Intelligent Information Processing Chances of Crowdsourcing - PowerPoint PPT Presentation

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 Intelligent Information Processing – Chances of Crowdsourcing

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

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

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

Introduction Research interests Crowdsourcing Conclusion RF-based device-free activity recognition 10cm 10cm Hold over Open/close Take up Towards Away Swipe Swipe Swipe Swipe Wipe No bottom top left right gesture Intelligent Information Processing – Chances of Crowdsourcing

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

Introduction Research interests Crowdsourcing Conclusion IoT 2012 IoT 2012 Research interests Calculation of Functions on the RF-channel 1 transmit burst sequences . . . . . . . . . ◮ Mathematical calculation on the . . . . . . . . . . . . . . . wireless channel . . . burst . . . . . . . . . . . . . . . . . . ◮ Theoretical framework, time superimposed received burst sequence . . . . . . . . . . . . simulation, case studies t K Online Offline 1Sigg, Jakimovski, Beigl, Calculation of Function on the RF-channel for IoT, IoT 2012 Intelligent Information Processing – Chances of Crowdsourcing

Introduction Research interests Crowdsourcing Conclusion IoT 2012 IoT 2012 Research interests Calculation of Functions on the RF-channel 1 transmit burst sequences . . . . . . . . . ◮ Mathematical calculation on the . . . . . . . . . . . . . . . wireless channel . . . burst . . . . . . . . . . . . . . . . . . ◮ Theoretical framework, time superimposed received burst sequence . . . . . . . . . . . . simulation, case studies t K Online Offline 1Sigg, Jakimovski, Beigl, Calculation of Function on the RF-channel for IoT, IoT 2012 Intelligent Information Processing – Chances of Crowdsourcing

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

Introduction Research interests Crowdsourcing Conclusion Research interests Audio-based ad-hoc secure pairing 2 ◮ Use audio to generate secret key ◮ high Entropy, fuzzy cryptography, case studies, attack scenarios Hamming distance in created fingerprints Percentage of tests in one test run that passed at >5% for Kuiper KS p−values (loud audio source in 1.5m and 3m) Median percentage of identical bits in fingerprints Fingerprints created for matching audio samples 1.01947 (confidence value at α = 0.03) Fingerprints created for non−matching audio samples 1.01 0.8 Percentage of passed tests 0.99 0.75 0.7 0.97 0.65 0.95 0.6 0.93 0.92053 (confidence value at α = 0.03) 0.55 0.91 Only music Only clap Only speak Only snap Only whistle 0.5 Clap Music Snap Speak Whistle 0 2 4 6 8 10 12 14 16 18 20 Audio sequence class Test run 2S. Sigg et al., Secure Communication based on Ambient Audio, Accepted for IEEE Transactions on Mobile Computing Intelligent Information Processing – Chances of Crowdsourcing

Introduction Research interests Crowdsourcing Conclusion Chances/Challenges for Crowdsourcing Intelligent Information Processing – Chances of Crowdsourcing

Introduction Research interests Crowdsourcing Conclusion Chances/Challenges for Crowdsourcing Intelligent Information Processing – Chances of Crowdsourcing

Introduction Research interests Crowdsourcing Conclusion Chances/Challenges for Crowdsourcing Intelligent Information Processing – Chances of Crowdsourcing

Introduction Research interests Crowdsourcing Conclusion Chances/Challenges for Crowdsourcing Intelligent Information Processing – Chances of Crowdsourcing

Introduction Research interests Crowdsourcing Conclusion Chances/Challenges for Crowdsourcing Intelligent Information Processing – Chances of Crowdsourcing

Introduction Research interests Crowdsourcing Conclusion Chances/Challenges for Crowdsourcing Intelligent Information Processing – Chances of Crowdsourcing

Introduction Research interests Crowdsourcing Conclusion Do you have any questions? Stephan Sigg stephan.sigg@informatik.uni-goettingen.de Intelligent Information Processing – Chances of Crowdsourcing