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Crowdsourcing data for consumer-ready environmental monitoring

Crowdsourcing data for consumer-ready environmental monitoring

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HumanLogic Principals Karen Donoghue and Craig Newell delivered a 45-minute remote presentation with Q&A to the Tufts Department of Computer Science.

The talk was titled "Crowdsourcing data for consumer-ready environmental monitoring" and covered challenges around using crowdsourced data to design and deploy environmental monitoring at scale.

The audience included invited members of the departments of Mechanical Engineering and Civil and Environmental Engineering.

For more information on HumanLogic please email info@humanlogic.com.

HumanLogic Principals Karen Donoghue and Craig Newell delivered a 45-minute remote presentation with Q&A to the Tufts Department of Computer Science.

The talk was titled "Crowdsourcing data for consumer-ready environmental monitoring" and covered challenges around using crowdsourced data to design and deploy environmental monitoring at scale.

The audience included invited members of the departments of Mechanical Engineering and Civil and Environmental Engineering.

For more information on HumanLogic please email info@humanlogic.com.

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Crowdsourcing data for consumer-ready environmental monitoring

  1. 1. HUMANLOGIC.COM Crowdsourcing data for consumer-ready environmental monitoring Prepared for Tufts University Department of Computer Science Host: Professor Ming Chow Karen Donoghue and Craig Newell HumanLogic
  2. 2. HUMANLOGIC.COM Thank you for joining! We’re recording and we will take Q&A at the end via chat
  3. 3. HUMANLOGIC.COM Interaction design for IoT devices at large scale
  4. 4. HUMANLOGIC.COM What is Particulate Matter (PM)? (also called also called particle pollution)
  5. 5. HUMANLOGIC.COM Source: © Encyclopédie de l’Environnement PM10 <= 10 𝞵m PM2.5 <= 2.5 𝞵m PM1 <= 1.0 𝞵m Coarse particles Upper respiratory tract Fine particles Lower respiratory tract Very fine particles Alveolus (tiny air sacs)
  6. 6. HUMANLOGIC.COM The rise of low cost PM sensors
  7. 7. HUMANLOGIC.COM
  8. 8. HUMANLOGIC.COM Basic Data Gathering IoT System
  9. 9. HUMANLOGIC.COM ● Laser PM Sensor ● Microcontroller ● Case
  10. 10. HUMANLOGIC.COM Connectivity
  11. 11. HUMANLOGIC.COM ● WiFi as almost everyone has it without extra costs ● HTTPS for simplicity over MQTT, etc.
  12. 12. HUMANLOGIC.COM Collection network
  13. 13. HUMANLOGIC.COM
  14. 14. HUMANLOGIC.COM Data access
  15. 15. HUMANLOGIC.COM ● HTTPS ● Efficiency ● Cacheability ● Validity
  16. 16. HUMANLOGIC.COM
  17. 17. HUMANLOGIC.COM Local Haze crowdsources air quality sensor readings worldwide and rates sensor accuracy
  18. 18. HUMANLOGIC.COM
  19. 19. HUMANLOGIC.COM See Local Haze demo video on YouTube: https://youtu.be/Ex85q6zH8rc
  20. 20. HUMANLOGIC.COM Lessons learned for building IoT systems
  21. 21. HUMANLOGIC.COM What user experiences work best for dealing with lots of things?
  22. 22. HUMANLOGIC.COM “At scale”: many, thousands, hundreds of thousands, millions, tens of millions...
  23. 23. HUMANLOGIC.COM Ease of use as a core attribute
  24. 24. HUMANLOGIC.COM
  25. 25. HUMANLOGIC.COM To help manage the size of the infinite scrolling list we added filtering
  26. 26. HUMANLOGIC.COM
  27. 27. HUMANLOGIC.COM After filtering, we added relevancy
  28. 28. HUMANLOGIC.COM
  29. 29. HUMANLOGIC.COM We added a map view to help users better understand the contents of the list
  30. 30. HUMANLOGIC.COM
  31. 31. HUMANLOGIC.COM Understand what attributes of the data impact perception of “trustworthiness”
  32. 32. HUMANLOGIC.COM Precision Confidence Recency Transparency
  33. 33. HUMANLOGIC.COM Use the best interaction model(s) based on users’ needs - may need multiple
  34. 34. HUMANLOGIC.COM
  35. 35. HUMANLOGIC.COM Thank you! Send your questions via chat
  36. 36. HUMANLOGIC.COM info@humanlogic.com

Notas del editor

  • Karen: Introduction
  • CHECK COPYRIGHT OF IMAGE
    PM
    PM10 : inhalable particles, with diameters that are generally 10 micrometers and smaller; and
    PM2.5 : fine inhalable particles, with diameters that are generally 2.5 micrometers and smaller (The average human hair is about 70 micrometers in diameter – making it 30 times larger than the largest fine particle.

  • HTTPS
    Polling vs Push
    Efficiency
    Just the needed data
    Cacheability
    No need to download it if nothing has changed
    Validity
    How long is the data valid for?
    When should the next poll happen?
  • Targeting consumers that are also technical citizen scientists
  • Over 18K sensors
  • Note that with the correct technical implementation using techniques such as lazy loading, speculative loading, etc….
  • May not know how often the data is being refreshed
  • What is the future of interaction design for large numbers of entities? - search and rule based interactions
    To what extent is query language design important in designing for these kinds of interaction models?
    What skills are needed for designers of these “at scale” systems? What roles do these practitioners play in organizations?

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