SIGMOD '95 Proceedings of the 1995 ACM SIGMOD international conference on Management of data
Distance browsing in spatial databases
ACM Transactions on Database Systems (TODS)
Queue - RFID
Adaptive cleaning for RFID data streams
VLDB '06 Proceedings of the 32nd international conference on Very large data bases
Query processing in spatial network databases
VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29
An adaptive RFID middleware for supporting metaphysical data independence
The VLDB Journal — The International Journal on Very Large Data Bases
Scalable network distance browsing in spatial databases
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
Event queries on correlated probabilistic streams
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
Cascadia: A System for Specifying, Detecting, and Managing RFID Events
Proceedings of the 6th international conference on Mobile systems, applications, and services
A Lattice-Based Semantic Location Model for Indoor Navigation
MDM '08 Proceedings of the The Ninth International Conference on Mobile Data Management
RFID in the supply chain: panacea or Pandora's box?
Communications of the ACM
Evaluating probability threshold k-nearest-neighbor queries over uncertain data
Proceedings of the 12th International Conference on Extending Database Technology: Advances in Database Technology
Access Methods for Markovian Streams
ICDE '09 Proceedings of the 2009 IEEE International Conference on Data Engineering
Probabilistic Inference over RFID Streams in Mobile Environments
ICDE '09 Proceedings of the 2009 IEEE International Conference on Data Engineering
Building the Internet of Things Using RFID: The RFID Ecosystem Experience
IEEE Internet Computing
Longitudinal study of a building-scale RFID ecosystem
Proceedings of the 7th international conference on Mobile systems, applications, and services
Graph Model Based Indoor Tracking
MDM '09 Proceedings of the 2009 Tenth International Conference on Mobile Data Management: Systems, Services and Middleware
Scalable continuous range monitoring of moving objects in symbolic indoor space
Proceedings of the 18th ACM conference on Information and knowledge management
Probabilistic threshold k nearest neighbor queries over moving objects in symbolic indoor space
Proceedings of the 13th International Conference on Extending Database Technology
Leveraging spatio-temporal redundancy for RFID data cleansing
Proceedings of the 2010 ACM SIGMOD International Conference on Management of data
ROAD: A New Spatial Object Search Framework for Road Networks
IEEE Transactions on Knowledge and Data Engineering
A tutorial on particle filters for online nonlinear/non-GaussianBayesian tracking
IEEE Transactions on Signal Processing
Randomized skip lists-based private authentication for large-scale RFID systems
Proceedings of the fourteenth ACM international symposium on Mobile ad hoc networking and computing
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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 particle filters 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 particle filter-based location inference method 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 extensive simulations with real-world parameters. Our experimental results show that the proposed algorithms can evaluate indoor spatial queries effectively and efficiently.