Coverage optimization to support security monitoring

Murray, Alan T.; Kim, Kamyoung; Davis, James W.; Machiraju, Raghu; Parent, Rick

doi:10.1016/j.compenvurbsys.2006.06.002

Cited by 138 publications

(83 citation statements)

References 24 publications

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“…Murray et al (2007) defined an object function as the coverage and the weighted coverage of an interest area. Mittial et al (2004) used the density function of people to define the object function.…”

Section: Problem Definitionmentioning

confidence: 99%

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A Genetic Algorithm to Solve the Optimum Location Problem for Surveillance Sensors

Kim

et al. 2016

Journal of the Korean Society of Surveying, Geodesy, Photogramm

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Due to threats caused by social disasters, operating surveillance devices are essential for social safety. CCTV, infrared cameras and other surveillance equipment are used to observe threats. This research proposes a method for searching for the optimum location of surveillance sensors. A GA (Genetic Algorithm) was used, since this algorithm is one of the most reasonable and efficient methods for solving complex non-linear problems. The sensor specifications, a DEM (Digital Elevation Model) and VITD (Vector Product Interim Terrain Data) maps were used for input data. We designed a chromosome using the sensor pixel location, and used elitism selection and uniform crossover for searching final solution. A fitness function was derived by the number of detected pixels on the borderline and the sum of the detection probability in the surveillance zone. The results of a 5-sensor and a 10-sensor were compared and analyzed. This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http:// creativecommons.org/licenses/by-nc/3.0) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Section: Problem Definitionmentioning

confidence: 99%

“…Cooper (1964) is a leader in the field of location-allocation problems, suggesting a number of heuristic algorithms. Murray s work addressed sensor placement for security monitoring in 3D urban cases (Murray et al, 2007).…”

Section: Introductionmentioning

confidence: 99%

A Genetic Algorithm to Solve the Optimum Location Problem for Surveillance Sensors

Kim

et al. 2016

Journal of the Korean Society of Surveying, Geodesy, Photogramm

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“…Debido a esto, se impondrá como restricción fuerte de diseño del sistema de vigilancia, que cada par de estaciones (nodos) que están conectadas directamente entre sí, posean al menos una cámara de vigilancia ubicada en alguna de estas estaciones. Por ejemplo, para las parejas de nodos en el grafo de la Figura 1: (1,2), (1,3), (2,3), (2,4), y (3,5), debe haber al menos una cámara de vigilancia localizada en una de las estaciones de cada uno de estos pares. Suponiendo que se desea construir la red de vigilancia para el sistema mostrado en la Figura 1 y se cuenta con presupuesto para instalar 3 cámaras, es posible encontrar varias soluciones al problema.…”

Section: Introductionunclassified

Modelos de localización de cámaras de vigilancia en una red de transporte público masivo

2017

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ResumenEste artículo estudia el problema de localización de cámaras de vigilancia aplicado a una red de transporte público masivo. Se considera una red de estaciones conectadas entre sí, mediante rutas predeterminadas de buses. El problema estudiado consiste en escoger las estaciones que deben ser vigiladas mediante cámaras con el fin de optimizar simultáneamente dos objetivos: El valor esperado del número de crímenes detectados por las cámaras, y la calidad de las imágenes captadas por el sistema de vigilancia completo. Se formulan dos modelos de optimización basados en programación entera para este problema considerando múltiples períodos, restricciones de presupuesto y restricciones de conectividad donde se busca garantizar que al menos se cuente con una cámara de vigilancia por cada pareja de estaciones conectadas directamente. Se realiza una comparación

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“…First defined in [15], researchers applied the MCLP to various facility location sectors such as allocation of security-military firms, warehouses, emergency systems and healthcare facilities. In their study [28], authors formulate a MCLP model for determining the locations of video sensors to control the security of an urban area. In another military application, [29] study the problem of enhancing maintenance schedules of Intercontinental Ballistic Missiles (ICBM) for a given security requirement level.…”

Section: Literature Reviewmentioning

confidence: 99%

A multi-criteria assessment of the p-median, maximal coverage and p-center location models

2017

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Original scientific paper Location problems consider locating facilities with the objective of finding their best locations. In most real world problems, it is common that a demand node is required to be covered with multiple facilities in order to ensure a backup supply. The backup supply is necessary especially for public or emergency service location problems where a covered demand may not be serviced if it's designated facility is engaged serving other demands. In this study we consider three classic location models, i.e. p-median, maximal coverage and p-center, and compare their performances with respect to seven decision criteria under Q-coverage requirement. For this purpose, we generate multiple problem instances and solve each instance with the three models for different Q-values. Our numerical results reveal the pros and cons of each model to assist decision makers in determining the most promising model with respect to each assessment criteria. Keywords: backup coverage; facility location; maximal coverage; p-center; p-median Višekriterijska procjena modela lokacije p-srednje, maksimalne pokrivenosti i p-centralnoIzvorni znanstveni članak Problemi lokacije znače lociranje objekata s ciljem pronalaženja najbolje lokacije. Kod većine stvarnih problema uobičajeno je da zahtijevani čvor zadovoljava višestruke uvjete kako bi osigurao rezervnu opskrbu. Ta je rezervna opskrba posebno potrebna kod javnih ili problema lokacije hitne službe kada se pokrivena potražnja ne može osigurati ako je njezin za to određeni objekt zauzet pružanjem usluge drugim zahtijevima. U ovom radu razmatramo tri klasična modela lokacije, t.j. p-srednje, maksimalne pokrivenosti i p-centralno, te uspoređujemo njihovo funkcioniranje u odnosu na sedam kriterija za donošenje odluke u okviru zahtjeva Q-pokrivenosti (Q-coverage). U tu svrhu generiramo slučajeve višestrukih problema i svaki slučaj rješavamo s tri modela za različite Q vrijednosti. Naši numerički rezultati otkrivaju argumente za i protiv svakog modela koji pomažu u donošenju odluke o određivanju modela koji bi najviše odgovarao u odnosu na svaki od kriterija koji se uzimaju u obzir pri donošenju procjene.

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Coverage optimization to support security monitoring

Cited by 138 publications

References 24 publications

A Genetic Algorithm to Solve the Optimum Location Problem for Surveillance Sensors

A Genetic Algorithm to Solve the Optimum Location Problem for Surveillance Sensors

Modelos de localización de cámaras de vigilancia en una red de transporte público masivo

A multi-criteria assessment of the p-median, maximal coverage and p-center location models

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