Darwin Phones: the Evolution of Sensing and Inference on Mobile Phones - PowerPoint PPT Presentation

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Darwin Phones: the Evolution of Sensing and Inference on Mobile Phones

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  1. Darwin Phones: the Evolution of Sensing and Inference on Mobile Phones Emiliano Miluzzo*, Cory T. Cornelius*, AshwinRamaswamy*, TanzeemChoudhury*, Zhigang Liu**, Andrew T. Campbell* * CS Department – Dartmouth College ** Nokia Research Center – Palo Alto

  2. Emiliano Miluzzo miluzzo@cs.dartmouth.edu

  3. evolution of sensing and inference on mobile phones Emiliano Miluzzo miluzzo@cs.dartmouth.edu

  4. PR time Emiliano Miluzzo miluzzo@cs.dartmouth.edu

  5. Emiliano Miluzzo miluzzo@cs.dartmouth.edu

  6. Emiliano Miluzzo miluzzo@cs.dartmouth.edu

  7. Emiliano Miluzzo miluzzo@cs.dartmouth.edu

  8. Emiliano Miluzzo miluzzo@cs.dartmouth.edu

  9. Emiliano Miluzzo miluzzo@cs.dartmouth.edu

  10. ok… so what ?? Emiliano Miluzzo miluzzo@cs.dartmouth.edu

  11. density Emiliano Miluzzo miluzzo@cs.dartmouth.edu

  12. sensing accelerometer …. digital compass microphone light sensor/camera GPS WiFi/bluetooth Emiliano Miluzzo miluzzo@cs.dartmouth.edu

  13. sensing …. accelerometer air quality / pollution sensor digital compass gyroscope microphone light sensor/camera GPS WiFi/bluetooth Emiliano Miluzzo miluzzo@cs.dartmouth.edu

  14. programmability • free SDK • - multitasking Emiliano Miluzzo miluzzo@cs.dartmouth.edu

  15. hardware • - 600 MHz CPU • - up to 1GB • application memory computation capability is increasing Emiliano Miluzzo miluzzo@cs.dartmouth.edu

  16. application distribution Emiliano Miluzzo miluzzo@cs.dartmouth.edu

  17. application distribution deploy apps onto millions of phones at the blink of an eye Emiliano Miluzzo miluzzo@cs.dartmouth.edu

  18. application distribution deploy apps onto millions of phones at the blink of an eye collect huge amount of data for research purposes Emiliano Miluzzo miluzzo@cs.dartmouth.edu

  19. cloud infrastructure cloud - backend support Emiliano Miluzzo miluzzo@cs.dartmouth.edu

  20. cloud infrastructure cloud - backend support Emiliano Miluzzo miluzzo@cs.dartmouth.edu

  21. cloud infrastructure cloud - backend support we want to push intelligence to the phone Emiliano Miluzzo miluzzo@cs.dartmouth.edu

  22. cloud infrastructure cloud - backend support preserve the phone user experience (battery lifetime, ability to make calls, etc.) Emiliano Miluzzo miluzzo@cs.dartmouth.edu

  23. cloud infrastructure cloud - backend support • sensing • run machine learning algorithms locally (feature extraction + inference) Emiliano Miluzzo miluzzo@cs.dartmouth.edu

  24. cloud infrastructure cloud - backend support run machine learning algorithms (learning) • sensing • run machine learning algorithms locally (feature extraction + inference) Emiliano Miluzzo miluzzo@cs.dartmouth.edu

  25. cloud infrastructure cloud - backend support store and crunch big data (fusion) run machine learning algorithms (learning) • sensing • run machine learning algorithms locally (feature extraction + inference) Emiliano Miluzzo miluzzo@cs.dartmouth.edu

  26. cloud infrastructure cloud - backend support store and crunch big data (fusion) run machine learning algorithms (learning) 3 to 5 years from now our phones will be as powerful as a • sensing • run machine learning algorithms locally (feature extraction + inference) Emiliano Miluzzo miluzzo@cs.dartmouth.edu

  27. cloud infrastructure cloud - backend support store and crunch big data (fusion) run machine learning algorithms (learning) 3 to 5 years from now our phones will be as powerful as a • sensing • run machine learning algorithms locally (feature extraction + inference) Emiliano Miluzzo miluzzo@cs.dartmouth.edu

