Detection and Tracking of Discrete Phenomena in Sensor-Network Databases

This paper introduces a framework for Phenomena Detection and Tracking (PDT, for short) in sensor network databases. Examples of detectable phenomena include the propagation over time of a pollution cloud or an oil spill region. We provide a crisp definition of a phenomenon that takes into consideration both the strength and the time span of the phenomenon. We focus on discrete phenomena where sensor readings are drawn from a discrete set of values, e.g., item numbers or pollutant IDs, and we point out how our work can be extended to handle continuous phenomena. The challenge for the proposed PDT framework is to detect as much phenomena as possible, given the large number of sensors, the overall high arrival rates of sensor data, and the limited system resources. Our proposed PDT framework uses continuous SQL queries to detect and track phenomena. Execution of these continuous queries is performed in three phases; the joining phase, the candidate selection phase, and the grouping/out...

• Type: Conference
• Year: 2005
• Where: SSDBM
• Authors: Mohamed H. Ali, Mohamed F. Mokbel, Walid G. Aref, Ibrahim Kamel
• Keywords: Candidate Selection Phase, Database, PDT Framework, Phenomena, SSDBM 2005