A new Innovation Generation grant from the Motorola Solutions Foundation will help Array of Things expand its educational curriculum to additional Chicago Public Schools classrooms in 2018. Building upon two successful years of workshops with over 300 students at Lane Tech High School, the program will train teachers and package materials for a hands-on experience with the Internet of Things, coding, data science, and other key computer science and technology concepts.
For the second year running, Array of Things worked with Chicago’s Lane Tech High School on a workshop curriculum called “Lane of Things.” Over eight weeks, students in Lane Tech’s Innovation and Creation Lab and Physical Computing Lab worked with scientists and designers from the Array of Things project on designing and building their own sensor boxes. Teams then deployed those boxes around the school, collecting data on foot traffic, air quality, noise, student behavior, and more.
There were many motivations for Chicago’sArray of Things (AoT) project. While it is primarily an experimental research platform to measure the city and provide researchers with a testbed for new “smart city” concepts, AoT also aspires to be a research platform for Chicago educators, students, and residents.
As was the case last year, over 150 students worked in teams of three to learn about science, measurement, design and problem solving, data analytics, teamwork, and in the process, acquire hands-on experience with the concept of “Internet of Things” (or “IoT”)—an underlying enabler of the Array of Things.
For this second year of the program, IoT device platform makers Particle joined the team, providing the students with programmable, wireless networked microprocessors that served as the internal brains for their sensor “motes.” Students programmed these devices—calledPhotons—using Particle’s web portal. They learned how to make different internet services interact; for instance, programming the Photons to send data to online spreadsheets, streamlining the process of collecting and analyzing data.
Over the course of eight weeks, the student teams learned important skills, such as:
1.Formulating an hypothesis or question to be answered through experimentation. Although the curriculum revolves around sensors and IoT technologies, these are means rather than ends. To conduct a real scientific project, student teams first conceived of an hypothesis or a question. A good example is the project fromGroup 403. These students were interested in learning whether there is “a correlation between temperature, humidity, carbon monoxide, hydrogen, and UV levels in a greenhouse. The greenhouse gets the most sunlight out of any room in Lane Tech, so we wondered if there was any correlation with UV from the sun and the various gas levels in the room.”
2.Developing an experiment. Here the students designed a device that would take measurements relevant to their hypothesis or question.Group 708 designed a device that would let them measure “whether or not the time of day influences the amount of people that enter/exit the attendance office.” Typically, such an experiment would involve an observer with a clipboard, but the students used a motion sensor, placed in the doorway to the attendance office.
3.System design and problem solving. Once student groups decided on what kind of measurement to do, and what kind of sensor would be needed, they learned how to build the electronics that would use the sensor to gather data, as well as an enclosure and mounting system to position their devices for optimal measurement.Group 678 needed to enclose and mount their device in the dance studio in order “to record the level of sound in terms of volume and how loud a room gets, the temperature and humidity, and [use] a motion sensor to get an estimate of how many people walk by, get close, or interact with the photon. The three can all show correlation to how active the classroom is.”
4.Data analytics and web applications. While taking measurements for two weeks, project teams stored their data in online databases and spreadsheets, allowing them to graph and analyze the data. To do this, students learned how to expose variables to Particle’s cloud, then program online databases and spreadsheets to pull that data.Group 709 built a system to measure water temperature and clarity in Lane Tech’s aquaponics laboratory in order to provide “early warning” of system failure. They used graphs to analyze the data and concluded that their “data did not reveal any tampering or major failures in the aquaponic system (which is good) and we are confident that the mote would have detected a major problem if it had occurred. We are considering leaving the mote up over the summer when power to the aquaponic system is most likely to be accidentally shut off.”
5.Teamwork. Unexpected challenges often bring out the best in teams. Group 402 discovered a hardware issue that delayed their installation, and had to pull together under a deadline to resolve it. They describe it quite well: “The day before we were supposed to deploy, we came into class and found that the pins connecting the wires on our sound sensor were broken off. So, we spent the whole period re-soldering the sensor and wiring it to the breadboard. We missed at least 3-4 days of data pulling, but it all worked out in the end.”
What’s Next for Lane of Things?
The LoT team is already working on packaging the curriculum and developing a workshop to enable faculty from other high schools to bring the program to their schools. And of course, the team is eagerly preparing for next year’s program, which will include teaching the students how to use theArray of Things application programming interfaces to incorporate data from the Array of Things! If you are a teacher or school representative interested in participating in future versions of Lane of Things, please contact us at firstname.lastname@example.org.
- Charlie Catlett, Director, Urban Center for Computation and Data
The Hawaii International Conference on System Sciences (HICSS-51) will be January 3-6, 2018, at Hilton Waikoloa Village on Hawaii’s Big Island. We are organizing the minitrack "Turning Smart: Challenges and Experiences in Smart Application Development" at the conference. The conference provides a unique and highly interactive environment for researchers to exchange perspectives and ideas in various areas of information, computer, and system sciences.
Speaking at SC17 in Denver this week, a panel of smart city practitioners shared the strategies, techniques and technologies they use to understand their cities better and to improve the lives of their residents. With data coming in from all over the urban landscape and worked over by machine learning algorithms, Debra Lam, managing director for smart cities & inclusive innovation at Georgia Tech who works on strategies for Atlanta and the surrounding area, said “we’ve embedded research and development into city operations, we’ve formed a match making exercise between the needs of the city coupled with the most advanced research techniques.”
Panel moderator Charlie Cattlett, director, urban center for computation & data Argonne National Laboratory who works on smart city strategies for Chicago, said that the scale of data involved in complex, long-term modeling will require nothing less than the most powerful supercomputers, including the next generation of exascale systems under development within the Department of Energy. The vision for exascale, he said, is to build “a framework for different computation models to be coupled together in multiple scales to look at long-range forecasting for cities.”
Researchers at the Urban Center for Computation and Data, an initiative by the University of Chicago and Argonne National Laboratory, have developed equipment that is being posted on light poles around the city to provide granular details about air quality, traffic, sound volume and temperature.
After working out glitches with the electronics and redesigning protective enclosures for the devices, dubbed the Array of Things, the scientists are planning to have 500 monitors up and running by the end of next year.
Charlie Catlett, a data scientist who directs the project, said the goal is to provide researchers and the public with new kinds of data that can be used to improve quality of life. The latest version of the monitors is designed to make it easier to add new technology as the field improves and expands.