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Decentralized estimation of forest fire spread using reactive and cognitive mobile sensors

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Abstract

This paper presents a study of the ability to build an observer for a complex system using a decentralized multi-agent system for the coordination of mobile sensors. The environment is modeled using a CA model representing forest fire spread. The initial distribution for the different species in the vegetation is generated using a Perlin algorithm. Implementation is realized on GPGPU. A coherence measure for the observation error is defined. The observation itself is realized with mobile sensors and a decentralized coordination of the trajectories. We analyze the balance between individual and collective behaviours of agents which is required to achieve the best performance with respect to the chosen coherence measure. Two kinds of agent’ behaviour are studied: reactive and cognitive.

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Notes

  1. In terms of behavioural rules mimicking the physical interactions and translating them into a fully discrete universe (Chopard 2012).

  2. Computational efficiency considerations led us to include the neighbourhood radius as a part of the state of a cell. Indeed, it varies with the type of vegetation. An alternative (and more usual) approach would be to define directly the neighbourhood of the largest possible one for all possible types of vegetation and the evolution rule accordingly.

  3. Perlin noise is an algorithm able to randomly generate coherent and continuous noise in N dimensions. For instance, it has been used to generate realistic heights for mountains in video games worlds (Parberry 2014).

  4. Cells on the left boundary have no left adjacent cell for example.

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Authors and Affiliations

  1. Université Grenoble Alpes, LCIS, 26000, Valence, France

    Guillaume Schlotterbeck, Clement Raïevsky & Laurent Lefèvre

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  1. Guillaume Schlotterbeck
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  2. Clement Raïevsky
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  3. Laurent Lefèvre
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Correspondence to Laurent Lefèvre.

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Schlotterbeck, G., Raïevsky, C. & Lefèvre, L. Decentralized estimation of forest fire spread using reactive and cognitive mobile sensors. Nat Comput 17, 537–551 (2018). https://doi.org/10.1007/s11047-017-9627-0

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  • DOI: https://doi.org/10.1007/s11047-017-9627-0

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