Coverage problem in camera-based sensor networks using the CUDA platform
DC Field | Value | Language |
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dc.contributor.author | Seo, Jae-Hyun | - |
dc.contributor.author | Yoon, Yourim | - |
dc.contributor.author | Kim, Yong-Hyuk | - |
dc.date.available | 2020-02-27T16:41:11Z | - |
dc.date.created | 2020-02-06 | - |
dc.date.issued | 2017-12-07 | - |
dc.identifier.issn | 1550-1477 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/gachon/handle/2020.sw.gachon/5359 | - |
dc.description.abstract | Closed-circuit televisions serve as prevention against crime, and many studies for closed-circuit television deployment have been conducted. The closed-circuit television deployment in downtown is similar to solving coverage problem in wireless camera-based sensor networks. The difference between the two problems is various environmental factors such as buildings, roads, camera capability, and movements of pedestrians. We use a genetic algorithm to increase the efficiency of closed-circuit television deployment in two-dimensional topography. In addition, a parallel experiment using general-purpose computing on graphics processing units is added to improve computing speed, which is a disadvantage in genetic algorithms. The target region is 500mx500m and consists of 50x50grids. The fitness of the evaluation, which refers to a detection rate, is calculated from the corresponding cell when a pedestrian moves to each cell depending on whether the pedestrian is detected. The proposed experiment was superior to the random deployment experiment by approximately 37.5%. There was no significant difference in the detection rate between the CPU experiment and a NVIDIA GeForce GTX 970 experiment in the 95% confidence interval. The efficiency of a CUDA kernel function using the NVIDIA GeForce GTX 970 graphic card was analyzed. | - |
dc.language | 영어 | - |
dc.language.iso | en | - |
dc.publisher | SAGE PUBLICATIONS INC | - |
dc.relation.isPartOf | INTERNATIONAL JOURNAL OF DISTRIBUTED SENSOR NETWORKS | - |
dc.title | Coverage problem in camera-based sensor networks using the CUDA platform | - |
dc.type | Article | - |
dc.type.rims | ART | - |
dc.description.journalClass | 1 | - |
dc.identifier.wosid | 000417687100001 | - |
dc.identifier.doi | 10.1177/1550147717746353 | - |
dc.identifier.bibliographicCitation | INTERNATIONAL JOURNAL OF DISTRIBUTED SENSOR NETWORKS, v.13, no.12 | - |
dc.identifier.scopusid | 2-s2.0-85039841487 | - |
dc.citation.title | INTERNATIONAL JOURNAL OF DISTRIBUTED SENSOR NETWORKS | - |
dc.citation.volume | 13 | - |
dc.citation.number | 12 | - |
dc.contributor.affiliatedAuthor | Yoon, Yourim | - |
dc.type.docType | Article | - |
dc.subject.keywordAuthor | Sensor deployments | - |
dc.subject.keywordAuthor | coverage | - |
dc.subject.keywordAuthor | genetic algorithm | - |
dc.subject.keywordAuthor | parallel computing | - |
dc.subject.keywordAuthor | CUDA | - |
dc.relation.journalResearchArea | Computer Science | - |
dc.relation.journalResearchArea | Telecommunications | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Information Systems | - |
dc.relation.journalWebOfScienceCategory | Telecommunications | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
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