The active badge location system
ACM Transactions on Information Systems (TOIS)
Updating and Querying Databases that Track Mobile Units
Distributed and Parallel Databases - Special issue on mobile data management and applications
The Cricket location-support system
MobiCom '00 Proceedings of the 6th annual international conference on Mobile computing and networking
Offering a Precision-Performance Tradeoff for Aggregation Queries over Replicated Data
VLDB '00 Proceedings of the 26th International Conference on Very Large Data Bases
Moving Objects Information Management: The Database Challenge
NGITS '02 Proceedings of the 5th International Workshop on Next Generation Information Technologies and Systems
An Introduction to the Kalman Filter
An Introduction to the Kalman Filter
Filtering Location Stream in Moving Object Database
DEXA '04 Proceedings of the Database and Expert Systems Applications, 15th International Workshop
Bayesian Based Location Estimation System Using Wireless LAN
PERCOMW '05 Proceedings of the Third IEEE International Conference on Pervasive Computing and Communications Workshops
Evaluation of Spatio-temporal Predicates on Moving Objects
ICDE '05 Proceedings of the 21st International Conference on Data Engineering
A Friis-Based Calibrated Model for WiFi Terminals Positioning
WOWMOM '05 Proceedings of the Sixth IEEE International Symposium on World of Wireless Mobile and Multimedia Networks
A New Position Updating Algorithm for Moving Objects
IMSCCS '06 Proceedings of the First International Multi-Symposiums on Computer and Computational Sciences - Volume 2 (IMSCCS'06) - Volume 02
IMSCCS '06 Proceedings of the First International Multi-Symposiums on Computer and Computational Sciences - Volume 2 (IMSCCS'06) - Volume 02
Geometric Modeling and Visualization of Moving Objects on a Digital Map
GMAI '07 Proceedings of the Geometric Modelling and Imaging
Introducing a decision tree-based indoor positioning technique
Expert Systems with Applications: An International Journal
Conceptual Modeling for Moving Objects Database Applications
MDM '08 Proceedings of the The Ninth International Conference on Mobile Data Management
Efficient Similarity Join of Large Sets of Moving Object Trajectories
TIME '08 Proceedings of the 2008 15th International Symposium on Temporal Representation and Reasoning
Extended Kalman Filter for wireless LAN based indoor positioning
Decision Support Systems
A Hybrid Prediction Model for Moving Objects
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
A Data Model for Moving Objects Supporting Aggregation
ICDEW '07 Proceedings of the 2007 IEEE 23rd International Conference on Data Engineering Workshop
How to Build Your Own Moving Objects Database System
MDM '07 Proceedings of the 2007 International Conference on Mobile Data Management
Place: A Distributed Spatio-Temporal Data Stream Management System for Moving Objects
MDM '07 Proceedings of the 2007 International Conference on Mobile Data Management
Finding Probabilistic Nearest Neighbors for Query Objects with Imprecise Locations
MDM '09 Proceedings of the 2009 Tenth International Conference on Mobile Data Management: Systems, Services and Middleware
Modeling Trajectories: A Spatio-Temporal Data Type Approach
DEXA '09 Proceedings of the 2009 20th International Workshop on Database and Expert Systems Application
Set Nearest Neighbor Query for Trajectory of Moving Objects
FSKD '09 Proceedings of the 2009 Sixth International Conference on Fuzzy Systems and Knowledge Discovery - Volume 05
An efficient information access scheme for mobile objects
IRI'09 Proceedings of the 10th IEEE international conference on Information Reuse & Integration
An Adaptive Dead-Reckoning Updating Policy for Continuous Query
AICI '09 Proceedings of the 2009 International Conference on Artificial Intelligence and Computational Intelligence - Volume 02
International Journal of Intelligent Information and Database Systems
Hi-index | 12.05 |
A moving object database (MODB), a database representing information on moving objects, has many uses in a wide range of applications, such as the digital battlefield and transportation systems. In the transportation system, an MODB processes queries such as ''How long should I wait until the next bus arrives here?'' Therefore, location information on moving objects reflects the most important data the MODB has to manipulate. Most moving objects are equipped with a GPS (Global Positioning System) unit that sends location information to the MODB. However, GPS signals are usually very weak inside enclosed structures; thus, locating indoor moving objects requires more than the GPS. In this regard, indoor positioning for location-based services (LBSs) has been an important research topic for the last decade. There are many other differences between indoor and outdoor MODBs. For examples, the area where the indoor moving objects are moving around is much smaller than where the outdoor moving objects are moving around, and the speed of indoor moving objects is much slower than that of outdoor ones. Therefore, the indoor moving object database (IMODB) should be studied separately from the outdoor MODB or the MODB. One of the most important problems that the MODB has to solve is the updating problem. In this regard, this paper proposes an updating method of IMODBs for location-based services. Our method applies the Kalman filter to the most recently collected series of measured positions to estimate the moving object's position and velocity at the last moment of the series of the measurements and extrapolates the current position with the estimated position and velocity. If the difference between the extrapolated current position and the measured current position is less than the threshold, that is, if the two positions are close, we skip updating the IMODB. When the IMODB requires information on the moving object's position at a certain moment T, it applies the Kalman filter to the series of the recorded measurements at the moments before T and extrapolates the position at T with the Kalman filter in the same manner as the updating process described earlier. To verify the efficiency of our updating method, we applied our method to a series of measured positions obtained by employing the fingerprinting indoor positioning method while we walked through the test bed. We then analyzed the test results to calculate savings of communication cost and error.