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Mar 19, 2023 280 likes •468 views

Intelligent Information Processing Chances of Crowdsourcing Stephan Sigg Computer Networks Group NII Shonan Meeting Seminar 34, Shonan Village, 18.11.2013 Introduction Research interests Crowdsourcing Conclusion My background

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Intelligent Information Processing – Chances of Crowdsourcing

Stephan Sigg

Computer Networks Group

NII Shonan Meeting Seminar 34, Shonan Village, 18.11.2013

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Introduction Research interests Crowdsourcing Conclusion

My background

Intelligent Information Processing – Chances of Crowdsourcing

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Introduction Research interests Crowdsourcing Conclusion

My background

Intelligent Information Processing – Chances of Crowdsourcing

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Introduction Research interests Crowdsourcing Conclusion Intelligent Information Processing – Chances of Crowdsourcing

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Introduction Research interests Crowdsourcing Conclusion Intelligent Information Processing – Chances of Crowdsourcing

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Introduction Research interests Crowdsourcing Conclusion

RF-based device-free activity recognition

10cm 10cm

Towards Away Hold over Open/close Take up Swipe bottom Swipe top Swipe left Swipe right Wipe No gesture

Intelligent Information Processing – Chances of Crowdsourcing

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Introduction Research interests Crowdsourcing Conclusion Intelligent Information Processing – Chances of Crowdsourcing

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Introduction Research interests Crowdsourcing Conclusion

Research interests

Calculation of Functions on the RF-channel1

◮ Mathematical calculation on the

wireless channel

◮ Theoretical framework,

simulation, case studies

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

t K burst

superimposed received burst sequence transmit burst sequences time

Offline Online

IoT 2012 IoT 2012

1Sigg, Jakimovski, Beigl, Calculation of Function on the RF-channel for IoT, IoT 2012 Intelligent Information Processing – Chances of Crowdsourcing

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Introduction Research interests Crowdsourcing Conclusion

Research interests

Calculation of Functions on the RF-channel1

◮ Mathematical calculation on the

wireless channel

◮ Theoretical framework,

simulation, case studies

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

t K burst

superimposed received burst sequence transmit burst sequences time

Offline Online

IoT 2012 IoT 2012

1Sigg, Jakimovski, Beigl, Calculation of Function on the RF-channel for IoT, IoT 2012 Intelligent Information Processing – Chances of Crowdsourcing

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Introduction Research interests Crowdsourcing Conclusion Intelligent Information Processing – Chances of Crowdsourcing

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Introduction Research interests Crowdsourcing Conclusion

Research interests

Audio-based ad-hoc secure pairing2

◮ Use audio to generate secret key ◮ high Entropy, fuzzy cryptography,

case studies, attack scenarios

Clap Music Snap Speak Whistle 0.5 0.55 0.6 0.65 0.7 0.75 0.8

Hamming distance in created fingerprints (loud audio source in 1.5m and 3m)

Audio sequence class

Median percentage of identical bits in fingerprints Fingerprints created for matching audio samples Fingerprints created for non−matching audio samples 2 4 6 8 10 12 14 16 18 20 0.91 0.93 0.95 0.97 0.99 1.01 Test run Percentage of passed tests

Percentage of tests in one test run that passed at >5% for Kuiper KS p−values

1.01947 (confidence value at α = 0.03) 0.92053 (confidence value at α = 0.03) Only music Only whistle Only snap Only speak Only clap

2S. Sigg et al., Secure Communication based on Ambient Audio, Accepted for IEEE Transactions on Mobile

Computing Intelligent Information Processing – Chances of Crowdsourcing

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Introduction Research interests Crowdsourcing Conclusion

Chances/Challenges for Crowdsourcing

Intelligent Information Processing – Chances of Crowdsourcing

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Introduction Research interests Crowdsourcing Conclusion

Chances/Challenges for Crowdsourcing

Intelligent Information Processing – Chances of Crowdsourcing

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Introduction Research interests Crowdsourcing Conclusion

Chances/Challenges for Crowdsourcing

Intelligent Information Processing – Chances of Crowdsourcing

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Introduction Research interests Crowdsourcing Conclusion

Chances/Challenges for Crowdsourcing

Intelligent Information Processing – Chances of Crowdsourcing

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Introduction Research interests Crowdsourcing Conclusion

Chances/Challenges for Crowdsourcing

Intelligent Information Processing – Chances of Crowdsourcing

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Introduction Research interests Crowdsourcing Conclusion

Chances/Challenges for Crowdsourcing

Intelligent Information Processing – Chances of Crowdsourcing

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Introduction Research interests Crowdsourcing Conclusion

Do you have any questions?

Stephan Sigg stephan.sigg@informatik.uni-goettingen.de

Intelligent Information Processing – Chances of Crowdsourcing