The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
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The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
Un objectif important des réseaux de capteurs de surveillance est de surveiller efficacement l’environnement et de détecter, localiser et classer les cibles d’intérêt. Le placement optimal des capteurs nous permet de minimiser la main d'œuvre et le temps, d'acquérir des informations précises sur la situation et le mouvement de la cible et de changer rapidement de tactique dans le domaine dynamique. La plupart des recherches précédentes concernant le déploiement des capteurs ont été menées sans tenir compte des facteurs d’entrée pratiques. Ainsi, dans cet article, nous appliquons des facteurs d'entrée plus réels tels que les capacités des capteurs, les caractéristiques du terrain, l'identification de la cible et la direction des mouvements de la cible au problème de placement des capteurs. Nous proposons un nouvel algorithme génétique hybride efficace en régime permanent offrant une faible charge de calcul ainsi qu'un placement optimal des capteurs pour améliorer la capacité de surveillance afin de surveiller et de localiser les véhicules cibles. L'algorithme proposé introduit de nouveaux croisements et mutations géographiques bidimensionnels. En utilisant un nouveau simulateur adoptant l'algorithme génétique proposé développé dans cet article, nous démontrons des applications réussies au problème de placement des capteurs de surveillance sans fil dans le monde réel, donnant des taux de détection et de classification très élevés, 97.5 % et 87.4 %, respectivement.
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Jae-Hyun SEO, Yong-Hyuk KIM, Hwang-Bin RYOU, Si-Ho CHA, Minho JO, "Optimal Sensor Deployment for Wireless Surveillance Sensor Networks by a Hybrid Steady-State Genetic Algorithm" in IEICE TRANSACTIONS on Communications,
vol. E91-B, no. 11, pp. 3534-3543, November 2008, doi: 10.1093/ietcom/e91-b.11.3534.
Abstract: An important objective of surveillance sensor networks is to effectively monitor the environment, and detect, localize, and classify targets of interest. The optimal sensor placement enables us to minimize manpower and time, to acquire accurate information on target situation and movement, and to rapidly change tactics in the dynamic field. Most of previous researches regarding the sensor deployment have been conducted without considering practical input factors. Thus in this paper, we apply more real-world input factors such as sensor capabilities, terrain features, target identification, and direction of target movements to the sensor placement problem. We propose a novel and efficient hybrid steady-state genetic algorithm giving low computational overhead as well as optimal sensor placement for enhancing surveillance capability to monitor and locate target vehicles. The proposed algorithm introduces new two-dimensional geographic crossover and mutation. By using a new simulator adopting the proposed genetic algorithm developed in this paper, we demonstrate successful applications to the wireless real-world surveillance sensor placement problem giving very high detection and classification rates, 97.5% and 87.4%, respectively.
URL: https://global.ieice.org/en_transactions/communications/10.1093/ietcom/e91-b.11.3534/_p
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@ARTICLE{e91-b_11_3534,
author={Jae-Hyun SEO, Yong-Hyuk KIM, Hwang-Bin RYOU, Si-Ho CHA, Minho JO, },
journal={IEICE TRANSACTIONS on Communications},
title={Optimal Sensor Deployment for Wireless Surveillance Sensor Networks by a Hybrid Steady-State Genetic Algorithm},
year={2008},
volume={E91-B},
number={11},
pages={3534-3543},
abstract={An important objective of surveillance sensor networks is to effectively monitor the environment, and detect, localize, and classify targets of interest. The optimal sensor placement enables us to minimize manpower and time, to acquire accurate information on target situation and movement, and to rapidly change tactics in the dynamic field. Most of previous researches regarding the sensor deployment have been conducted without considering practical input factors. Thus in this paper, we apply more real-world input factors such as sensor capabilities, terrain features, target identification, and direction of target movements to the sensor placement problem. We propose a novel and efficient hybrid steady-state genetic algorithm giving low computational overhead as well as optimal sensor placement for enhancing surveillance capability to monitor and locate target vehicles. The proposed algorithm introduces new two-dimensional geographic crossover and mutation. By using a new simulator adopting the proposed genetic algorithm developed in this paper, we demonstrate successful applications to the wireless real-world surveillance sensor placement problem giving very high detection and classification rates, 97.5% and 87.4%, respectively.},
keywords={},
doi={10.1093/ietcom/e91-b.11.3534},
ISSN={1745-1345},
month={November},}
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TY - JOUR
TI - Optimal Sensor Deployment for Wireless Surveillance Sensor Networks by a Hybrid Steady-State Genetic Algorithm
T2 - IEICE TRANSACTIONS on Communications
SP - 3534
EP - 3543
AU - Jae-Hyun SEO
AU - Yong-Hyuk KIM
AU - Hwang-Bin RYOU
AU - Si-Ho CHA
AU - Minho JO
PY - 2008
DO - 10.1093/ietcom/e91-b.11.3534
JO - IEICE TRANSACTIONS on Communications
SN - 1745-1345
VL - E91-B
IS - 11
JA - IEICE TRANSACTIONS on Communications
Y1 - November 2008
AB - An important objective of surveillance sensor networks is to effectively monitor the environment, and detect, localize, and classify targets of interest. The optimal sensor placement enables us to minimize manpower and time, to acquire accurate information on target situation and movement, and to rapidly change tactics in the dynamic field. Most of previous researches regarding the sensor deployment have been conducted without considering practical input factors. Thus in this paper, we apply more real-world input factors such as sensor capabilities, terrain features, target identification, and direction of target movements to the sensor placement problem. We propose a novel and efficient hybrid steady-state genetic algorithm giving low computational overhead as well as optimal sensor placement for enhancing surveillance capability to monitor and locate target vehicles. The proposed algorithm introduces new two-dimensional geographic crossover and mutation. By using a new simulator adopting the proposed genetic algorithm developed in this paper, we demonstrate successful applications to the wireless real-world surveillance sensor placement problem giving very high detection and classification rates, 97.5% and 87.4%, respectively.
ER -