Author
MH Ali, MF Mokbel, WG Aref, I Kamel
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.
