Paper Info
Title
Optimizing Sensor Placement for Intruder Detection with Genetic Algorithms
Abstract
Sensor networks are effective tools for detecting intruders. However, the standard technique of placing sensors in a perimeter is not optimal. Using optimization techniques to determine sensor placement can improve the effectiveness of the sensor network. The optimization should take into account the environmental conditions and place sensors to take advantage of these conditions. Additionally, there are multiple objectives to consider in sensor placement, specifically the probability of detection and the time to detect. Genetic algorithms are capable of optimizing both objectives simultaneously, achieving the Pareto-optimal curve. This allows the designer of the network to specify a necessary value for one objective and get sensor placements that optimize the other objective. Compared to the standard perimeter configurations, the genetic algorithm networks perform significantly better with respect to both probability of detection and time to detect.
Year
DOI
Venue
2007
10.1109/ISI.2007.379555
New Brunswick, NJ
Keywords
Field
DocType
Pareto optimisation,distributed sensors,genetic algorithms,probability,security of data,Pareto-optimal curve,environmental condition,genetic algorithm,intruder detection,optimization technique,probability,sensor network,sensor placement,Genetic algorithms,Object detection,Sensor networks,Site security monitoring
Data mining,Object detection,Computer science,Interpolation,Multi-objective optimization,Wireless sensor network,Statistical power,Genetic algorithm
Conference
ISBN
Citations 
PageRank 
1-4244-1329-X
7
0.65
References 
Authors
3
1
Name
Order
Citations
PageRank
Samuel R. Barrett170.65