Coverage problem in camera-based sensor networks using the CUDA platform
- Authors
- Seo, Jae-Hyun; Yoon, Yourim; Kim, Yong-Hyuk
- Issue Date
- 7-Dec-2017
- Publisher
- SAGE PUBLICATIONS INC
- Keywords
- Sensor deployments; coverage; genetic algorithm; parallel computing; CUDA
- Citation
- INTERNATIONAL JOURNAL OF DISTRIBUTED SENSOR NETWORKS, v.13, no.12
- Journal Title
- INTERNATIONAL JOURNAL OF DISTRIBUTED SENSOR NETWORKS
- Volume
- 13
- Number
- 12
- URI
- https://scholarworks.bwise.kr/gachon/handle/2020.sw.gachon/5359
- DOI
- 10.1177/1550147717746353
- ISSN
- 1550-1477
- 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.
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