NoizCrowd:
A Crowd-Based Data Gathering and
Management System for Noise Level Data
Mariusz Wisniewski, Gianluca
Demartini,...
Motivation - Big Data
• Large dataset are necessary to enable analytics
and support decision making
– Meteorological stati...
NoizCrowd
• A crowd-sensing approach to big data generation
using commodity sensors
• Crowd-source noise level in a geo re...
Outline
• Related approaches
• NoizCrowd Architecture Overview
– Data Gathering
– Storage
– Modeling
– Export and Visualiz...
Related Work
• Participatory Sensing vs Sensor Networks
– Low cost / High cost
– Mobile phones / Sensors
– Distributed / C...
Related Work
• Noise Mapping Apps
– NoiseTube: opensource, widespread usage
– NoiseMap: control over data
– SoundSense: ma...
NoizCrowd Architecture
Gianluca Demartini 7
Data Gathering
• By means of Crowd-sourcing
– GPS: location
– Microphone: noise level
– Internet connection: send data to ...
Data Storage
• App sends median and peak dB values over
few seconds
• Spatio-temporal data: non-relational storage
system ...
Noise Modeling
• Data from crowd is noisy and skewed/sparse
• Raw data is not shown to the end users
• Models to deal with...
Data Export and Visualization
• From SciDB data is
– converted to RDF
– stored in dipLODocus[RDF]
– Available via SPARQL
•...
Gianluca Demartini 12
Data Models
• Spatial Interpolation
– In the same time interval, data from different
locations
– Need to be computational ...
Data Models
• Temporal interpolation
– Short ranges (minutes) like spatial interp. in 3D
– Long ranges, look for patterns ...
Noise Propagation Models
• We adopt an existing model that takes into
account:
– Sound power
– Distance from source
– Dire...
Materialization of Models
• Data from models
– Is computationally expensive to generate
– May be a lot since we can cover ...
Performance Evaluation (1)
• 30 outdoor deployments
– 2,3,4 smartphones
– Multiple noise sources
– Urban setting, flat are...
Performance Evaluation (2)
• Sound propagation and source location
• 3 smartphones, 100dB source
Gianluca Demartini 18
Performance Evaluation (3)
• Sound level of source error
– 16% with 3 measurements
– 10% with 4 measurements
– 9% with 5 m...
NoizCrowd - Conclusions
• Large scale data is key for decision making
• Crowd-source noise level data using mobiles
– Scal...
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NoizCrowd: A Crowd-Based Data Gathering and Management System for Noise Level Data

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NoizCrowd: A Crowd-Based Data Gathering and Management System for Noise Level Data

  1. 1. NoizCrowd: A Crowd-Based Data Gathering and Management System for Noise Level Data Mariusz Wisniewski, Gianluca Demartini, Apostolos Malatras, and Philippe Cudré-Mauroux University of Fribourg, Switzerland
  2. 2. Motivation - Big Data • Large dataset are necessary to enable analytics and support decision making – Meteorological station / car traffic • Set up a large-scale sensing infrastructure is costly and time-consuming • Create a large amount of valuable data – Crowdsourcing – Data generation models – Smartphones as sensors – Big Data analytics Gianluca Demartini 2
  3. 3. NoizCrowd • A crowd-sensing approach to big data generation using commodity sensors • Crowd-source noise level in a geo region • Noise propagation models to generate data • Array data management techniques to scale • Results accessible via a visual interface • Support decisions (e.g., where to live) Gianluca Demartini 3
  4. 4. Outline • Related approaches • NoizCrowd Architecture Overview – Data Gathering – Storage – Modeling – Export and Visualization • Data Models • Performance Evaluation Gianluca Demartini 4
  5. 5. Related Work • Participatory Sensing vs Sensor Networks – Low cost / High cost – Mobile phones / Sensors – Distributed / Centralized management – Privacy, data quality • Applications: Environment, vehicle routing Gianluca Demartini 5
  6. 6. Related Work • Noise Mapping Apps – NoiseTube: opensource, widespread usage – NoiseMap: control over data – SoundSense: machine learning to classify sounds • NoizCrowd – Data in RDF linkable to other datasets (linkeddata.org) – Scalable storage: generate data by interpolation Gianluca Demartini 6
  7. 7. NoizCrowd Architecture Gianluca Demartini 7
  8. 8. Data Gathering • By means of Crowd-sourcing – GPS: location – Microphone: noise level – Internet connection: send data to server • Microphone Calibration – Sound level meter – Sharing conversion table for smartphone models Gianluca Demartini 8
  9. 9. Data Storage • App sends median and peak dB values over few seconds • Spatio-temporal data: non-relational storage system (SciDB) – Durable storage – Retrieve data to build models – Export data for visualization • Multi-dimensional array (space and time) • Distributed storage Gianluca Demartini 9
  10. 10. Noise Modeling • Data from crowd is noisy and skewed/sparse • Raw data is not shown to the end users • Models to deal with – Overlapping data – Missing data Gianluca Demartini 10
  11. 11. Data Export and Visualization • From SciDB data is – converted to RDF – stored in dipLODocus[RDF] – Available via SPARQL • Visualization – Overlay noise level on a map – Additional chart for time evolution Gianluca Demartini 11
  12. 12. Gianluca Demartini 12
  13. 13. Data Models • Spatial Interpolation – In the same time interval, data from different locations – Need to be computational simple (large volume) – Bi-dimensional range queries in space (SciDB) – K-nearest neighbor interpolation – Computed in parallel Gianluca Demartini
  14. 14. Data Models • Temporal interpolation – Short ranges (minutes) like spatial interp. in 3D – Long ranges, look for patterns and infer • E.g., every Monday at 11am we have 50dB and we miss a Monday measurement • E.g., same measurement (50dB) in same area 2h ago and now Gianluca Demartini 14
  15. 15. Noise Propagation Models • We adopt an existing model that takes into account: – Sound power – Distance from source – Directivity – Atmospheric absorption – Excess attenuation (we use meteo conditions) • Difficult to measure with smartphone • Constant in a given region (and use GPS info) Gianluca Demartini 15
  16. 16. Materialization of Models • Data from models – Is computationally expensive to generate – May be a lot since we can cover any region • We do late materialization – At query time – Only for the specific request – Cached and indexed for future requests – Incremental updates of views, if possible Gianluca Demartini 16
  17. 17. Performance Evaluation (1) • 30 outdoor deployments – 2,3,4 smartphones – Multiple noise sources – Urban setting, flat area of 50x50 meters • Professional-grade noise level meter as gold standard measurement • 85% of interpolated data +-6dB error • 63% of interpolated data +-4dB error Gianluca Demartini 17
  18. 18. Performance Evaluation (2) • Sound propagation and source location • 3 smartphones, 100dB source Gianluca Demartini 18
  19. 19. Performance Evaluation (3) • Sound level of source error – 16% with 3 measurements – 10% with 4 measurements – 9% with 5 measurements • Source location – 3m error on average Gianluca Demartini 19
  20. 20. NoizCrowd - Conclusions • Large scale data is key for decision making • Crowd-source noise level data using mobiles – Scale-out using an array backend – Generate missing data and visualize • Next steps – Android app – Data recording as background feature – Additional materialization strategies http://exascale.info Gianluca Demartini 20

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