Why? SenseML 2014 Keynote. Immanuel Schweizer

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1 Why? SenseML 2014 Keynote Immanuel Schweizer

2 Background Immanuel Schweizer TU Darmstadt, Germany Telecooperation Lab Ubiquitous Computing Smart Urban Networks SenseML

3 Background Graph-based optimization for P2P networks PhD Thesis Energy-efficient network protocols for wireless sensor networks Flow Control Topology Control Application: Urban Management SenseML

4 Background SenseML

5 Inductive Loops >150 traffic lights ~3,000 sensors Two parameters Utilization Count SenseML

6 Street Cars ~10 sensors Deployed on streetcars Solar cells, Zigbee (868 MHz), temperature, GPS, SenseML

7 Phones / Noisemap Noise pollution via microphone More than 2000 installations 30 active users per day ~ 750,000 data points Gamification Calibration SenseML

8 da_sense SenseML

9 More sensors more data! SenseML

10 And more data OpenSense (ETH Zurich, DeviceAnalyzer (University of Cambridge, SenseML

11 What do we do with all that data? SenseML

12 What do we do with all that data? Help with planning tasks Understand human activity Environmental models Detect events Track users Nowcasting / Forecasting SenseML

13 Machine Learning SenseML

14 What s special about sensor data? SenseML

15 Where does sensor data come from? SenseML

16 Sensor Infrastructure SenseML

17 Sensor Infrastructure High cost per sensor Mostly wired High quality of information Some kind of certification SenseML

18 Sensor Infrastructure (Wireless) Sensor Networks SenseML

19 Wireless Sensor Networks Cheaper hardware Mostly wireless Battery-powered Mixed quality of information High diversity SenseML

20 Sensor Infrastructure (Wireless) Sensor Networks Mobile Sensing / User-generated Data SenseML

21 Mobile Sensing Easy development and deployment Almost no hardware cost Lack of control over quality of information Privacy Humans-in-the-loop SenseML

22 Sensor Infrastructure Quality (Wireless) Sensor Networks Quantity Mobile Sensing / User-generated Data SenseML

23 What s special about sensor data? Heterogeneity Unstructured vs. Structured data Different hardware Different Sensors Mobile Phones vs. Dedicated Hardware Heterogeneity of data sources Spatial and time resolution Quality-of-Information Low cost sensors Mobility Human-in-the-loop Faults Placement SenseML

24 Preprocessing Data Fusion Integrating External Sources Filtering Approximation Fault Detection Manual Cleaning SenseML

25 Example 1: Location SenseML

26 Example 2: Filtering Noisemap SenseML

27 Example 2: Filtering Noisemap SenseML

28 Example 3: Road Network Traffic measurements Noise measurements Idea: Predict traffic, based on noise measurements SenseML

29 Example 3: Road Network SenseML

30 Road network data processing Road Characteristics Road Type Surface Type Maximum Speed Oneway Number of lanes Etc. Road Segment Road Segment Geometry A polygon area in WGS 84 coordinate system Selection area geometry An area around the road segment, excluding the space near neighbor segements and the areas of surrounding buildings. Average sound pressure level for a time interval Weather conditions Traffic level SenseML

31 Road network data processing OpenStreetMap Goal - create road segments automatically Largest free road network dataset OSM Data format Node, way, relation Attributes SenseML

32 Road network data processing OSM - Non-planar topology Straight-forward planarization not possible Road segment separated in multiple polylines SenseML

33 Road network data processing Misclassified road links Remove "unclassified" roads Filter by length Represent multiple ways as single way Merge ways Missing common node Merge nodes in proximity of 5 cm SenseML

34 Road network data processing Clean up Combine parallel ways of the same street SenseML

35 Road network data processing 2D geometry Based on number of lanes SenseML

36 Road network data processing Spatial filter Which sound pressure records to include? Straight-forward approach: select measurements based on proximity 2 spatial buffers around each segment SelectionArea = A\(B 1 B 2 B n ) SenseML

