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IoT Sensor Networks in Smart Buildings: A Performance Assessment Using Queuing Models.

Brena Santos1, André Soares1, Tuan-Anh Nguyen2

  • 1Programa de Pós-Graduação em Ciência da Computação, Universidade Federal do Piauí (UFPI), Teresina-Piauí 64049-550, Brazil.

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This study introduces a queuing network model to assess smart building performance before construction. It found that the number of processing cores significantly impacts response time, crucial for optimizing IoT infrastructure.

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Area of Science:

  • Computer Science
  • Smart Buildings
  • Internet of Things (IoT)

Background:

  • Smart buildings utilize IoT devices for monitoring, but system malfunctions pose risks.
  • Existing performance assessment models lack analysis of computational resource capacity, core counts, and sensor clustering.
  • Quantifying operational performance metrics is essential for ensuring the reliability of smart building systems.

Purpose of the Study:

  • To propose a queuing network-based architecture for evaluating intelligent building infrastructure performance.
  • To analyze the impact of computational resources (edge and fog layers) on system performance.
  • To identify potential bottlenecks and optimize computational architectures prior to building construction.

Main Methods:

  • Developed a queuing network model representing a multi-floor, multi-room smart building with sensors and edge devices.
  • Employed Design of Experiments (DoE) for comprehensive sensitivity analysis to identify performance bottlenecks.
  • Conducted simulations across three scenarios varying the number of cores, fog nodes, and both simultaneously.

Main Results:

  • Sensitivity analysis revealed that the number of cores has a greater impact on response time than the number of nodes.
  • Simulations demonstrated how varying resources affects key metrics like average response time, utilization, flow, discard rates, and message counts.
  • Overloading the system with insufficient resources can lead to an inability to support new requests.

Conclusions:

  • The proposed queuing network model effectively evaluates smart building computational architectures.
  • System designers can use this model and analysis to optimize resource allocation (cores, nodes) for improved performance and reliability.
  • Proactive performance evaluation aids in preventing system overload and ensuring the quality of service in intelligent buildings.