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Helping Students Find Uncongested Study Spaces

Using a Raspberry Pi 4, a PiTFT Display, and Data  It is often difficult for students to find available places to study on large college campuses. In this article, we describe a method by which places to study at Cornell University can be located. We used Internet traffic data collected from wireless access points as a proxy for congestion, and for finding the least congested study spaces and best routes to them. At most universities, we typically find students texting, calling, or checking upcoming class schedules on campus with their smartphones, which are connected to the campus Internet. Of course, they may also carry a laptop, tablet, and other electronic equipment. When these devices are connected to the Internet through Wireless Access Points (WAPs), the resulting traffic data, when drilled down to the physical location level, can serve as a proxy for the level of campus congestion across campus. Our project, called Campus Congestion, is an embedded-system project that shows a graphical representation of the level of congestion in common areas in Cornell University’s Engineering Quad. It is used to help students find available places to study. We had four project objectives: Access the data from the different APs that are present in a room. •Create a graphical representation over a map of the level of campus congestion in common areas. • Offer a ranked list of study spaces, prioritizing the ones with the least amount of campus congestion, and offer routes to get there. • Allow the user to control the progression through the menus and select options with either mouse clicks on a computer monitor, or by “remote control” buttons on a PiTFT display screen connected to a Raspberry Pi 4. This project utilizes the public Multi Router Traffic Grapher (MRTG) website, provided by Cornell’s IT department. An example of MRTG graph data is shown in Figure 1. By leveraging the network traffic data of the Local Area
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Helping Students Find Uncongested Study Spaces

by Minjung Kwon & Esther In time to read: 15 min