Give me purpose! - Technische Universität Darmstadt · PDF fileBackground •Immanuel...

Why?SenseML 2014 Keynote

Immanuel Schweizer

Background

• Immanuel Schweizer

• TU Darmstadt, Germany

• Telecooperation Lab• Ubiquitous Computing

• Smart Urban Networks

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Background

• Graph-based optimization forP2P networks

• PhD Thesis • Energy-efficient network protocols

for wireless sensor networks

• Flow Control

• Topology Control

• Application: Urban Management

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Background

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Inductive Loops

• >150 traffic lights• ~3,000 sensors

• Two parameters• Utilization

• Count

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Street Cars

• ~10 sensors• Deployed on streetcars

• Solar cells, Zigbee (868 MHz), temperature, GPS, …

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Phones / Noisemap

• Noise pollution via microphone

• More than 2000 installations• 30 active users per day

• ~ 750,000 data points

• Gamification

• Calibration

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da_sense

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More sensors…

…more data!SenseML 2014 9

And more data…

OpenSense (ETH Zurich, http://www.opensense.ethz.ch/trac/)

DeviceAnalyzer (University of Cambridge, https://deviceanalyzer.cl.cam.ac.uk/)

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http://www.opensense.ethz.ch/trac/

https://deviceanalyzer.cl.cam.ac.uk/

What do we do with all that data?

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What do we do with all that data?

• Help with planning tasks

• Understand human activity

• Environmental models

• Detect events

• Track users

• Nowcasting / Forecasting

• …

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Machine Learning

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What‘s special about sensor data?

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Where does sensor data come from?

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Sensor Infrastructure

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• High cost per sensor

• Mostly wired

• High quality of information

• Some kind of certification

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(Wireless) Sensor Networks

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Wireless Sensor Networks

• Cheaper hardware

• Mostly wireless

• Battery-powered

• Mixed quality of information

• High diversity

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Mobile Sensing / User-generated Data

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Mobile Sensing

• Easy development and deployment

• Almost no hardware cost

• Lack of control over quality of information

• Privacy

• Humans-in-the-loop

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Mobile Sensing / User-generated Data

Qu

antity

Qu

ality

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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

• …

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Preprocessing

• Data Fusion

• Integrating External Sources

• Filtering

• Approximation

• Fault Detection

• Manual Cleaning

• …

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Example 1: Location

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Example 2: Filtering Noisemap

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Example 2: Filtering Noisemap

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Example 3: Road Network

• Traffic measurements

• Noise measurements

• Idea: Predict traffic, based on noise measurements

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Road network data processing

Road Segment

· Road Type· Surface Type· Maximum Speed· Oneway· Number of lanes· Etc.

Road Characteristics

A polygon area in WGS 84 coordinate system

An area around the road segment, excluding the

space near neighbor segements and the areas of surrounding buildings.

Road Segment Geometry Selection area geometry

Average sound pressure level for a time interval

Traffic levelWeather conditions

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Road network data processingOpenStreetMap

• Goal - create road segments automatically

• Largest free road network dataset

• OSM Data format• Node, way, relation• Attributes

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OSM - Non-planar topology

• Straight-forward planarization not possible• Road segment separated in multiple polylines

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• 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

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• Clean up

• Combine parallel ways of the same street

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• 2D geometry• Based on number of lanes

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𝑆𝑒𝑙𝑒𝑐𝑡𝑖𝑜𝑛𝐴𝑟𝑒𝑎 = 𝐴\(𝐵1 ∪ 𝐵2 ∪ … 𝐵𝑛)


Spatial filter

• Which sound pressure records to include?

• Straight-forward approach: select measurements based on proximity

• 2 spatial buffers around each segment

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• 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

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=?

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Real-world data

• Classes for classification• Sound Level

• Traffic Level

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Example: Traffic Level

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Example: Traffic Level

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Real-world data

• Classes for classification• Sound Level

• Traffic Level

• Evaluation

• Transferability

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Example: Noise Pollution

Visualization

Sound Level Prediction

ARFF Writer

Classification

Decision Tree Learning

Final Model

1

2

OpenStreetMap

Extracting OSM information about

nearby streets

LinkedGeoData

Extracting information about nearby buildings

Object Data (RDF)

WeatherData

Extracting weather information in the surrounding area

Data File

Data File

External Data Sources

Additional Data

Adding additional information

SPARQL

Attributes

Noisemap

Instances of noise data

Initial Dataset

Point of Interest

Geocoordinates

1

2

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Evaluation

• Cross Validation• Accuracy, Precision, Recall ~

80%

• Other Models• Same Resolution

• Same Input Data

• Difference?

• Human-readable rules

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Transferability

• Perfect Model for Darmstadt

• No noise data in Nancy, France

• Same Features?• External data sources

• Different regulations

• …

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Pipeline

Visualization


ARFF Writer

Classification


Final Model

1

2

OpenStreetMap


nearby streets

LinkedGeoData


Object Data (RDF)

WeatherData


Data File

Data File


Additional Data


SPARQL

Attributes

Noisemap


Initial Dataset

Point of Interest

Geocoordinates

1

2

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PipelinesVisualization


ARFF Writer

Classification


Final Model

1

2

OpenStreetMap


nearby streets

LinkedGeoData


Object Data (RDF)

WeatherData


Data File

Data File


Additional Data


SPARQL

Attributes

Noisemap


Initial Dataset

Point of Interest

Geocoordinates

1

2

Layer 1

Layer 2

Layer 3

Measurements Traffic Data

Measurement Filter Traffic ParserOSM Parser

OSM XML

Machine Learning ModelTraining Set Builder

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Pipelines

• StandardizedToolbox• Rapidminer++

• Generalize Components (with interfaces)

• Learn and share• What parts can be generalized? Why?

• Share your experience about building these pipelines

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Visualization


ARFF Writer

Classification


Final Model

1

2

OpenStreetMap


nearby streets

LinkedGeoData


Object Data (RDF)

WeatherData


Data File

Data File


Additional Data


SPARQL

Attributes

Noisemap


Initial Dataset

Point of Interest

Geocoordinates

1

2


• Preprocessing• Heterogeneity

• QoI

• Real-World• Classes

• Evaluation

• Transferability

• Pipeline• Share, learn, and standardize?

• More automation

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Give me purpose! - Technische Universität Darmstadt · PDF fileBackground •Immanuel...

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