RFID-Based Indoor Spatial Query Evaluation with Bayesian Filtering Techniques
Bo Hui, Wenlu Wang, Jiao Yu, Zhitao Gong, Wei-Shinn Ku, Min-Te Sun,, Hua Lu

TL;DR
This paper introduces Bayesian filtering techniques combined with novel indoor tracking models to improve the efficiency and accuracy of indoor spatial query evaluation using RFID data, validated through experiments.
Contribution
It proposes new Bayesian filtering-based location inference methods and indoor tracking models for indoor spatial queries, addressing challenges unique to indoor environments.
Findings
Effective evaluation of indoor spatial queries demonstrated
Algorithms show high efficiency in experiments
Open-source code and data provided
Abstract
People spend a significant amount of time in indoor spaces (e.g., office buildings, subway systems, etc.) in their daily lives. Therefore, it is important to develop efficient indoor spatial query algorithms for supporting various location-based applications. However, indoor spaces differ from outdoor spaces because users have to follow the indoor floor plan for their movements. In addition, positioning in indoor environments is mainly based on sensing devices (e.g., RFID readers) rather than GPS devices. Consequently, we cannot apply existing spatial query evaluation techniques devised for outdoor environments for this new challenge. Because Bayesian filtering techniques can be employed to estimate the state of a system that changes over time using a sequence of noisy measurements made on the system, in this research, we propose the Bayesian filtering-based location inference methods…
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Taxonomy
TopicsData Management and Algorithms · Indoor and Outdoor Localization Technologies · Human Mobility and Location-Based Analysis
MethodsGreedy Policy Search
