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Forest fire monitoring system supported by unmanned aerial vehicles and edge computing: a performance evaluation using petri nets

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Abstract

Forest fires cause damage to life, biodiversity, and properties, affecting natural ecosystems, healthy and only. During the practical application of fire prevention, numerous detection techniques have been thoroughly researched to prevent devastating fires. The techniques improve early fire detection and accelerate emergency response, reducing damage and optimizing system containment operations. Wildfire detection requires a robust infrastructure for equipment, maintenance, and ongoing monitoring. Effective cooperation between components and integration of technologies is key to a consistent and comprehensive response to fires. This article proposes a fire monitoring system using drones, cameras, and edge computing technologies. We use Stochastic Petri Nets (SPN) to model the structure and evaluate the system’s performance. The model is parameterizable, allowing adjustments to the components’ resource capabilities and service time. Twenty-four parameters can be defined, making it possible to evaluate a wide variety of different scenarios. The results obtained in different scenarios in this work have the potential for auxiliary administrators of monitoring systems to estimate more than six analyses and plan more optimized architectures as needed.

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Authors and Affiliations

  1. Laboratory of Applied Research to Distributed Systems (PASID), Federal University of Piauí (UFPI), Picos, Piauí, Brazil

    Arthur Sabino, Luiz Nelson Lima, Carlos Brito, Leonel Feitosa & Francisco Airton Silva

  2. R. Cícero Duarte, no 905, Junco, 64607-670, Brazil

    Arthur Sabino, Luiz Nelson Lima, Carlos Brito, Leonel Feitosa & Francisco Airton Silva

  3. University of Brasilia (UnB), Brasília, DF, Brazil

    Marcos F. Caetano & Priscila Solis Barreto

  4. Prédio CIC/EST, Asa Norte, 70919900, Brazil

    Marcos F. Caetano & Priscila Solis Barreto

Authors
  1. Arthur Sabino
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  2. Luiz Nelson Lima
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  3. Carlos Brito
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  4. Leonel Feitosa
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  5. Marcos F. Caetano
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  6. Priscila Solis Barreto
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  7. Francisco Airton Silva
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The authors contributed equally to this work.

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Correspondence to Francisco Airton Silva.

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Sabino, A., Lima, L.N., Brito, C. et al. Forest fire monitoring system supported by unmanned aerial vehicles and edge computing: a performance evaluation using petri nets. Cluster Comput (2024). https://doi.org/10.1007/s10586-024-04504-5

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