Phenomenon-Aware Sensor Database Systems

  • M. H. Ali
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4254)


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.


Sensor Network Cluster Head Query Processing Relevance Feedback Mining Association Rule 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • M. H. Ali
    • 1
  1. 1.Department of Computer SciencePurdue University 

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