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Sensorworld: A New Approach To Incorporating Large Scale Sensor Data Into Engineering Learning Environments

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Conference

2010 Annual Conference & Exposition

Location

Louisville, Kentucky

Publication Date

June 20, 2010

Start Date

June 20, 2010

End Date

June 23, 2010

ISSN

2153-5965

Conference Session

Signal Processing Education

Tagged Division

Computers in Education

Page Count

10

Page Numbers

15.1059.1 - 15.1059.10

Permanent URL

https://peer.asee.org/16761

Download Count

17

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Paper Authors

author page

Hanjun Xian Purdue University

author page

Krishna Madhavan Purdue University

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Abstract
NOTE: The first page of text has been automatically extracted and included below in lieu of an abstract

SensorWorld: A New Approach to Incorporating Large-scale Sensor Data into Engineering Learning Environments

Abstract

Sensors play a critical role in engineering and science applications. However, most engineering students very rarely have access to large-scale real-world sensor data within the classrooms. Students who major in fields such as environmental engineering are not well prepared for the engineering professions because of the gap between real-world scenarios and scale of the data used within the classrooms. Diverse and non-standard software interfaces to sensors compound this problem significantly. Our goal is to document and make available data from a large variety of real-world sensors to engineering students through the iPhone and iPod Touch. Our project addresses this problem by implementing a middleware framework in the application server and a client on iPhone to facilitate access to sensor data.

The primary research questions that this paper will address are: (1) How can sensor data be incorporated into current engineering learning environments effectively? (2) What are the problems of utilizing large-scale data within the scope of an engineering curriculum? and (3) What are the characteristics of a middleware framework that will allow the inclusion of real- world data sources within the classroom? Currently, we support a total of 1136 sensors from a variety of sources. This dataset contains sensor data of air temperature, water temperature, water level, wind speed, air pressure, precipitation, conductivity, and soil moisture, and is being rapidly expanded to support a large universal set of open sensors.

Success of this project provides a chance to bring practice-oriented education into engineering classrooms. Students will be able to access real-time, real-world sensor data with a single iPhone application. Effective visualization and interface for navigation of sensor data helps engineering students better understand concepts, identify patterns, and discover problems not addressed in the textbooks. Engineering students are likely to be more engaged in the learning process by studying the latest natural phenomenon such as flooding in Atlanta and drought in Texas.

1. Introduction

Sensors play a critical role in engineering and science applications such as monitoring environmental metrics, controlling industrial processes, and coordinating traffic flow. Inclusion of sensing science (also known as sensor science) and sensor data within engineering classrooms is becoming increasingly beneficial for engineering education. It motivates students to pursue science and engineering disciplines and associated career paths1. Further, it makes the teaching in the laboratory more interesting2 and engaging3. Furthermore, sensor science helps prepare students with a foundation of instrumentation technology for the measurement and control of industrial processes4. Despite the above efforts to produce a prevailing culture of sensing science, the vast majority of engineering students very rarely have access to a large number of real-world sensors within the classrooms. A lack of effective ways to incorporate large-scale sensor data into engineering curricula retards students’ development of problem solving skills in a real-world contexts.

Xian, H., & Madhavan, K. (2010, June), Sensorworld: A New Approach To Incorporating Large Scale Sensor Data Into Engineering Learning Environments Paper presented at 2010 Annual Conference & Exposition, Louisville, Kentucky. https://peer.asee.org/16761

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