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
Negotiation-based protocols for disseminating information in wireless sensor networks
Wireless Networks - Selected Papers from Mobicom'99
ASCENT: Adaptive Self-Configuring sEnsor Network Topologies
ACM SIGCOMM Computer Communication Review
Sampling from a moving window over streaming data
SODA '02 Proceedings of the thirteenth annual ACM-SIAM symposium on Discrete algorithms
Mining Sequential Patterns: Generalizations and Performance Improvements
EDBT '96 Proceedings of the 5th International Conference on Extending Database Technology: Advances in Database Technology
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Towards Sensor Database Systems
MDM '01 Proceedings of the Second International Conference on Mobile Data Management
The design of an acquisitional query processor for sensor networks
Proceedings of the 2003 ACM SIGMOD international conference on Management of data
Fjording the Stream: An Architecture for Queries Over Streaming Sensor Data
ICDE '02 Proceedings of the 18th International Conference on Data Engineering
Nile: A Query Processing Engine for Data Streams
ICDE '04 Proceedings of the 20th International Conference on Data Engineering
Approximate Aggregation Techniques for Sensor Databases
ICDE '04 Proceedings of the 20th International Conference on Data Engineering
Hash-Merge Join: A Non-blocking Join Algorithm for Producing Fast and Early Join Results
ICDE '04 Proceedings of the 20th International Conference on Data Engineering
Habitat monitoring with sensor networks
Communications of the ACM - Wireless sensor networks
Airborne traffic surveillance systems: video surveillance of highway traffic
Proceedings of the ACM 2nd international workshop on Video surveillance & sensor networks
SPASS: scalable and energy-efficient data acquisition in sensor databases
Proceedings of the 4th ACM international workshop on Data engineering for wireless and mobile access
Nile-PDT: a phenomenon detection and tracking framework for data stream management systems
VLDB '05 Proceedings of the 31st international conference on Very large data bases
Stream window join: tracking moving objects in sensor-network databases
SSDBM '03 Proceedings of the 15th International Conference on Scientific and Statistical Database Management
Detection and tracking of discrete phenomena in sensor-network databases
SSDBM'2005 Proceedings of the 17th international conference on Scientific and statistical database management
Scheduling for shared window joins over data streams
VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29
Processing sliding window multi-joins in continuous queries over data streams
VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29
Model-driven data acquisition in sensor networks
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Beyond average: toward sophisticated sensing with queries
IPSN'03 Proceedings of the 2nd international conference on Information processing in sensor networks
Boundary estimation in sensor networks: theory and methods
IPSN'03 Proceedings of the 2nd international conference on Information processing in sensor networks
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Recent advances in large-scale sensor-network technologies enable the deployment of a huge number of sensors in the surrounding environment. Sensors do not live in isolation. Instead, close-by sensors experience similar environmental conditions. Hence, close-by sensors may indulge in a correlated behavior and generate a “phenomenon”. A phenomenon is characterized by a group of sensors that show “similar” behavior over a period of time. Examples of detectable phenomena include the propagation over time of a pollution cloud or an oil spill region. In this research, we propose a framework to detect and track various forms of phenomena in a sensor field. This framework empowers sensor database systems with phenomenon-awareness capabilities. Phenomenon-aware sensor database systems use high-level knowledge about phenomena in the sensor field to control the acquisition of sensor data and to optimize subsequent user queries. As a vehicle for our research, we build the Nile-PDT system, a framework for Phenomenon Detection and Tracking inside Nile, a prototype data stream management system developed at Purdue University.