A data model and data structures for moving objects databases
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
A foundation for representing and querying moving objects
ACM Transactions on Database Systems (TODS)
Physical layer driven protocol and algorithm design for energy-efficient wireless sensor networks
Proceedings of the 7th annual international conference on Mobile computing and networking
The cougar approach to in-network query processing in sensor networks
ACM SIGMOD Record
IEEE Transactions on Knowledge and Data Engineering
The Geometry of Uncertainty in Moving Objects Databases
EDBT '02 Proceedings of the 8th International Conference on Extending Database Technology: Advances in Database Technology
Moving Objects Databases: Issues and Solutions
SSDBM '98 Proceedings of the 10th International Conference on Scientific and Statistical Database Management
Towards Sensor Database Systems
MDM '01 Proceedings of the Second International Conference on Mobile Data Management
Developments in Spatio-Temporal Query Languages
DEXA '99 Proceedings of the 10th International Workshop on Database & Expert Systems Applications
TAG: a Tiny AGgregation service for ad-hoc sensor networks
ACM SIGOPS Operating Systems Review - OSDI '02: Proceedings of the 5th symposium on Operating systems design and implementation
Approximate Aggregation Techniques for Sensor Databases
ICDE '04 Proceedings of the 20th International Conference on Data Engineering
Managing uncertainty in moving objects databases
ACM Transactions on Database Systems (TODS)
Balancing energy efficiency and quality of aggregate data in sensor networks
The VLDB Journal — The International Journal on Very Large Data Bases
A line in the sand: a wireless sensor network for target detection, classification, and tracking
Computer Networks: The International Journal of Computer and Telecommunications Networking - Special issue: Military communications systems and technologies
TinyDB: an acquisitional query processing system for sensor networks
ACM Transactions on Database Systems (TODS) - Special Issue: SIGMOD/PODS 2003
Modeling and querying moving objects in networks
The VLDB Journal — The International Journal on Very Large Data Bases
Security for the Mythical Air-Dropped Sensor Network
ISCC '06 Proceedings of the 11th IEEE Symposium on Computers and Communications
Set Membership Classification: A Unified Approach to Geometric Intersection Problems
IEEE Transactions on Computers
Processing proximity queries in sensor networks
DMSN '06 Proceedings of the 3rd workshop on Data management for sensor networks: in conjunction with VLDB 2006
The design and implementation of a declarative sensor network system
Proceedings of the 5th international conference on Embedded networked sensor systems
Long-Term Animal Observation by Wireless Sensor Networks with Sound Recognition
WASA '09 Proceedings of the 4th International Conference on Wireless Algorithms, Systems, and Applications
Deriving spatio-temporal query results in sensor networks
SSDBM'10 Proceedings of the 22nd international conference on Scientific and statistical database management
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Research on Moving Object Databases (MOD) has resulted in sophisticated query mechanisms for moving objects and regions. Wireless Sensor Networks (WSN) support a wide range of applications that track or monitor moving objects. However, applying the concepts of MOD to WSN is difficult: While MOD tend to require precise object positions, the information acquired in WSN may be incomplete or inaccurate. This may be because of limited detection ranges, node failures or detection mechanisms that only determine if an object is in the vicinity of a node, but not its exact position. In this paper, we study the processing of spatiotemporal queries in WSN. First, we adapt the models used in MOD to WSN while keeping their semantical depth. Second, we propose two approaches for processing such queries in WSN in-network instead of collecting all data at the base station. Our experimental evaluations using simulation as well as a Sun SPOT deployment show that our measures reduce communication by up to 89%, compared to collecting all information at the base station.