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Samuel Barrett. Optimizing Sensor Placement for Intruder Detection with Genetic Algorithms. In IEEE International Conference on Intelligence and Security Informatics, pp. 185 –188, May 2007.
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.
@INPROCEEDINGS{ISI07-Barrett,
author={Samuel Barrett},
booktitle={IEEE International Conference on Intelligence and Security Informatics},
title={Optimizing Sensor Placement for Intruder Detection with Genetic Algorithms},
year={2007},
month=may,
pages={185 -188},
keywords={Pareto-optimal curve;environmental condition;genetic algorithm;intruder detection;optimization technique;probability;sensor network;sensor placement;Pareto optimisation;distributed sensors;genetic algorithms;probability;security of data;},
doi={10.1109/ISI.2007.379555},
ISSN={},
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.
}
}
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