1. Ahmed Helmy Computer and Information Science and Engineering (CISE) Department University of Florida helmy@ufl.edu, http://www.cise.ufl.edu/~helmy Founder & Director: NOMADS Group & Wireless Mobile Networking Lab Global-Scale Sensing and Analysis of Vehicular Mobility (Forming Big Data Vehicular Traces) Funded by:

2. Why Vehicular Measurement? Mobile Networking –Vehicular mobility modeling, simulation –Evaluation of emerging mobile vehicular networks protocols, service, application design & analysis –V-2-V communication: federal requirement in 2015! Transportation –Understanding traffic congestion build-up –Congestion mitigation, traffic mgmt –Transportation urban planning, pollution monitoring, public safety management, ….

3. Vehicular Traces: The Dilemma Need traces for realistic modeling, simulation –Where is the data? (started looking ~2003!!) Vehicular Traces Have ‘some’ data on protected server in locked room I can get in, but cannot take anything out with me! I can show you, but then I’d have to kill you !!! 3 vehicular-related traces (accel, AP, connectivity) 1 taxi cab location trace [privacy, contractual issues] Pedestrian Traces Have collected data. Will make it available & send you the link We have measurements, we will make it available through Crawdad website 110 traces (pedestrian mobility/locations, device- encounters, wireless signal, network measurements) Need a new approach… for sure!

4. Vehicular Mobility Sensing at Planet Scale - Imagery data from webcams - Estimate traffic density - Spatio-temporal analysis, modeling Vehicular Tracing System Traffic density estimates * IEEE INFOCOM NetSciCom 2012, GI 2013. * ACM MobiSys HotPlanet 2012, * ACM SIGSPATIAL IWCTS 2013 (Best Paper Award) * G. Thakur, P. Hui, A. Helmy

5. Traffic Modeling 5 Heavy-tailed distributions better at modeling empirical values of traffic densities. Heavy-tailed distributions combined model more than 85% of all 700+ locations. Heavy-tailed Memory-less

6. Distributions: Curve fitting 6 Heavy-tailed better at fitting empirical distribution Log-gamma, Log-logistic, Weibull Memory-less deviate

7. Scaling of stochastic self similar traffic Granularity of traffic is scaled from 1 minute to 10, 100, to 1000 minutes. Plots are invariant to the chosen time granularity. 7

8. Percentage locations with Self-similar traffic Shows the distribution of seven estimators of Hurst parameter Value ranges from 0.5 – 0.9 8

9. Percentage locations with Self-similar traffic Mean value > 0.65. Plots are invariant to the chosen time granularity. The percentage of locations from every region that have self-similarity in their traffic patterns. 9

10. Future Work Spatio-temporal Analyses: –congestion causality analysis & prediction Vehicular mobility simulator comparisons Link to design & evaluation of vehicular networks Cross-correlate between pedestrian & vehicular mobility Other sources of data? New architectures for large scale VNets?

11. Municipal Vehicular Trace DBs - Large-scale instrumentation on roads (line detectors/sensors, road-side microwave antennas, STEWARD DB in FL) - Integration from multiple sources and cross-correlation/processing

12. Vehicular Networking at Scale: Smart Plates - Government based initiative – The automobile ‘black box’ & more - Does not require car modifications or manufacturing - Several issues of management, privacy, safety, security, etc.

13. Final Thoughts Pressing need for a community-wide library of vehicular measurements/traces of various types [ VehiLib !] Real need for a rich set of scenarios for evaluating, simulating different services, protocols, applications Benchmarking: systematic realistic purposeful testing for worst, best and average cases

14. Thank you! Ahmed Helmy helmy@ufl.edu URL: www.cise.ufl.edu/~helmy NOMADS, MobiLib: cise.ufl.edu/~helmy/MobiLib * Thanks to my students and collaborators MobiCom 2010, Chicago