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<p>Video surveillance systems serve various purposes, including access control, crime prevention, and internal security. However, storing data and images generated by these systems is challenging due to device volume, image quality, and storage demand. Evaluating the availability of video surveillance systems is crucial. Ensuring uninterrupted operation is essential, as system failures can compromise the effectiveness of such systems. This study proposes models in reliability block diagrams (RBD) and stochastic Petri nets (SPN) to analyze the availability of video surveillance systems. Moreover, the models are validated in a real testbed system, improving the trust in results. In addition, we propose new architectures based on availability importance indexes and consider hot and cold standby redundancies. The results show that an availability of 99.999% was achieved, representing a significant improvement and proving that models can support planning computational infrastructures for video surveillance systems.</p>

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Availability evaluation of a video surveillance system with distributed storage

  • 期刊论文

  • 发表日期：

    2025年2月25日

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  • Ivson Borges,
  • Ermeson Andrade,
  • Francisco Airton Silva,
  • Gustavo Callou

摘要

Video surveillance systems serve various purposes, including access control, crime prevention, and internal security. However, storing data and images generated by these systems is challenging due to device volume, image quality, and storage demand. Evaluating the availability of video surveillance systems is crucial. Ensuring uninterrupted operation is essential, as system failures can compromise the effectiveness of such systems. This study proposes models in reliability block diagrams (RBD) and stochastic Petri nets (SPN) to analyze the availability of video surveillance systems. Moreover, the models are validated in a real testbed system, improving the trust in results. In addition, we propose new architectures based on availability importance indexes and consider hot and cold standby redundancies. The results show that an availability of 99.999% was achieved, representing a significant improvement and proving that models can support planning computational infrastructures for video surveillance systems.