Generating semantic-based trajectories for indoor moving objects

  • Authors:

  • Huaishuai Wang;Peiquan Jin;Lei Zhao;Lanlan Zhang;Lihua Yue

  • Affiliations:

  • School of Computer Science and Technology, University of Science and Technology of China, China;School of Computer Science and Technology, University of Science and Technology of China, China;School of Computer Science and Technology, University of Science and Technology of China, China;School of Computer Science and Technology, University of Science and Technology of China, China;School of Computer Science and Technology, University of Science and Technology of China, China

  • Venue:
  • WAIM'11 Proceedings of the 2011 international conference on Web-Age Information Management
  • Year:
  • 2011

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Abstract

This paper presents a novel method to generate semantic-based trajectories for indoor moving objects. Indoor moving objects management has been a research focus in recent years. In order to get the trajectory data of indoor moving objects, we have to deply numerous positioning equipments, such as RFID readers and tags. In addition, it is a very complex and costly process to construct different environment settings for various indoor applications. To solve those problems, we propose to use virtual positioning equipments, e.g. RFID readers and tags, to simulate indoor environment. Furthermore, we present a semantic-based approach to generating trajectories for indoor moving objects, which takes into account the type of moving objects, the relationship between moving objects and locations, and the distribution of the trajectories. Compared with previous approaches, our method is more realistic for the simulation of indoor scenarios, and can provide useful trajectory data for further indoor data management analysis. Finally, we design and implement a tool for the generation of semantic-based trajectories for indoor moving objects, and conduct a case study to demonstrate its effectiveness. The results show that it can generate semantic-based trajectories for indoor moving objects according to different parameters and semantic settings.