Ad Hoc Networks. First International Conference, ADHOCNETS 2009, Niagara Falls, Ontario, Canada, September 22-25, 2009. Revised Selected Papers

Research Article

Enhancing Learning Using Modular Wireless Sensor Networking (WSN) Hands-On Experiments

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  • @INPROCEEDINGS{10.1007/978-3-642-11723-7_34,
        author={Ezgi Taslidere and Fernand Cohen and Fredricka Reisman},
        title={Enhancing Learning Using Modular Wireless Sensor Networking (WSN) Hands-On Experiments},
        proceedings={Ad Hoc Networks. First International Conference, ADHOCNETS 2009, Niagara Falls, Ontario, Canada, September 22-25, 2009. Revised Selected Papers},
        proceedings_a={ADHOCNETS},
        year={2012},
        month={7},
        keywords={Wireless sensor networks educational research target detection receiver operating characteristics (ROC) curve decision making motion tracking},
        doi={10.1007/978-3-642-11723-7_34}
    }
    
  • Ezgi Taslidere
    Fernand Cohen
    Fredricka Reisman
    Year: 2012
    Enhancing Learning Using Modular Wireless Sensor Networking (WSN) Hands-On Experiments
    ADHOCNETS
    Springer
    DOI: 10.1007/978-3-642-11723-7_34
Ezgi Taslidere1,*, Fernand Cohen1,*, Fredricka Reisman1,*
  • 1: DREXEL UNIVERSITY
*Contact email: ezgi@drexel.edu, fscohen@drexel.edu, reismafk@drexel.edu

Abstract

This paper presents the use of WSN in educational research as a platform for enhanced learning through hands-on modular experiments to illustrate abstract theoretical concepts in diverse courses in Electrical Engineering. 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 (e.g., wireless embedded networks, detection and estimation, stochastic processes, probability theory, statistical pattern recognition, and digital signal processing) and illustrating different theoretical concepts are presented in details. 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. Assessment of the experiments as pedagogical tools are also presented through well designed evaluation questionnaires given to the students. Assessment survey shows that both the sensor network platform and the novel experiments built on them are pedagogically successful tools.