Collecting and Visualizing Real Time Environment Data in the City of Charlottetown

A presentation to the Atlantic Association of Planning Technicians Annual Meeting on June 12, 2019 in Sackville, NB.

Transcript

1. Collecting and Visualizing Real Time Environment Data in the City of Charlottetown Atlantic Association of Planning Technicians June 12, 2019 Peter Rukavina Reinvented Inc., Charlottetown https://ruk.ca/
2. The Story Begins I became interested in where we get our electricity on Prince Edward Island.
3. Electricity Flow
6. https://tso.nbpower.com/Public/en/SystemInformation_realtime.asp
9. Golf at 50BPM by user707748013 50 MW 50 BPM
10. W&W - Bigfoot (Uriel M Deephouse Remix) by urielm 122 MW 122 BPM
11. Wind Energy
16. 155% of our electricity load from the wind 10:30 p.m. 7:30 a.m. 8 Hours
19. Home Electricity 
 and Water
31. Taking Stock
32. • Sensors are cheap, and there are a lot of things we can measure. • Being peripherally aware of things we can measure can positively alter our behaviour. • Data communication via wifi is a technical and support challenge, and doesn’t scale.
33. Measure Your City
40. LoRaWAN • LoRa = Long Range (and low power) • LoRaWAN is a networking protocol for connecting gateways and devices. • Excellent for transmitting small amounts of data over long distances with little power. • Helps solve the “data communication over wifi is a customer support nightmare” issue. • Helps solve the “wifi devices use a lot of power” issue.
44. SNACKS Sensor Network Around Charlottetown’s Key Surroundings
53. SNACKS Stack • Sensor nodes (low-powered Arduino boards) broadcast readings, which are picked up by strategically-placed The Things Network gateways. • Gateways send readings to The Things Network. • Application on The Things Network sends readings to Amazon IoT service where they are archived in a database. • A web application visualizes the archived readings.
55. SNACKS Challenges • Battery management in off-the-shelf LoRaWAN-equipped Arduino devices isn’t great (~2 weeks battery life or less). • “Onboarding” a new sensor node is more complicated than it needs to be. • Visualization technique hasn’t been real-world-tested yet, so we don’t know if it’s optimal.
56. SNACKS Next • Work on battery life issues (possibly adopting Amersfoort’s custom board). • Look at a broader range of sensors (particulate matter, solar radiation, radon, sound, etc.) • Explore the possibilities of using LoRaWAN to manage water and electricity meters at scale. • Roll out devices to citizens and start to gather data. • Continue to be curious.
57. Thank you. Slides at https://l.ruk.ca/aapt