Abstract
The real-time availability of data from data servers, often also provided by sensors and measuring devices, as well as the dynamics of phenomena involving complex systems, raise new challenges for researchers. New practices are needed to evaluate and analyze the networks’ structures of underlying structural modeling of their dynamics. In this work, we propose a similarity based on information theory defined on a set of fuzzy graphs so that comparisons can be made. We introduce the Jensen-Shannon divergence measure to compare fuzzy networks. In our approach, this measure is based on fuzzy local probabilities and incorporates networks’ topological characteristics. This approach consists in building a comparison measure using a fuzzy probability distribution as an invariant. This invariant mitigates encoding problems, and the comparison measurement focuses entirely on networks’ topology. In this work, the proposed method is deployed for fuzzy topological navigation networks with energy dissipation in smart building spaces. The proposed modeling of navigation networks in intelligent buildings can be integrated into the OGC IndoorGML standard. It can also still be used for other types of networks.
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Boulmakoul, A., Daissaoui, A., Karim, L., Lbath, A. (2022). Jensen-Shannon Divergence for Smart Buildings’ Fuzzy Topological Networks Similarity Analytics. In: Kahraman, C., Cebi, S., Cevik Onar, S., Oztaysi, B., Tolga, A.C., Sari, I.U. (eds) Intelligent and Fuzzy Techniques for Emerging Conditions and Digital Transformation. INFUS 2021. Lecture Notes in Networks and Systems, vol 307. Springer, Cham. https://doi.org/10.1007/978-3-030-85626-7_31
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DOI: https://doi.org/10.1007/978-3-030-85626-7_31
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