Directed diffusion: a scalable and robust communication paradigm for sensor networks
MobiCom '00 Proceedings of the 6th annual international conference on Mobile computing and networking
The cougar approach to in-network query processing in sensor networks
ACM SIGMOD Record
Gossip-Based Computation of Aggregate Information
FOCS '03 Proceedings of the 44th Annual IEEE Symposium on Foundations of Computer Science
Managing uncertainty in sensor database
ACM SIGMOD Record
Distributed deviation detection in sensor networks
ACM SIGMOD Record
Approximate Aggregation Techniques for Sensor Databases
ICDE '04 Proceedings of the 20th International Conference on Data Engineering
Energy-efficient surveillance system using wireless sensor networks
Proceedings of the 2nd international conference on Mobile systems, applications, and services
Compressing historical information in sensor networks
SIGMOD '04 Proceedings of the 2004 ACM SIGMOD international conference on Management of data
Prediction and indexing of moving objects with unknown motion patterns
SIGMOD '04 Proceedings of the 2004 ACM SIGMOD international conference on Management of data
Balancing energy efficiency and quality of aggregate data in sensor networks
The VLDB Journal — The International Journal on Very Large Data Bases
Snapshot Queries: Towards Data-Centric Sensor Networks
ICDE '05 Proceedings of the 21st International Conference on Data Engineering
TAG: a Tiny AGgregation service for Ad-Hoc sensor networks
OSDI '02 Proceedings of the 5th symposium on Operating systems design and implementationCopyright restrictions prevent ACM from being able to make the PDFs for this conference available for downloading
REED: robust, efficient filtering and event detection in sensor networks
VLDB '05 Proceedings of the 31st international conference on Very large data bases
Contour map matching for event detection in sensor networks
Proceedings of the 2006 ACM SIGMOD international conference on Management of data
Constraint chaining: on energy-efficient continuous monitoring in sensor networks
Proceedings of the 2006 ACM SIGMOD international conference on Management of data
Distributed set-expression cardinality estimation
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Model-driven data acquisition in sensor networks
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Building Efficient Aggregation Trees for Sensor Network Event-Monitoring Queries
GSN '09 Proceedings of the 3rd International Conference on GeoSensor Networks
Energy-efficient processing of spatio-temporal queries in wireless sensor networks
Proceedings of the 18th SIGSPATIAL International Conference on Advances in Geographic Information Systems
Collection trees for event-monitoring queries
Information Systems
Detecting proximity events in sensor networks
Information Systems
Enabling knowledge extraction from low level sensor data
DEXA'11 Proceedings of the 22nd international conference on Database and expert systems applications - Volume Part II
Data transformation and query management in personal health sensor networks
Journal of Network and Computer Applications
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Sensor networks are often used to perform monitoring tasks, such as in animal or vehicle tracking and in surveillance of enemy forces in military applications. In this paper we introduce the concept of proximity queries that allow us to report interesting events that are observed by nodes in the network that are within certain distance of each other. An event is triggered when a user-programmable predicate is satisfied on a sensor node. We study the problem of computing proximity queries in sensor networks using existing communication protocols and then propose an efficient algorithm that can process multiple proximity queries, involving several different event types. Our solution utilizes a distributed routing index, maintained by the nodes in the network that is dynamically updated as new observations are obtained by the nodes. We present an extensive experimental study to show the benefits of our techniques under different scenarios. Our results demonstrate that our algorithms scale better and require orders of magnitude fewer messages compared to a straightforward computation of the queries.