Data Modelling for Thing Prediction in Station-of-Things Environment

The study in this paper uses the concept of Internet-of-Things (IoT) to represent a station containing a thing of interest termed as Station-of-Things (SoT). Research in communication, computation and sensors innovation has enabled to build intelligent SoT environment. This SoT environment is applicable to numerous situations to identify the inhabitance of chairs in a study hall, seats in a theatre or auditorium, cars in parking-lot, books in a bookshelf, and so forth. In this paper, the proposed communication model, network model and test results show the value of the work which establishes a framework to gauge and assess the inhabitance of the thing in a station. Further, the sensor network enables the capturing of dimensional-data. This data is utilized by a machine learning model to infer the type of a thing. Hence, the study demonstrates a preliminary data model to predict the type of thing in SoT environment.