A Scalable Approach for Dynamic Evacuation Routing in Large Smart Buildings

This paper considers the problem of dynamic evacuation routing in large smart buildings. We investigate a scalable routing approach which not only generates effective routes for evacuees but also quickly updates routes as the disaster status and building conditions could change during the evacuation time. We first design a flexible and scalable evacuation system for large smart buildings with multiple levels of computational support. Given such a system, we develop a novel distributed algorithm for finding effective evacuation routes dynamically by using an LCDT (Length-Capacity-Density-Trustiness) weighted graph model, which is built upon the current disaster information and building conditions. Finally, we propose a caching strategy which expedites dynamic route generation with the current effective route part(s) in order to improve the performance of dynamic evacuation in large buildings. To validate our approach, we test the proposed algorithm with our implementation of an evacuation simulator and compare the results with other approaches. Experimental results show that our approach outperforms other ones in the aspect of the evacuation time reduction and the maximum number of people being evacuated in each time span.