  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 as the basis for
evaluating indoor spatial queries with noisy RFID raw data. Furthermore, two
novel models, indoor walking graph model and anchor point indexing model, are
created for tracking object locations in indoor environments. Based on the
inference method and tracking models, we develop innovative indoor range and k
nearest neighbor (kNN) query algorithms. We validate our solution through use
of both synthetic data and real-world data. Our experimental results show that
the proposed algorithms can evaluate indoor spatial queries effectively and
efficiently. We open-source the code, data, and floor plan at
https://github.com/DataScienceLab18/IndoorToolKit.
