20-11-2012, 05:38 PM
Wireless Sensor Networks—A Hands-On Modular Experiments Platform for Enhanced Pedagogical Learning
1Wireless Sensor Networks.pdf (Size: 678.02 KB / Downloads: 38)
Abstract
This paper presents the use of wireless sensor networks
(WSNs) in educational research as a platform for enhanced
pedagogical learning. The aim here with the use of a WSN platform
was to go beyond the implementation stage to the real-life application
stage, i.e., linking the implementation to real-life applications,
where abstract theory and algorithmic implementations
become natural tools for the execution of application itself. Abstract
theoretical concepts are illustrated through hands-on modular
experiments in a host of diverse electrical and computer engineering
courses. The WSN consists of Mica2 motes with on-board
sensors, wireless communication antennas, and processors that are
programmed using NesC. Three sets of experiments feeding into
different courses [on topics such as wireless embedded networks,
detection and estimation theory, stochastic processes, probability
theory, statistical pattern recognition, and digital signal processing
(DSP)] and illustrating different theoretical concepts are presented
in detail. These experiments can be used as demos in those courses
and/or can be incorporated as hands-on laboratory projects to go
hand in hand with the course in which they are introduced. Also
presented is the assessment of the experiments as pedagogical tools,
made by means of well-designed evaluation questionnaires given to
the students. Both the sensor network platform and the novel experiments
built on this platform are found to be pedagogically successful
tools for learning about and teaching the theoretical concepts
introduced in those courses.
INTRODUCTION
TRANSLATING abstract theory into real-life application
problems is extremely important in engineering education.
Most of the time, it is difficult for students to link advanced
theoretical concepts in courses to real-life applications.
Hence, bridging this gap between theory and applications is
highly needed, timely, and very desirable. To that end, the work
described in this paper integrates a practical approach to assist
the learning and understanding of various theoretical concepts
in a wide range of electrical and computer engineering courses
through the use of wireless sensor networks (WSNs).
WSN is an emerging new field that draws applications from
diverse disciplines in engineering and science and stands as an
excellent candidate to be used as a platform for illustrating abstract
theoretical concepts in diverse courses in electrical and
computer engineering curricula.
There are various institutions working on incorporating applied
WSNs in courses on embedded systems [2]–[11]. This
work complements these activities, but also differs from them
in the use of the WSN platform as a pedagogical tool to teach
students advanced-level theoretical concepts in a variety of electrical
engineering courses. Judiciously designed WSN hands-on
experiments make these concepts more tangible, more related
to applications, and easier for the students to grasp. WSN has
been used in a variety of courses outside of the networking
area, in topics such as digital signal processing (DSP), stochastic
and nondeterministic systems, pattern recognition, and computer
vision.
WIRELESS SENSOR NETWORKS PLATFORM EXPERIMENTS
Exciting hands-on experiments have been developed for a
wide range of courses to facilitate understanding of the application
of theory to real-life problems. Three different experiments
are presented here as prototypes, using wireless sensor
networks as a platform. The first experiment is a basic target
detection experiment designed to be used in a WSN lab course
in order to show students the basic concepts of communication
between nodes and a cluster node, as well as sync. The second
experiment presents local, selected, and consensus decisions in
target detection and is designed to illustrate concepts of detection
and decision making in advanced courses dealing with detection
and estimation theory, such as Statistical Engineering
Theory, Statistical Pattern Recognition, and Digital Signal Processing.
The third experiment deals with motion tracking and
includes various concepts from detection and estimation theory,
motion estimation, and dynamic updates. This third experiment
has been used in Drexel University’s (Philadelphia, PA) Signal
Processing, Computer Vision, Detection and Estimation, and
Pattern Recognition courses.
CONCLUSION
Through hands-on experiments built on MICA2 motes (with
on-board sensors, wireless communication antennas, and its
own processors programmed using NesC), it has been shown
that how various theoretical abstract concepts in a variety of
ECE courses can be made tangible and easier to understand.
This is particularly true for those theoretical concepts that are
traditionally hard to grasp using the traditional lecturing/homework/
exams method. The proposed experiments based on the
WSN platform are shown to be exciting pedagogical learning
tools that are portable to various multidisciplinary existing
courses ranging from junior/senior to the graduate level.
This work was presented at Drexel Research Day 2008 (an
annual event where demos and posters are exhibited, presented,
and viewed by the university at large, as well as by visitors from
outside the university and also from industry), and the significance
of this work was recognized with the with “Best Poster
Presentation Award” under the category of “Innovation in Education
and Outreach.”