A multi-granularity grid-based graph model for indoor space

Abstract

With the development of the Internet of Things (IoT) and indoor positioning technologies, how to manage the rapidly increasing indoor moving object data is a research topic. The fundamental issue is to consider the representation of complicated indoor space as well as the moving object. In this paper, we aim to propose a complete indoor space model that can sup-port various indoor applications. The proposed model divides each indoor element into multi-granularity grids and introduces grids as indoor primitive geometry for the representation of connectivity between indoor elements and location of moving objects. The advantage of this approach is that it can simultaneously represent the connective relationship and geometric information of indoor elements, and give more accurate description of indoor distance and direction information. Several queries of indoor applications under the shopping mall scenar-io are employed to illustrate the completeness and robustness of our model. © 2014 SERSC.