Detection and tracking of discrete phenomena in sensor-network databases

M. H. Ali, Mohamed Mokbel, G. Aref Walid, Kamel Ibrahim

Research output: Contribution to journalConference article

20 Citations (Scopus)

Abstract

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/output phase. The joining phase employs an in-memory multi-way join algorithm that produces a set of sensor pairs with similar readings. The candidate selection phase filters the output of the joining phase to select candidate join pairs, with enough strength and time span, as specified by the phenomenon definition. The grouping/ output phase constructs the overall phenomenon from the candidate join pairs. We introduce two optimizations to increase the likelihood of phenomena detection while using less system resources. Experimental studies illustrate the performance gains of both the proposed PDT framework and the proposed optimizations.

Original languageEnglish
Pages (from-to)163-172
Number of pages10
JournalProceedings of the International Conference on Scientific and Statistical Database Management, SSDBM
Publication statusPublished - 1 Dec 2005
Externally publishedYes

Fingerprint

Sensor networks
Joining
Sensors
Oil spills
Pollution
Data storage equipment

ASJC Scopus subject areas

  • Software
  • Information Systems

Cite this

Detection and tracking of discrete phenomena in sensor-network databases. / Ali, M. H.; Mokbel, Mohamed; Walid, G. Aref; Ibrahim, Kamel.

In: Proceedings of the International Conference on Scientific and Statistical Database Management, SSDBM, 01.12.2005, p. 163-172.

Research output: Contribution to journalConference article

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