Location of a Person by Means of Sensors' Network

The different algorithm of data classification and clustering to identify the subject on the room is introduced. The aim is to provide a learning approach for pattern classification of presence sensors data. The focus system is on its application to find the best method for identification the subject location using a specific category of data simulated. The main features of the system include: automatic rule generation, automatic ranges generation, learning and adaptability capability. Simulation values concerning a different cluster methods results are presented. The system successfully detected the location in X and Y axes and body temperature. Its response time in identification suggests the feasibility in real-time applications.