A probabilistic model for the deployment of sensors

Carter, Brian S.; Ragade, Rammohan K.

doi:10.1109/sas.2009.4801767

Cited by 25 publications

(21 citation statements)

References 14 publications

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“…For acoustic sensors, a binary sensing model was applied, whereas the field-of-view metric [24] was applied for image sensors. Their study extended a previous study [19] by adding probabilistic coverage determination methods.…”

Section: Replacementmentioning

confidence: 63%

“…Carter and Ragade [20] aimed to solve the problem by covering the target locations (target points) of a finite set by using predetermined sensors. They attempted to solve two deployment objectives (i.e., to minimize the number of deployed sensors and guarantee coverage of all target points) by using microbial GA [23].…”

Section: Replacementmentioning

confidence: 99%

“…(b) Probability of not being detected at (a) × Probability of cell (4, 2) being detected by sensor (2,2) which is the next closest distance. (c) Probability of not being detected at (b) × Probability of cell (4, 2) being detected by sensor (6,4) which is the next closest distance. (d) Probability of not being detected at (c) × Probability of cell (4, 2) being detected by sensor (4,6) which is the next closest distance.…”

Section: Calculation Of Vehicle Movement In Parallelmentioning

confidence: 99%

“…(c) Probability of not being detected at (b) × Probability of cell (4, 2) being detected by sensor (6,4) which is the next closest distance. (d) Probability of not being detected at (c) × Probability of cell (4, 2) being detected by sensor (4,6) which is the next closest distance.…”

Section: Calculation Of Vehicle Movement In Parallelmentioning

confidence: 99%

“…Sensing models are divided into binary and probabilistic sensing models. In a binary sensing model [4,5], a sensor has a simple fixed sensing radius and the object is detected if an object is present in the sensing radius; otherwise, it is not detected. In the probabilistic sensing model [6], various factors, such as noise and barriers, capable of affecting the accuracy of the sensor reading are taken into consideration.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

An Efficient Large-Scale Sensor Deployment Using a Parallel Genetic Algorithm Based on CUDA

Seo

Yoon

Kim

2016

International Journal of Distributed Sensor Networks

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We have employed evolutionary computation to solve the optimization problem of sensor deployment in battlefield environments. A genetic algorithm has the advantage of delivering results of a higher quality than simple computational algorithms, but it has the drawback of requiring too much computing time. This study aimed not only to shorten the computing time to as close to real-time as possible by using the Compute Unified Device Architecture (CUDA) but also to maintain a solution quality that is as good as or better than the case when the proposed algorithm is not used. In the proposed genetic algorithm, parallelization was applied to speed up the fitness evaluation requiring heavy computation time. The proposed CUDA-based design approach for complex and various sensor deployments is validated by means of simulation. We parallelized two parts in Monte Carlo simulation for the fitness evaluation: moving lots of tested vehicles and calculating the probability of detection (POD) for each vehicle. The experiment was divided into CPU and GPU experiments depending on arithmetic unit types. In the GPU experiment, the results showed similar levels for the detection probability by GPU and CPU, and the computing time decreased by approximately 55-56 times.

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Section: Replacementmentioning

confidence: 63%

Section: Replacementmentioning

confidence: 99%