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Publication
Context-based multi-agent recommender system, supported on IoT, for guiding the occupants of a building in case of a fire
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| 5.29 MB | Adobe PDF |
Advisor(s)
Abstract(s)
The evacuation of buildings in case of fire is a sensitive issue for civil society that also
motivates the academic community to develop and study solutions to improve the efficiency of
evacuating these spaces. The study of human behavior in fire emergencies has been one of the areas
that have deserved the attention of researchers. However, this modeling of human behavior is difficult
and complex because it depends on factors that are difficult to know and that vary from country
to country. In this paper, a paradigm shift is proposed which, instead of focusing on modeling the
behavior of occupants, focuses on conditioning this behavior by providing real-time information on
the most efficient evacuation routes. Making this information available to occupants is possible with
a solution that takes advantage of the growing use of the IoT (Internet of Things) in buildings to help
occupants adapt to the environment. Supported by the IoT, multi-agent recommender systems can
help users to adapt to the environment and provide the occupants with the most efficient evacuation
routes. This paradigm shift is achieved through a context-based multi-agent recommender system
based on contextual data obtained from IoT devices, which recommends the most efficient evacuation
routes at any given time. The obtained results suggest that the proposed solution can improve the
efficiency of evacuating buildings in the event of a fire; for a scenario with two hundred people
following the system recommendations, the time they take to reach a safe place decreases by 17.7%.
Description
Keywords
Multi-agent systems Recommender systems Context-based recommender systems IoT—Internet of Things Fire building evacuation Ontologies Occupant behavior conditioning Building occupant guidance