Networks

2015 ◽  
Vol 2015 ◽  
pp. 1-14 ◽  
Author(s):  
Kezhong Liu ◽  
Yang Zhuang ◽  
Zhibo Wang ◽  
Jie Ma
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Event Detection ◽  
Fault Tolerant ◽  
Decision Fusion ◽  
Wireless Sensor ◽  
Detection Accuracy ◽  
Fusion Algorithm ◽  
Detection Scheme ◽  
Spatiotemporal Correlation

Reliable event detection is one of the most important objectives in wireless sensor networks (WSNs), especially in the presence of faulty nodes. Existing fault-tolerant event detection approaches usually take the probability of faulty nodes into account and fusion techniques to weaken the influence of faulty readings are usually developed. Through extensive experiments, we discover a phenomenon that event detection accuracy degrades quickly when the faulty sensors ratio reaches a critical value. This problem has not drawn enough attention and a solution to the problem is our concern. In this paper, a spatiotemporal correlation based fault-tolerant event detection scheme (STFTED) is proposed, which leverages a two-stage decision fusion and spatiotemporal correlation to improve the event detection quality. In the low-level local stage, a location-based weighted voting scheme (LWVS) is developed to make decision fusion locally on each sensor node, which is based on neighboring nodes and the geographical distributions of two decision quorums. In the high-level global stage, a Bayesian fusion algorithm is adopted to reach a consensus among individual detection decisions made by sensor nodes. Simulation results demonstrate that the proposed approach is highly effective and a better quality of event detection can be obtained compared with the optimal threshold decision schemes (OTDS).

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