  28. cloud infrastructure cloud - backend support store and crunch big data (fusion) run machine learning algorithms (learning) 3 to 5 years from now our phones will be as powerful as a • sensing • run machine learning algorithms locally (feature extraction + inference) Emiliano Miluzzo miluzzo@cs.dartmouth.edu

  29. cloud infrastructure cloud - backend support store and crunch big data (fusion) run machine learning algorithms (learning) 3 to 5 years from now our phones will be as powerful as a • Sensing • run machine learning algorithms locally • (feature extraction + learning + inference) Emiliano Miluzzo miluzzo@cs.dartmouth.edu

  30. programmability sensing cloud infrastructure Emiliano Miluzzo miluzzo@cs.dartmouth.edu

  31. programmability sensing ?? cloud infrastructure Emiliano Miluzzo miluzzo@cs.dartmouth.edu

  32. societal scale sensing reality mining using mobile phones will play a big role in the future global mobilesensornetwork Emiliano Miluzzo miluzzo@cs.dartmouth.edu

  33. end of PR – now darwin Emiliano Miluzzo miluzzo@cs.dartmouth.edu

  34. a small building block towards the big vision Emiliano Miluzzo miluzzo@cs.dartmouth.edu

  35. from motes to mobile phones Emiliano Miluzzo miluzzo@cs.dartmouth.edu

  36. evolution of sensing and inference on mobile phones from motes to mobile phones Emiliano Miluzzo miluzzo@cs.dartmouth.edu

  37. evolution of sensing and inference on mobile phones from motes to mobile phones • classification model • evolution darwin • classification model • pooling • collaborative • inference Emiliano Miluzzo miluzzo@cs.dartmouth.edu

  38. darwin sensing apps social context microphone camera audio / pollution / RF fingerprinting GPS/WiFi/ cellular air quality pollution image / video manipulation • classification model • evolution darwin applies distributed computing and collaborative inference concepts to mobile sensing systems • classification model • pooling • collaborative • inference Emiliano Miluzzo miluzzo@cs.dartmouth.edu

  39. why darwin? mobile phone sensing today Emiliano Miluzzo miluzzo@cs.dartmouth.edu

  40. why darwin? mobile phone sensing today train classification model X in the lab Emiliano Miluzzo miluzzo@cs.dartmouth.edu

  41. why darwin? mobile phone sensing today train classification model X in the lab deploy classifier X Emiliano Miluzzo miluzzo@cs.dartmouth.edu

  42. why darwin? mobile phone sensing today train classification model X in the lab deploy classifier X train classification model X’ in the lab Emiliano Miluzzo miluzzo@cs.dartmouth.edu

  43. why darwin? mobile phone sensing today train classification model X in the lab deploy classifier X train classification model X’ in the lab deploy classifier X’ Emiliano Miluzzo miluzzo@cs.dartmouth.edu

  44. why darwin? mobile phone sensing today a fully supervised approach doesn’t scale! train classification model X in the lab deploy classifier X train classification model X’ in the lab deploy classifier X’ Emiliano Miluzzo miluzzo@cs.dartmouth.edu

  45. why darwin? a same classifier does not scale to multiple environments (e.g., quiet and noisy env) Emiliano Miluzzo miluzzo@cs.dartmouth.edu

  46. why darwin? a same classifier does not scale to multiple environments (e.g., quiet and noisy env) Emiliano Miluzzo miluzzo@cs.dartmouth.edu

  47. why darwin? a same classifier does not scale to multiple environments (e.g., quiet and noisy env) Emiliano Miluzzo miluzzo@cs.dartmouth.edu

  48. why darwin? a same classifier does not scale to multiple environments (e.g., quiet and noisy env) darwin creates new classification models transparently from the user (classification model evolution) Emiliano Miluzzo miluzzo@cs.dartmouth.edu

  49. why darwin? ability for an application to rapidly scale to many devices Emiliano Miluzzo miluzzo@cs.dartmouth.edu

  50. why darwin? ability for an application to rapidly scale to many devices darwin re-uses classification models when possible (classification model pooling) Emiliano Miluzzo miluzzo@cs.dartmouth.edu