Ahmed HelmyComputer 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:

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, ….

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!

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

• Heavy-tailed distributions better at modeling empirical values of traffic densities.

• Heavy-tailed distributions combined model more than 85% of all 700+ locations.

Heavy-tailedMemory-less

Distributions: Curve fitting

6

0

1

2

3

4

5

6 x 10 5

Prob

abili

ty D

ensi

ty

Time

Measured DataExponentialLog gammaLog logisticNormalWeibull

• 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.

8

Percentage locations with Self-similar traffic• Shows the distribution of seven estimators of

Hurst parameter• Value ranges from 0.5 – 0.9

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.

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?

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

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.

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

Thank you!Ahmed Helmy helmy@ufl.eduURL: www.cise.ufl.edu/~helmy

NOMADS, MobiLib: cise.ufl.edu/~helmy/MobiLib

* Thanks to my students and collaboratorsMobiCom 2010, Chicago