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Enhancing Smart Building Surveillance Systems in Thin Walls: An Efficient Barrier Design.
Lee, Taewoo; Kim, Hyunbum.
Affiliation
  • Lee T; Department of Embedded Systems Engineering, Incheon National University, Incheon 22012, Republic of Korea.
  • Kim H; Department of Embedded Systems Engineering, Incheon National University, Incheon 22012, Republic of Korea.
Sensors (Basel) ; 24(2)2024 Jan 17.
Article in En | MEDLINE | ID: mdl-38257687
ABSTRACT
This paper introduces an efficient barrier model for enhancing smart building surveillance in harsh environment with thin walls and structures. After the main research problem of minimizing the total number of wall-recognition surveillance barriers, we propose two distinct algorithms, Centralized Node Deployment and Adaptation Node Deployment, which are designed to address the challenge by strategic placement of surveillance nodes within the smart building. The Centralized Node Deployment aligns nodes along the thin walls, ensuring consistent communication coverage and effectively countering potential disruptions. Conversely, the Adaptation Node Deployment begins with random node placement, which adapts over time to ensure efficient communication across the building. The novelty of this work is in designing a novel barrier system to achieve energy efficiency and reinforced surveillance in a thin-wall environment. Instead of a real environment, we use an ad hoc server for simulations with various scenarios and parameters. Then, two different algorithms are executed through those simulation environments and settings. Also, with detailed discussions, we provide the performance analysis, which shows that both algorithms deliver similar performance metrics over extended periods, indicating their suitability for long-term operation in smart infrastructure.
Key words

Full text: 1 Database: MEDLINE Type of study: Screening_studies Language: En Journal: Sensors (Basel) Year: 2024 Type: Article

Full text: 1 Database: MEDLINE Type of study: Screening_studies Language: En Journal: Sensors (Basel) Year: 2024 Type: Article