An indoor tracking system and pattern recognition algorithms as key components of IoT-based entertainment industry

摘要

The paper presents the key components of an innovative Integrated Visitor Support System (IVSS) for the distributed entertainment industry, based on Internet of Things (IoT) technology and classification analysis. Regarding the modern IoT layered models, including indoor tracking technologies and pattern recognition algorithm, the project of IVSS was presented. Simple tracking concepts based on chaining of Login Records to form a Path Vector (PV) by so-called poly-LR-isation process was proposed. The data generation model was also developed, where a Weibull and Poison distributions were adopted to generating Login Period and Entry Time parameters, respectively. The data in the form of PVs set was generated for a virtual theme park containing twenty monitored Points of Interest (POIs). The 5 different Visitors' profiles and 4 different behavior types were defined in the model. The efficiency of pattern recognition was calculated according to the Linear Discriminant Analysis concept. Influence of the set of independent variables on the efficiency of correctly classified labels was reported. It has been shown that high efficiency in recognizing the types of behavior can be obtained including the total number of logins in the POIs area and first login times, whereas the proper separation of profiles needs additional data as Login Period.