Recognizing Exponential Inter-Contact Time in VANETs Hongzi Zhu Appeared at IEEE INFOCOM 2010, San Diego

Introduction to Vehicular Ad hoc Networks A special case of MANET and DTN Vehicles can talk while moving Vehicle density accounts Wireless communication inter-vehicle, vehicle-roadside Data forwarding Store-and-forward vs. store-carry-forward Provision of rich applications Next-generation intelligent transportation system (ITS), e.g., driving safety, efficient traffic infrastructure management, onboard entertainment

Why Mobility Models? System performance heavily depends on vehicular mobility characteristics how often can two vehicles encounter how long can such communications last Inter-contact time (ICT) studies the time elapsed between two successive contacts of the same two vehicles No existing vehicular mobility models based on real experiments theoretical mobility models, such as RWM, RWP and RDM, not practical for real systems some empirical studies mainly based on human mobility, indicating that the ICT tail distribution follows a power law distribution (heavy tail) How about in VANETs?

ShanghaiGrid Project 6,800 taxies and 3,600 buses equipped with GPS devices

Collecting Taxi GPS Trace Data GPRS channel GPS signals

Taxi GPS Trace Data Collecting vehicular trace data (demo available at http://www.cse.ust.hk/dcrg) select motion trace data from 2,100 taxies during the whole month of February in 2007 Data description loaded taxies send GPS reports every one minute vacant taxies send GPS reports every 15 seconds a GPS report: ID, the longitude and latitude coordinates of current location, timestamp, speed, heading and the status of the taxi (i.e., loaded or vacant)

A real contact between v1 and v2 Extracting Contacts A contact happens when the reported locations of two taxies are within a given time window and within a given communication range at the same time Using sliding time window to examine contacts between a pair of taxies A real contact between v1 and v2 A GPS report from v1 A sliding time window t1 A GPS report from v2

Calculate Inter-Contact Time the time elapsed between two successive contacts of the same pair of vehicles ICT1 ICT2 ICT3

Impact of Sliding Time Window Size Large time windows false contacts: two taxies may actually be far away from reported locations increase the weight of small values of inter-contact times in the whole distribution Small time windows miss real contacts: two taxies encountered but did not send reports simultaneously increase the weight of large values of inter-contact times in the distribution

Tail Distribution (CCDF) of the Inter-Contact Time Straight lines in linear-log scale indicate exponential tail distributions, i.e., P{X>t} ~e-βt This rapid cutoff is caused by the limited duration of the trace data Linear-log scale, time window (TW): 1s, 30s and 60s, communication range (CR): 50m and 100m

Establishing Model Parameters Remove the cutoff part seek for the divide point from which the second derivatives (decay acceleration) of the log-scaled P{X>t} are nonzero (take a small positive value in practice). Identify the exponent constant β perform the least square regression analysis

Future Work Investigate the key factors that generate the exponential tail distributions Establish theoretical models suit for VANETs Investigate the ultimate end-to-end delay with regards to link failures

Thank you! Q&A