Open-Space Emergency Guiding With Individual Density Prediction Based on Internet of Things Localization

This article proposes an open-space emergency guiding (OSEG) framework that explores deep learning techniques to predict individual densities for evacuation based on Internet of Things localization. The OSEG framework adopts Densely Connected Convolutional Networks that can reserve fine-grained features in earlier convolutional layers and extract them in later convolutional layers to efficiently reduce prediction errors. In addition, OSEG can dynamically guide individuals to exits in the shortest time though balancing the evacuation load among exits as individual densities are non-uniform in indoor spaces. According to our review of relevant research, this is the first framework that integrates deep learning for individual density prediction with load-balancing evacuation for emergency guiding in open indoor spaces, where the distance to each exit, capacities of all exits, concurrent moving of evacuees, and distribution of indoor individuals/groups are considered simultaneously. Simulation results show that our approach outperforms existing methods and can balance the evacuation load of each exit for individuals/groups to minimize total evacuation time, which can reduce 50% of total evacuation time at most compared with existing methods.