Satelliten- und Mobilfunk

1. CityPulse: Reliable Information Processing in Smart City Frameworks Ralf Tönjes University of Applied Sciences Osnabrück, Germany Satelliten- und Mobilfunk 1 Prof. Dr.-Ing. Ralf Tönjes

2. Content • Introduction • Framework for Smart City Data Analysis • QoI Monitoring • Spatial Reasoning • Conclusion

3. Smart Services are Context-aware Personal Digital AssistantRecommender System Augmented Reality Context-aware Traffic Management Advertisements

5. Smart City Data • Data is multi-modal and heterogeneous • Requires (near-) real-time analysis • Noisy and incomplete • Time and location dependent • Dynamic and varies in quality • Crowd sourced data can be unreliable • Data alone may not give a clear picture • we need contextual information, • background knowledge, • multi-source information and • obviously better data analytics solutions…

6. Content • Introduction • Framework for Smart City Data Analysis • QoI Monitoring • Spatial Reasoning • Conclusion

7. An Integrated Approach Re-usable components

8. CityPulse Framework • Virtualisation • Heterogeneous data sources • Overcome silo architectures and provide common abstract interface • Assigning semantic annotations to data streams • Federation (Sensor Fusion) • Combines heterogeneous data streams to one unified view • Aggregation (Data Fusion) • Reduce amount of data: • Clustering • Filtering • Pattern recognition • Complex event processing • Smart Adaptation • Higher level information processing • Real-time reasoning • Enables adaptation of the data processing pipeline

9. CityPulse Framework • User Centric Decision Support • Goal: provide optimal configuration of smart city applications • Social and context analysis • Matchmaking and discovery mechanisms • Match data according to users preferences and context • Reliable Information Processing • Challenge: Dynamic environments, changes and prone to errors • Reliable data processing requires accuracy and trust (reputation) • Cope with • Malfunctions • Disappearing sensors • Conflicting data by monitoring of streams (runtime) • Smart City Applications

10. Content • Introduction • Framework for Smart City Data Analysis • QoI Monitoring • Spatial Reasoning • Conclusion

11. Problem: Unreliable Data • Unreliable, outdated, temporarily unavailable data • Contradicting data • Single data sources could provide faulty information • Example • Travel planning application that needscurrent traffic information • Traffic sensors deliver contradictory information • Malfunctioning sensor which delivers false information? or Local traffic jam? • Provenance of Data • Trust in social media data Jam! Ok

12. Identification of application independent information quality parameters and metrics Definition of an explicit semantic modelfor quality annotation of smart city data streams Result: 5 Categories, 23 Parameters Modelling Trustworthiness and QoI

13. Quality of Information Quality of Information (QoI)

14. Atomic Monitoring: Rating Current Implementation for: • Frequency: (based on t(x)virt – t(x-1)virt) • Age: (based on tnow – t(x-1)sample) • Latency: (based on t(x)virt – t(x)sample) • Completeness: (completeness of tuple) • Correctness: sanity check derived from stream annotation (value range, data format, etc.)

15. Atomic Monitoring – QoI Explorer

16. Atomic Monitoring Evaluation – QoI Explorer

17. Atomic Monitoring: Traffic Frequency

18. Where are the bad sensors?

19. Composite Monitoring: Correlation Determine Temporal Distance Compute PartialCorrectness Determine Temporal Distance Compute PartialCorrectness Compute CompositeCorrectness Find Correlated Streams Event Determine Temporal Distance Compute PartialCorrectness . . . . . . Which streams can be used to validate event? How long does it take for the event to reach the sensor? Does the other stream agree? Do all other streams agree?

20. Composite Monitoring • Time series analysis • Sensors 179202 and 179228 detecting slow traffic at event time  assumption that event is plausible

21. Content • Introduction • Framework for Smart City Data Analysis • QoI Monitoring • Spatial Reasoning • Conclusion

22. Improve QoI by Finding Correlated Streams

23. Euclidean Distance Does not Reflect Data for Infrastructure (Like Streets) The nearest traffic sensor does not reflect the traffic status. Voronoi diagram - depicting the nearest traffic sensor (labelled with a number) and traffic condition value for every street segment inside a Voronoi cell:

24. Example: Misleading Distances • How far is the next hospital?

25. Optimisation by Distance Metric • Correlating similarities between sensor time series against their distance to each other • Better regressions when using shortest path distance • Convincing model (less corellation with higher distance) • Smaller variance Comparing 1 Parking Garage Sensor against 10 Traffic Sensors: 449 Traffic Sensors in Aarhus Denmark

26. Correlation of Distance Metrics • Pairwise correlation of 449 traffic sensors. • Resulting correlation values (Pearson correlation) have been correlated against different distance models. • => The utilisation of matching metrics and a time shift of the time series shows a significant effect on the correlation value. Time Offset: modells propagation speed

27. Conclusion Objective: Enable uptake of context-aware Smart City applications Approach • MakeRawData Meaningful • Semantic annotation for knowledge based machine interpretation • Processing CapabilitesforUnreliable Data • Modelling and processing trustworthiness and QoI • Reasoning in the city depends heavily on spatial context • Appropriate distance measures are required by spatial reasoning,e.g. shortest path • Multiple information coverage of the same spatiotemporal boundaries is needed • Individual distance calculations help finding correlation partners (Euclidean dist. is not sufficient, but can be first iteration step) • Cross domainre-usabletools • To overcome silo architectures and • ease service creation

28. Thank you! • EU FP7 CityPulse Project: http://www.ict-citypulse.eu/ @ictcitypulse r.toenjes@hs-osnabrueck.de

29. Backup