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Integrating Sensor Networks in Undergraduate Curriculum: A Marriage between Theory and Practice

Introduction

Wireless Sensor Networks are composed of small nodes equipped with sensor(s), a wireless radio, and limited computational power. Sensor nodes are used as data collectors and also in data forwarding. The nodes collect the sensed data and use their neighbors to forward it to the destination. In general, a network is composed of several sensor nodes and one or more destinations or gateway (also called “sink”) nodes that receive the collected data and may process it and take the appropriate actions. The network can have a large or small number of nodes depending on the needs of the application. Sensor networks were originally motivated by military applications such as surveillance, but were quickly found to be useful in diverse civilian applications, such as environmental monitoring, healthcare, home automation, space exploration, chemical processing, disaster relief, etc. This diverse set of applications, spanning multiple fields, sparks interests of students with varied backgrounds.

In this paper, we detail the ongoing efforts at Drexel University, aimed at adapting the successes of previous experiences in teaching sensor networks at the undergraduate level1-6, to create a new laboratory-based undergraduate course in sensor networks, and to make extensive use of the new laboratory’s modular experiments in other courses and disciplines. The project is funded by NSF CCLI program of the Division of Undergraduate Education.

Sensor networks as a pedagogical tool

We believe that sensor network experiments can be very pedagogical in illustrating many abstract concepts in other courses/disciplines. For example, medium access and routing protocols can be used in undergraduate networking sequence courses; basics of radio communication can illustrate concepts in an introductory telecommunication course; a Digital Signal Processing (DSP) sequence dealing with image/biomedical modeling and interpretation, using curve modeling with parametric models such as Bezier and B-splines could be illustrated with an experiment dealing with traffic engineering where students are asked to route important data from a node to a destination node while adhering to a predetermined trajectory described by such parametric models; as part of the stochastic system course, students are introduced to detection and hypothesis testing theory and Receiver Operating Characteristic (ROC) analysis. An experiment can be developed to ask the students to track a moving toy vehicle in a room by integrating and routing the proximity data from the sensors to a sink node for data and decision fusion. Similarly, simple probability concepts taught at the junior level can be illustrated with simple experiments.

Aiming at a tight integration of theoretical concepts with a hands-on laboratory experience, we designed modular experiments to be used as a platform to naturally improve understanding of networking and concepts in other courses such as DSP, stochastic and non-deterministic systems, biomedical imaging, as well as freshman and senior design projects. This will provide the students with an exciting learning environment and will make the mastery of important STEM

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Elancheziyan, A., & de Oliveira, J., & Cohen, F., & Reisman, F. (2008, June), Integrating Sensor Networks In Undergraduate Curriculum: A Marriage Between Theory And Practice Paper presented at 2008 Annual Conference & Exposition, Pittsburgh, Pennsylvania. 10.18260/1-2--4199