Intelligent Sampling over Wireless Sensor Networks

PhD Thesis Proposal Defence


Title: "Intelligent Sampling over Wireless Sensor Networks"

by

Miss Yongzhen Zhuang


Abstract:

Nowadays, wireless sensor networks are being widely used in many
monitoring applications, such as monitoring farm belts, large-scale
habitats, environmental disasters, and so on and so forth. The tiny
distributed sensors provide samples of environmental parameters, for
example, temperature, humidity, gas pressure, decibel levels, to the
applications. Since sensors are always constrained by their limited
battery power, how to efficiently use a large number of sensors and
their samples according to the application needs is of great importance.
More specifically, we discuss two types of sampling techniques, temporal
and spatial.

Temporal sampling decides how many samples a sensor should obtain to,
for example, process a query, clean the data, or detect an event. In
other words, temporal sampling adjusts the sampling rates to achieve
enough accuracy in the application. It is known that tiny imprecise
sensors are of low data quality and are easily affected by random noise.
They cannot provide bounded accuracy for the applications built upon
them. One way to increase the accuracy is by multiple sampling, which is
the most effective and straightforward technique to improve the data
quality. The basic idea is to obtain multiple samples from either one or
several sensors, and use their average in the applications. The
enforcement of this multiple sampling technique in sensor network
applications is not trivial, for the sensed data are distributed
time series and sensors have to collaborate to achieve the overall
accuracy requirement of the application. Our goal is to use an
appropriate number of samples in each sensor depending on the
application requirements. We mainly focus on three applications, query
processing, data cleaning and event detection. In the query application,
we build a query plan to minimize the sampling cost while satisfying the
accuracy constraint. In the data cleaning application, we use weighted
moving average with weights came from a sampling process to efficiently
reconstruct the true data with low sampling costs. In the event
detection application, we use range prediction and a hypothesis test to
locate events in realtime with high confidence.

On the other hand, spatial sampling focuses on selecting a subset of
sensors to be used in a monitoring application. In many applications,
spatial sampling can dramatically reduce the energy consumption because
the sensors that are not selected can switch themselves to a low-cost
sleep mode. Spatial sampling can have many alternatives, depending on the
application requirements. For example, clustering-based sampling, such as
snapshot sampling, uses a representative sensor to substitute for the
similar sensors nearby. Our ongoing works of spatial sampling mainly focus
on the following directions: (1) We model the event detection application
as regional pattern queries, and propose a spatial sampling approach based
on random walk to find the pattern regions. We show that it is more
efficient to walk through a random path to locate the pattern region than
abundantly revoke many irrelevant sensors, especially when the monitoring
field is huge and the pattern regions are rare. (2) In many applications,
the placement of the sensors can be quite irregular due to the
construction of the monitoring area (steep rocky, moderate incline, or
flat silty) and sorts of natural effects (flood or wind). We propose an
unbiased sampling technique that selects and weights sensors so that to
provide an unbiased representation to the application as if the sensors
are deployed uniformly. (3) We present a section sampling technique that
groups sensors into random sections. Each single section can represent the
monitoring field by its own. We can achieve more energy efficiency by
using fewer sections, or high query accuracy by using more sections. (4)
We propose a new model for the event detection application that defines
events as the top-k regions with the highest regional statistics (e.g.
regional sum). We try to adopt the sampling techniques to provide an
accurate and energy efficient approximation of the top-k regions.


Date:     		Thursday, 10 January 2008

Time:                   10:00a.m.-12:00noon

Venue:                  Room 3501
			lifts 25-26

Committee Members:      Dr. Lei Chen (Supervisor)
			Prof. Frederick Lochovsky (Chairperson)
                        Dr. Shing-Chi Cheung
			Prof. Vincent Shen


**** ALL are Welcome ****