scholarly journals Local Event Boundary Detection with Unreliable Sensors: Analysis of the Majority Vote Scheme

2014 ◽  
pp. 1-12
Author(s):  
Peter Brass ◽  
Hyeon-Suk Na ◽  
Chan-Su Shin
Keyword(s):  
Majority Vote ◽  
Boundary Detection ◽  
Vote Scheme ◽  
Event Boundary

scholarly journals Event boundary detection using autonomous agents in a sensor network

2021 ◽  
Author(s):  
Adil Jaffer
Keyword(s):  
Sensor Network ◽  
Autonomous Agents ◽  
Boundary Detection ◽  
Boundary Node ◽  
Sensory Data ◽  
Transmission Distance ◽  
N Efficiency ◽  
Novel Approach ◽  
Event Boundary ◽  
Distance To The Boundary

We propose a novel approach to event boundary detection, where autonomous agents are deployed in order to minimize the number of transmissions required to discover an event boundary. The goal of our algorithm is to reduce the number of non-boundary node transmissions (i.e. nodes within the event area and not within transmission distance to the boundary), since the sensory data from these nodes are not required for event boundary detection. The algorithm works by first randomly generating a fraction of agents within the event nodes, then discovering and mapping the boundary, and finally reporting the aggregated results to the user. Simulations demonstrate that the algorithm exhibits O(n) efficiency relationship with the event area, which is an improvement over existing methods that show O(n²) relationships. Furthermore, we demonstrate that the boundary of an event may be successfully mapped using the proposed algorithm.

scholarly journals Event boundary detection using autonomous agents in a sensor network

2021 ◽  
Author(s):  
Adil Jaffer
Keyword(s):  
Sensor Network ◽  
Autonomous Agents ◽  
Boundary Detection ◽  
Boundary Node ◽  
Sensory Data ◽  
Transmission Distance ◽  
N Efficiency ◽  
Novel Approach ◽  
Event Boundary ◽  
Distance To The Boundary

We propose a novel approach to event boundary detection, where autonomous agents are deployed in order to minimize the number of transmissions required to discover an event boundary. The goal of our algorithm is to reduce the number of non-boundary node transmissions (i.e. nodes within the event area and not within transmission distance to the boundary), since the sensory data from these nodes are not required for event boundary detection. The algorithm works by first randomly generating a fraction of agents within the event nodes, then discovering and mapping the boundary, and finally reporting the aggregated results to the user. Simulations demonstrate that the algorithm exhibits O(n) efficiency relationship with the event area, which is an improvement over existing methods that show O(n²) relationships. Furthermore, we demonstrate that the boundary of an event may be successfully mapped using the proposed algorithm.

scholarly journals An Information Entropy Based Event Boundary Detection Algorithm in Wireless Sensor Networks

Symmetry ◽  
2019 ◽  
Vol 11 (4) ◽  
pp. 537 ◽  
Author(s):  
Huafeng Wu ◽  
Qingshun Meng ◽  
Jiangfeng Xian ◽  
Xiaojun Mei ◽  
Christophe Claramunt ◽  
...  
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Information Entropy ◽  
Large Scale ◽  
Boundary Detection ◽  
Detection Algorithm ◽  
Wireless Sensor ◽  
Detection Accuracy ◽  
Environment Monitoring ◽  
Event Boundary

Wireless Sensor Networks (WSNs) have been extensively applied in ecological environment monitoring. Typically, event boundary detection is an effective method to determine the scope of an event area in large-scale environment monitoring. This paper proposes a novel lightweight Entropy based Event Boundary Detection algorithm (EEBD) in WSNs. We first develop a statistic model using information entropy to figure out the probability that a sensor is a boundary sensor. The EEBD is independently executed on each wireless sensor in order to judge whether it is a boundary sensor node, by comparing the values of entropy against the threshold which depends on the boundary width. Simulation results demonstrate that the EEBD is computable and offers valuable detection accuracy of boundary nodes with both low and high network node density. This study also includes experiments that verify the EEBD which is applicable in a real ocean environmental monitoring scenario using WSNs.

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