Predicting Crowd Intensity Based on Statistics of Mobile Signal Analysis in Smart City Using Heatmap Layer by Fuzzy Inference and Deep Learning.

Abstract Wireless networks the mobile user to establish a wireless connection with the central server in the fixed network. On-line activities such as transaction and querying information can be conducted by mobile user. Mobile user queries are location dependent in mobile environment. Mobile user wants to move to hospital or hypermarket, before moving to the location, mobile user wants to know the crowd at the destination , Based on the number of people present, the mobile user can choose to go to another branch. At present a mobile user cannot get the details about the crowd in the particular location. Recent researches are the vehicle tracking, taxi services, road traffic and location-based queries. A proposed research idea is to identify the number of people present in a given area in a city. We use the Heatmap layer tool to find out the crowd intensity. Heatmap layer will indicate crowd intensity of a particular area, Heatmap layer show the color on the map, red colour in the particular location is represent about high intensity of crowd and the green colour is represent lower intensity of crowd, Heatmap shows crowd intensity through mobile signal, more people present more mobile signal in that location. In our research proposal ,we calculate the people crowd using deep learning algorithm. This research application can be used in smart city for the school fees payment , Ministry office , hospital (out Patient not to wait more time in hospital) , hypermarket and cinema theater. Numerical method is obtained by using the fuzzy inference model.