The Impact of False and Nuisance Alarms on the Design Optimization of Physical Security Systems

Bandlow, Alisa; Jones, Katherine A.; Brown, Nathanael J. K.; Nozick, Linda K.

doi:10.1007/978-3-319-41956-5_18

Cited by 3 publications

(5 citation statements)

References 13 publications

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“…First, scenarios are used to describe the connection between weather, visibility conditions and intruder capabilities with detection probability. Second, the probability of interruption given detection (PI) is now computed including the impact of nuisance and false alarm rates (NAR/FAR) on operator performance as described in [1]. These modifications yield a more realistic and comprehensive optimization model compared to previous work by treating the alarm station operator (ASO) as an integral part of the PPS and the various environmental scenarios as design considerations.…”

Section: Model Formulationmentioning

confidence: 99%

“…Additionally, we treat the ASO processing time (assessment plus queue waiting time), which has a calculated mean value as described in [1], as a Gaussian random variable with a 10% standard deviation. The resultant probability that the RFT (augmented by the ASO processing time) is less than the intruder travel time to the target (after detection) is given by equation (5).…”

Section: Model Formulationmentioning

confidence: 99%

“…A genetic algorithm (GA) determines the best mix of investments to apply to the network based on an objective composed of investment cost, NAR/FAR, and PI. To provide a more realistic RFT, we include an assessment time based on system NAR/FAR using the ASO model described in [1].…”

Section: Solution Proceduresmentioning

confidence: 99%

“…Each child produced is postprocessed to guarantee feasibility by ensuring that at least one layer of each investment type (delay, detection, and ASO) is included, since anything less creates an infeasible solution. If no ASO investments are initially selected, then the minimum number required to keep the NAR/FAR per operator within the "low" range of alarms per day (as defined in [1]) is added. If multiple ASO layers are selected, then the layer with the least number of operators that can feasibly process all alarms is kept, with all others removed.…”

Section: Solution Proceduresmentioning

confidence: 99%

See 3 more Smart Citations

A Stochastic Programming Approach to the Design Optimization of Layered Physical Protection Systems

Brown¹,

Jones²,

Bandlow³

et al. 2017

Proceedings of the 50th Hawaii International Conference on System Sciences (2017)

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Section: Model Formulationmentioning

confidence: 99%

Section: Model Formulationmentioning

confidence: 99%

Section: Solution Proceduresmentioning

confidence: 99%

Section: Solution Proceduresmentioning

confidence: 99%

See 2 more Smart Citations

A Stochastic Programming Approach to the Design Optimization of Layered Physical Protection Systems

Brown¹,

Jones²,

Bandlow³

et al. 2017

Proceedings of the 50th Hawaii International Conference on System Sciences (2017)

Self Cite

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“…Thus, the consequence of a residential fire is often monumental and tragic, resulting in significant losses. Fire alarms, which supposedly protect people from a possible fire outbreak by giving off the warning, can casually fail humanity by its false fire detection (Bandlow et al, 2017). This false positive alarm causes inconveniencies rather than protection.…”

Section: Introductionmentioning

confidence: 99%

Smart fire monitoring system for a city: design and development

Sansolis¹,

Dumpit²,

Feliprada³

2021

Technium

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All Fire Alarm Systems essentially operate on the same fundamental principle and framework. It provides audible and visual alarm signals. An alarm is raised if a sensor detects smoke or heat, warns people that there may be a fire, and evacuate the premises immediately. Consequently, concerned individuals may call the fire department to mitigate the emergency at which, at this point of time, the firefighter's Emergency Respond Time (ERT) is vital. ERT is one of the contributing factors to how much fire will consume lives and, or properties. In worst cases, an entire building turns into ashes, lives taken because of a slow ERT. Then there are these casual "false-alarms" caused by the false-positive readings of the installed Fire Alarm Systems. These false alarms cause mass panic and consume firefighters' valuable time and resources. The instances mentioned above are a few reasons why there is a need to implement an efficient city-wide fire alarm system. This study focused on the design, development and testing of a wide-area Smart Fire Monitoring System comprised of major parts, the devices and the system software. Specifically, this study aimed to develop a smart fire alarm device using the Agile Prototyping Methodology (APM) and employ Agile Software Development Methodology (ASDM) for the development of the Smart Fire Monitoring System Software (SFMSS). For the development of the smart fire alarm device, the proponents assembled necessary sensors, electrical components, microcontrollers, and other electrical modules to create a working smart fire alarm device that abled to detect smoke; detect temperature spikes; detect the existence of fire; broadcast a GPS coordinates to the fire department; transmit and receive data through RF signals using long-range radio frequency (RF) module and SMS technology; and, be functional and operational under Low Power mode. Concomitant with this device is its system software, SFMSS, to manage and process the broadcasted data. SFMSS is a centralized system developed for the fire department to monitor the city for a fire outbreak. SFMSS continuously communicates with the fire alarm devices to autonomously monitor fire presence via radio frequency and process SMS notifications containing GPS coordinates, convert them into a readable address, and plot it in a city map in the event of a fire emergency. The proponents conducted a Stress and Reliability Test on the smart fire alarm device and Product Evaluation for Quality, Efficiency, and Usability with the Bureau of Fire Protection Region 6 for the entirety of the system.

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Determining the Location of a Concealed Handgun on the Human Body using Marker-Less Gait Analysis and Machine Learning

Muchiri

Lukandu

Wanyembi

2019

2019 Conference on Next Generation Computing Applications (NextComp)

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The Impact of False and Nuisance Alarms on the Design Optimization of Physical Security Systems

Cited by 3 publications

References 13 publications

A Stochastic Programming Approach to the Design Optimization of Layered Physical Protection Systems

A Stochastic Programming Approach to the Design Optimization of Layered Physical Protection Systems

Smart fire monitoring system for a city: design and development

Determining the Location of a Concealed Handgun on the Human Body using Marker-Less Gait Analysis and Machine Learning

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