1. A Community Platform for
Auto-Annotated Recreational Maps
Till Riedel1, Phillip Scholl1, Christian Decker1 and Michael Beigl2
1TecO,Universität Karlsruhe(TH), Vincenz Prießnitz Str. 3
76131 Karlsruhe, Germany
<{riedel,scholl,cdecker}@teco.edu>
2IBR,UniversitätBraunschweig, Mühlenpfordstr. 23
Braunschweig , Germany
<beigl@teco.edu>
2. Motivation
Recreational maps
Outdating
Specialized
City map Karlsruhe: Skate map Karlsruhe:
still using Version from 1990 Specialized Annotation
(or Google maps :-) ) Should have newest version !
2 A Community Platform for Auto-Annotated Recreational Maps (SWDMNSS 2007)
3. Recreational Maps
Specialized for sport, degree of expertise
more detailed annotation = better map
road size doesn't matter
Road quality reflected in coloring/style
Not many print runs depending on sport
Out-dating rapidly:
Nature
Minor road work
3 A Community Platform for Auto-Annotated Recreational Maps (SWDMNSS 2007)
4. Alternative:
Track sharing communities
Large Community
Sharing single tracks
Simple or text based
annotation
Subjective/Biased
4 A Community Platform for Auto-Annotated Recreational Maps (SWDMNSS 2007)
5. Problem
Track fusion dependent on sports device
Differentiated annotation makes system complex
Users tend not to waste their time on intricate stuff
We need many users to build complete maps
Dilemma:
many users vs complex system
complete maps vs differentiated annotation
5 A Community Platform for Auto-Annotated Recreational Maps (SWDMNSS 2007)
6. Community Platform for
Auto-Annotated Recreational Maps
Minimal effort
Automate process as much as possible
Minimal cost
Use available technology
Minimal risk
Anonymization: don't share raw personal data
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7. (kind of) Wireless Sensor Networks
Sensor Nodes = Sportsmen w/:
Sensor Watches/Sensors on Equipment
Handheld GPS Systems
Mobile sparsely synchronized sensors
high coverage
high availabiliy
only back-end communication
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8. Track Classification
Classifier for Road Quality
Use input from sensors:
Acceleration
Vibration
GPS (speed, slope)
Simple rule based system:
e.g.: low(vibration(R)) => good(R)
Preset rule sets for different devices/preferences
Adjustable by user
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9. Map Creation
Create network of roads
Which tracks belong to same road?
Routes not equal in strict sense
Algorithm described in: Scott Morris, Alan Morris, Kobus
Barnard, Digital Trail Libraries, jcdl, pp. 63-71, Digital Libraries,
2004 ACM/IEEE Joint Conference on (JCDL'04), 2004.
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10. Aggregation and Personalization
Collect all predicates on all sub-tracks
Use road intersections to uniquely identify parts
Use common sensor predicates
Query locally restricted
Aggregate all data (bias towards recent data)
Reclassify using users preference
Color map by new classification
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11. Hardware
Cheap sensors:
Measure vibration
Low power
Affordable for early adopters
Loose coupling with location system:
Standard handheld GPS
Use existing technology
Only use timestamps to synchronize
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12. Future Work
From Concept to Implementation :)
Studies for different outdoor sports
Cycling
Inline skating
Hiking
Use this kind of WSNs in other apps
highly mobile and sparsely synchronized
low power and high coverage
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13. Conclusion
Concept for enhancing maps with sensor
information
rather than just displaying values
Use Communities and enable personalization
Design aspects for mass adaptation
also critical to other sensor web apps
Widen the understanding of wireless sensor
networks
sensors don't have to be connected all the time
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