scholarly journals A Grid-Based Distributed Event Detection Scheme for Wireless Sensor Networks

Sensors ◽  
2011 ◽  
Vol 11 (11) ◽  
pp. 10048-10062 ◽  
Author(s):  
Ja Won Ko ◽  
Yoon-Hwa Choi
2012 ◽  
Vol 3 (3) ◽  
pp. 53-71 ◽  
Author(s):  
Ying-Hong Wang ◽  
Kuo-Feng Huang ◽  
Shaing-Ting Lin

Wireless Sensor Networks (WSNs) can be widely utilized in many applications, especially in environmental surveillance. However, some holes exist within the WSNs caused by factors like non-uniform deployment of sensor nodes, depletion of energy from sensor nodes, the destruction from external forces, and the existence of physical obstacles, such as mountains and lakes. These holes degrade the performance of wireless sensor networks (WSNs). Hence, finding the position of the holes and utilizing the information to improve the performance of WSNs is a significant issue. To solve this problem, the authors propose a detection scheme for grid-based hole in WSNs. By means of grid architecture, they use the grid head to broadcast and forward the request and respond the holes detection. Sink then calculates the position of the holes for improving the performance of the WSNs.


2015 ◽  
Vol 2015 ◽  
pp. 1-14 ◽  
Author(s):  
Kezhong Liu ◽  
Yang Zhuang ◽  
Zhibo Wang ◽  
Jie Ma

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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