MEC-Based Evacuation Planning Using Variance Fractal Dimension Trajectory for Speech Classification

Evacuation models are used in crisis scenarios to optimize the path for occupants to escape a dangerous situation. The behavior of occupants in crisis scenarios has been demonstrated to differ between genders. This paper proposes an end-to-end system that can automatically model an evacuation plan based on the distribution of genders in a given space. Using MEC connected edge devices that can detect speech signals, and interfacing with the cloud through edge servers equipped with learning capabilities, an evacuation model can be generated in real-time according to current gender distribution in an occupied area. The key to making this system successful is an accurate gender speech classifier computed at the edge level. The classification model used was an SVM classifier, and the features used were MFCCs and VFDTs. The MFCCs displayed a gender classification accuracy of 91.47%, and when adding VFDT features, the gender learning accuracy was increased to 92.19%. This shows the benefit of adding VFDT features to speech classification accuracy. Indeed, running the classifier on the edge level is enabling the system to meet the maximum delay deadline (10 sec), as required by the NFPA code standards.

I. Introduction

Evacuation models are used to investigate the safety conditions of a given building. Evacuation calculations are a large part of performance based analyses to asses the level of life safety in buildings. Models are created based on numbers of factors such as their availability, overarching methods to simulate occupants, purpose, structure type, perspective of building and occupants, algorithms which can analyze occupant behavior, and estimations of time required for occupants to traverse the path and advance to safety outside of the building [1]. Males and females are noted to behave differently in crisis situations, such as an evacuation scenario. This makes it important for evacuation modeling engineers to know the genders of occupants in an occupied area. With information of occupant gender, the evacuation modeling will be more accurate and the evacuation algorithm will be able to relay the optimal path for evacuation. In order to accurately predict the genders of people in a space, a system needs to be setup so to grant the ability to detect the genders of people [1] [2].