Smartphone-based sensor networks and some statistical challenges: the Earthquake Network Android application

Transcription

1 Smartphone-based sensor networks and some statistical challenges: the Earthquake Network Android application Francesco Finazzi University of Bergamo 29 November University of Glasgow

2 Outline Outline Monitoring networks Wireless sensor networks Smartphone-based sensor networks (SSN) Analysis of SSN data Earthquake Network Android application

3 Classic monitoring networks Classic (wired) monitoring networks

4 Monitoring network node Monitoring network node Byres Road - Scottish air quality network

5 Monitoring network node Air quality monitoring network

6 Wireless sensor network Wireless sensor network Volcano monitoring - Harvard Sensor Networks Lab

7 Smartphone-based sensor networks Smartphone-based sensor network (SSN)

8 Smartphones as nodes Data transfer Power Can we use smartphones? GSM signal - not easy to have in remote areas External batteries, solar panels Sensing capabilities Acceleration, geomagnetic eld, temperature, illuminance, sound pressure, atm. pressure and humidity Visible light (camera) Position Sensor accuracy and calibration Not always good Geolocation GPS or GSM signal

20 Smartphones as nodes SSN applications Environmental eld estimation Temperature, pressure, humidity,... Earthquakes (accelerometer) Avalanches/landslides (microphone) Air quality (camera+ lter) Fire/smoke detection (camera)

26 SSN types SSN types Private SSNs - Sensor network with smartphones as nodes. The network is operated by a network manager which is responsible for buying, installing and maintaining the nodes. Public SSNs - Sensor network based on smartphones belonging to their single owners. The smartphone is part of the network only if the owner agrees to be part of it.

27 SSN types SSN types Private SSNs - Sensor network with smartphones as nodes. The network is operated by a network manager which is responsible for buying, installing and maintaining the nodes. Public SSNs - Sensor network based on smartphones belonging to their single owners. The smartphone is part of the network only if the owner agrees to be part of it.

28 Public SSNs Public SSNs architecture

29 Public SSNs Public SSNs Dynamic network: the total number of nodes, the number of active nodes and the location of the nodes change continuously; Location is not always known precisely (can we use non-localized smartphones?); Heterogeneous nodes: di erent vendors and models; Sensors are often uncalibrated; Asynchronous data acquisition; Preferential sampling (remote areas?); Someone else pays device and energy.

36 SSN data analysis SSN data analysis

37 Classic monitoring network - Data matrix Classic monitoring network - Data matrix

38 Classic monitoring network - Data matrix SSN - Geolocation

39 Introduction Monitoring networks WSN SSN SSN data analysis Classic monitoring network - Data matrix Dynamic network Earthquake Network Conclusions

40 Introduction Monitoring networks WSN SSN SSN data analysis Classic monitoring network - Data matrix Dynamic network Earthquake Network Conclusions

41 Classic monitoring network - Data matrix SSN - Data table

42 Calibration Sensor calibration Smartphone sensors are often uncalibrated or... they require periodic calibration Manual calibration is not easy/practicable

43 Calibration "On the y" calibration based on geofence

44 Calibration Calibration curve

45 Introduction Earthquake Network Android app

46 Earthquake Network as SSN Earthquake Network

47 Earthquake Network as SSN Earthquake Network

48 Why and what Why and what Why detect quakes? O cial networks take between 15 and 60 minutes to notify Population noti cation Intensity (shake map) What can be detected? Time of the event Intensity Epicenter (not easy) What cannot be detected? Magnitude (energy)

58 HW/SW architecture HW/SW architecture

59 HW/SW architecture HW/SW architecture

60 SW architecture App dashboard

61 Vibrations and quakes Vibration vs earthquake Vibration: smartphone movement Earthquake: waves of energy travelling through the Earth s layers Smartphones measure vibrations, the network measures earthquakes

64 Vibration detection Vibration detection

65 Accelerometer calibration Accelerometer calibration

66 Vibration detection Vibration detection - Battery charging

67 Introduction Monitoring networks WSN SSN SSN data analysis Earthquake Network Earthquake detection Earthquake detection (non geolocalized smartphones?) Conclusions

68 Dynamic network Active nodes

69 Dynamic network Active nodes

70 Earthquake Network

71 Dynamic network Active nodes Downloads: (11 months) Total active users: Active nodes at any time: Enabled nodes at any time: (3% 8%) 3% of 1.4 billion (potential) smartphones is a large number

76 Conclusions Conclusions Environmental sensors are becoming available for o -the-shelf products Smartphones are the natural candidates to carry around sensors However: A SSN should be based on a large number of smartphones (not easy) Calibrations of heterogeneous sensors can be an issue Statistical methods/software for SSN space-time data may not be immediately available

82 Pro version