1. IoT Week, 2 nd June 2016Ralf Tönjes1 University of Applied Sciences Osnabrück Satelliten- und MobilfunkProf. Dr.-Ing. Ralf Tönjes1 Ralf Tönjes University of Applied Sciences Osnabrück, Germany CityPulse: Reliable Information Processing in Smart City Frameworks

2. IoT Week, 2 nd June 2016Ralf Tönjes2 University of Applied Sciences Osnabrück Content 1.Introduction 2.Framework for Smart City Data Analysis 3.QoI Monitoring 4.Spatial Reasoning 5.Conclusion

3. IoT Week, 2 nd June 2016Ralf Tönjes3 University of Applied Sciences Osnabrück Smart Services are Context-aware Personal Digital Assistant Recommender System Advertisements Context-aware Traffic Management Augmented Reality

4. IoT Week, 2 nd June 2016Ralf Tönjes4 University of Applied Sciences Osnabrück

5. IoT Week, 2 nd June 2016Ralf Tönjes5 University of Applied Sciences Osnabrück 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. IoT Week, 2 nd June 2016Ralf Tönjes6 University of Applied Sciences Osnabrück Content 1.Introduction 2.Framework for Smart City Data Analysis 3.QoI Monitoring 4.Spatial Reasoning 5.Conclusion

7. IoT Week, 2 nd June 2016Ralf Tönjes7 University of Applied Sciences Osnabrück An Integrated Approach Re-usable components

8. IoT Week, 2 nd June 2016Ralf Tönjes8 University of Applied Sciences Osnabrück 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 CityPulse Framework

9. IoT Week, 2 nd June 2016Ralf Tönjes9 University of Applied Sciences Osnabrück 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 CityPulse Framework

10. IoT Week, 2 nd June 2016Ralf Tönjes10 University of Applied Sciences Osnabrück Content 1.Introduction 2.Framework for Smart City Data Analysis 3.QoI Monitoring 4.Spatial Reasoning 5.Conclusion

11. IoT Week, 2 nd June 2016Ralf Tönjes11 University of Applied Sciences Osnabrück  Unreliable, outdated, temporarily unavailable data  Contradicting data  Single data sources could provide faulty information Example –Travel planning application that needs current 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 Problem: Unreliable Data

12. IoT Week, 2 nd June 2016Ralf Tönjes12 University of Applied Sciences Osnabrück Modelling Trustworthiness and QoI Identification of application independent information quality parameters and metrics Definition of an explicit semantic model for quality annotation of smart city data streams Result: 5 Categories, 23 Parameters

13. IoT Week, 2 nd June 2016Ralf Tönjes13 University of Applied Sciences Osnabrück Quality of Information Quality of Information (QoI)

14. IoT Week, 2 nd June 2016Ralf Tönjes14 University of Applied Sciences Osnabrück Atomic Monitoring: Rating Current Implementation for: Frequency: (based on t(x) virt – t(x-1) virt ) Age: (based on t now – 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. IoT Week, 2 nd June 2016Ralf Tönjes15 University of Applied Sciences Osnabrück Atomic Monitoring – QoI Explorer

16. IoT Week, 2 nd June 2016Ralf Tönjes16 University of Applied Sciences Osnabrück Atomic Monitoring Evaluation – QoI Explorer

17. IoT Week, 2 nd June 2016Ralf Tönjes17 University of Applied Sciences Osnabrück Atomic Monitoring: Traffic Frequency

18. IoT Week, 2 nd June 2016Ralf Tönjes18 University of Applied Sciences Osnabrück Where are the bad sensors?

19. IoT Week, 2 nd June 2016Ralf Tönjes19 University of Applied Sciences Osnabrück Find Correlated Streams Determine Temporal Distance Compute Partial Correctness Compute Composite Correctness Event... 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? Composite Monitoring: Correlation

20. IoT Week, 2 nd June 2016Ralf Tönjes20 University of Applied Sciences Osnabrück Composite Monitoring  Time series analysis  Sensors 179202 and 179228 detecting slow traffic at event time  assumption that event is plausible 20

21. IoT Week, 2 nd June 2016Ralf Tönjes21 University of Applied Sciences Osnabrück Composite Monitoring: Pre-Processing to remove daily patterns

22. IoT Week, 2 nd June 2016Ralf Tönjes22 University of Applied Sciences Osnabrück Content 1.Introduction 2.Framework for Smart City Data Analysis 3.QoI Monitoring 4.Spatial Reasoning 5.Conclusion

23. IoT Week, 2 nd June 2016Ralf Tönjes23 University of Applied Sciences Osnabrück DistanceSightWayTrack/Vehicle Propagation Radial Radial with blocking Distinct Grid Restricted Layer on base Grid Example PollutionLightStreet SystemSubway Ride Feasibility SimpleComplexMedium Improve QoI by Finding Correlated Streams

24. IoT Week, 2 nd June 2016Ralf Tönjes24 University of Applied Sciences Osnabrück 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:

25. IoT Week, 2 nd June 2016Ralf Tönjes25 University of Applied Sciences Osnabrück Example: Misleading Distances How far is the next hospital?

26. IoT Week, 2 nd June 2016Ralf Tönjes26 University of Applied Sciences Osnabrück Results for Misleading Distances Task: find next available location for infrastructure 200000 Random start locations 3 hospitals, 13 pharmacies, 25 ATMs, 36 toilets, 45 waste baskets and 288 parking places Create a sorted list => Euclidean distance does often not reflect the shortest path

27. IoT Week, 2 nd June 2016Ralf Tönjes27 University of Applied Sciences Osnabrück 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

28. IoT Week, 2 nd June 2016Ralf Tönjes28 University of Applied Sciences Osnabrück 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

29. IoT Week, 2 nd June 2016Ralf Tönjes29 University of Applied Sciences Osnabrück Conclusion Objective: Enable uptake of context-aware Smart City applications Approach  Make Raw Data Meaningful  Semantic annotation for knowledge based machine interpretation  Processing Capabilites for Unreliable 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 domain re-usable tools  To overcome silo architectures and  ease service creation

30. IoT Week, 2 nd June 2016Ralf Tönjes30 University of Applied Sciences Osnabrück Thank you! EU FP7 CityPulse Project: http://www.ict-citypulse.eu/ @ictcitypulse r.toenjes@hs-osnabrueck.de

31. IoT Week, 2 nd June 2016Ralf Tönjes31 University of Applied Sciences Osnabrück Backup