The Korea Institute of Information and Commucation Engineering 2014; 12(1): 8-13
Published online March 31, 2014
https://doi.org/10.6109/jicce.2014.12.1.008
© Korea Institute of Information and Communication Engineering
We consider the sensor selection problem in large sensor networks where the goal is to find the best set of sensors that maximizes application objectives. Since sensor selection typically involves a large number of sensors, a low complexity should be maintained for practical applications. We propose a geometry-based sensor selection algorithm that utilizes only the information of sensor locations. In particular, by observing that sensors clustered together tend to have redundant information, we theorize that the redundancy is inversely proportional to the distance between sensors and seek to minimize this redundancy by searching for a set of sensors with the maximum average distance. To further reduce the computational complexity, we perform an iterative sequential search without losing optimality. We apply the proposed algorithm to an acoustic sensor network for source localization, and demonstrate using simulations that the proposed algorithm yields significant improvements in the localization performance with respect to the randomly generated sets of sensors.
Keywords Iterative sequential search, Localization, Sensor networks, Sensor selection
The Korea Institute of Information and Commucation Engineering 2014; 12(1): 8-13
Published online March 31, 2014 https://doi.org/10.6109/jicce.2014.12.1.008
Copyright © Korea Institute of Information and Communication Engineering.
Kim, Yoon Hak;
Department of Electronic Engineering, Chosun University
We consider the sensor selection problem in large sensor networks where the goal is to find the best set of sensors that maximizes application objectives. Since sensor selection typically involves a large number of sensors, a low complexity should be maintained for practical applications. We propose a geometry-based sensor selection algorithm that utilizes only the information of sensor locations. In particular, by observing that sensors clustered together tend to have redundant information, we theorize that the redundancy is inversely proportional to the distance between sensors and seek to minimize this redundancy by searching for a set of sensors with the maximum average distance. To further reduce the computational complexity, we perform an iterative sequential search without losing optimality. We apply the proposed algorithm to an acoustic sensor network for source localization, and demonstrate using simulations that the proposed algorithm yields significant improvements in the localization performance with respect to the randomly generated sets of sensors.
Keywords: Iterative sequential search, Localization, Sensor networks, Sensor selection
Gwanghyun Jo, Tae-Heon Yang, and Seong-Yoon Shin, Member, KIICE
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