|dc.description.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 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 (\(k\)NN) query algorithms.
We validate our solution through extensive simulations with real-world
parameters. Our experimental results show that the proposed
algorithms can evaluate indoor spatial queries effectively and