
Research Article
ITAR: A Method for Indoor RFID Trajectory Automatic Recovery
@INPROCEEDINGS{10.1007/978-3-031-24386-8_22, author={Ziwen Cao and Siye Wang and Degang Sun and Yanfang Zhang and Yue Feng and Shang Jiang}, title={ITAR: A Method for Indoor RFID Trajectory Automatic Recovery}, proceedings={Collaborative Computing: Networking, Applications and Worksharing. 18th EAI International Conference, CollaborateCom 2022, Hangzhou, China, October 15-16, 2022, Proceedings, Part II}, proceedings_a={COLLABORATECOM PART 2}, year={2023}, month={1}, keywords={Trajectory recovery Sequence-to-sequence model Radio frequency identification Graph neural network}, doi={10.1007/978-3-031-24386-8_22} }
- Ziwen Cao
Siye Wang
Degang Sun
Yanfang Zhang
Yue Feng
Shang Jiang
Year: 2023
ITAR: A Method for Indoor RFID Trajectory Automatic Recovery
COLLABORATECOM PART 2
Springer
DOI: 10.1007/978-3-031-24386-8_22
Abstract
With the increasing popularity of Radio Frequency Identification (RFID) technology, indoor applications based on RFID trajectory data analysis are becoming more and more extensive, such as personnel location, tracking, and heat map analysis. The effectiveness of indoor applications relies greatly on high-quality trajectory data. However, due to the constraints of the device and environment, RFID readers will miss reading data in real-world practice, which leads to a large number of indoor trajectories that are incomplete. To enhance trajectory data and support indoor applications more efficiently, many trajectory recovery methods to infer trajectories in free space have been proposed. However, existing methods cannot achieve automated inference and have low accuracy in inferring indoor trajectories. In this paper, we propose an Indoor Trajectory Automatic Recovery framework, ITAR, to recover missing points in indoor trajectories. ITAR adopts a sequence-to-sequence learning architecture to generate complete trajectories. We first construct a directed graph for each trajectory and use a graph neural network to capture complex location transition patterns. Then, we propose a multi-head attention mechanism to capture long-term correlations among trajectory points to improve performance. We conduct extensive experiments on synthetic and real datasets, and the results show that ITAR is superior in performance and robustness.