Road network data processing Exclude buildings Location accuracy - falsely included/excluded measurements Inward/outward offsetting Inward: minimize the

37 Road network data processing Exclude buildings Location accuracy - falsely included/excluded measurements Inward/outward offsetting Inward: minimize the number of included measurements, that are recorded outside Outward: minimize the number of filtered out measurements, that are recorded inside SenseML

38 Example 3: Road Network SenseML

39 What s special about sensor data? SenseML

40 What s special about sensor data? SenseML

41 What s special about sensor data? =? SenseML

42 Real-world data Classes for classification Sound Level Traffic Level SenseML

43 Example: Traffic Level SenseML

44 Example: Traffic Level SenseML

45 Real-world data Classes for classification Sound Level Traffic Level Evaluation Transferability SenseML

46 Example: Noise Pollution Initial Dataset External Data Sources Classification Visualization Noisemap OpenStreetMap Additional Data 1 Instances of noise data Data File Extracting OSM information about nearby streets Adding additional information 1 Attributes ARFF Writer Geocoordinates Extracting information about nearby buildings Extracting weather information in the surrounding area Decision Tree Learning 2 Point of Interest Object Data (RDF) SPARQL LinkedGeoData Data File WeatherData 2 Final Model Sound Level Prediction SenseML

47 Evaluation Cross Validation Accuracy, Precision, Recall ~ 80% Other Models Same Resolution Same Input Data Difference? Human-readable rules SenseML

48 Transferability Perfect Model for Darmstadt No noise data in Nancy, France Same Features? External data sources Different regulations SenseML

49 What s special about sensor data? SenseML

50 Pipeline Initial Dataset External Data Sources Classification Visualization Noisemap OpenStreetMap Additional Data 1 Instances of noise data Data File Extracting OSM information about nearby streets Adding additional information 1 Attributes ARFF Writer Geocoordinates Extracting information about nearby buildings Extracting weather information in the surrounding area Decision Tree Learning 2 Point of Interest Object Data (RDF) SPARQL LinkedGeoData Data File WeatherData 2 Final Model Sound Level Prediction SenseML

Pipelines Initial Dataset Noisemap External Data Sources OpenStreetMap Additional Data Classification Visualization 1 Instances of noise data Data File Extracting OSM

Object Data (RDF) SPARQL LinkedGeoData Extracting weather information in the surrounding area Data File WeatherData 2 Decision Tree Learning Final Model Sound Level

51 Pipelines Initial Dataset Noisemap External Data Sources OpenStreetMap Additional Data Classification Visualization 1 Instances of noise data Data File Extracting OSM information about nearby streets Adding additional information 1 Attributes ARFF Writer Geocoordinates 2 Point of Interest Extracting information about nearby buildings Object Data (RDF) SPARQL LinkedGeoData Extracting weather information in the surrounding area Data File WeatherData 2 Decision Tree Learning Final Model Sound Level Prediction Layer 1 OSM XML Measurements Traffic Data Layer 2 OSM Parser Measurement Filter Traffic Parser Layer 3 Training Set Builder Machine Learning Model SenseML

52 Pipelines Initial Dataset Noisemap External Data Sources OpenStreetMap Additional Data Classification Visualization 1 Instances of noise data Data File Extracting OSM information about nearby streets Adding additional information 1 Attributes ARFF Writer Standardized Toolbox Rapidminer++ Geocoordinates Extracting information about nearby buildings Object Data (RDF) SPARQL LinkedGeoData Extracting weather information in the surrounding area WeatherData Generalize Components (with interfaces) 2 Point of Interest Data File 2 Decision Tree Learning Final Model Sound Level Prediction Learn and share What parts can be generalized? Why? Share your experience about building these pipelines SenseML

53 What s special about sensor data? Preprocessing Heterogeneity QoI Real-World Classes Evaluation Transferability Pipeline Share, learn, and standardize? More automation SenseML

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