scholarly journals Lightweight Event Detection Scheme using Distributed Hierarchical Graph Neuron in Wireless Sensor Networks

10.5772/38732 ◽  
2011 ◽  
Author(s):  
Asad I. ◽  
Anang Hudaya Muhamad Amin ◽  
Raja Azlina Raja Mahmoo
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Event Detection ◽  
Wireless Sensor ◽  
Detection Scheme ◽  
Hierarchical Graph

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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