IoT Based Crowd Congestion and Stampede Avoidance in Hajj Using Wemos D1 with Machine Learning Approach

A huge mass gathering is a common scenario on various occasion when millions of people congest comparatively in a small place. During this time crowd management and controlling is become a very difficult and crucial task. Sometimes there were accidents such as stampede in which thousands of casualties during such convention. This research deals with crowd congestion to provide crowd safety and stampede avoidance. For this research purpose, Hajj is considered as the case study where we are proposed an E-wrist belt which is the integration of various sensor and Wemos D1 based on Internet of Things (IoT). Generally, stampede occurs in any large assembly mainly because of fall down of attendance, temperature and humidity. The machine learning approach is used for evaluating stampede probability by assessing temperature, humidity and fall of attendance. The objective of this research is to improve the existing crowd management controlling system in hajj by using E-wrist belt.