Http://aqualab.cs.northwestern.edu John Otto, Fabián Bustamante & Randall Berry EECS, Northwestern...

http://aqualab.cs.northwestern.edu

John Otto, Fabián Bustamante & Randall BerryEECS, Northwestern University

Down the Block and Around the Corner

The Impact of Radio Propagation onInter-vehicle Wireless Communication

Otto, Bustamante & Berry 2

Distributed systems on wheels

Size- and power-unlimited mobile network platform– Infrastructure-less– Mobility facilitates rapid information dissemination

Many promising applications– Traditional Internet access– Environmental sensing– Traffic advisory and driver safety

Challenging environment– Rapidly changing topology– Network density depends on vehicular density

Down the Block & Around the Corner


VANETs and the need for simulation

Live experimentation – Viable when a few nodes are enough– OK for a proof of concept– Not an option with 100’s of vehicles

Simulation-based experimentation and its risks– No agreed-upon platform– Vehicular mobility

• Traces and models– Signal propagation

• Trading scalability and realism



A building between us – urban networking

Performance of the network stack’s physical layer defines the boundaries of a system’s ability

… and your environment determines the performance of the physical layer

How does this impact our applications’ performance?

Signal propagation varies widely between open field

and urban settings



Measurement studies and where we fit in

Challenging assumptions– Kotz et al. (2004)

Opportunistic connectivity– Ott & Kutscher (2004)– Wu et al. (2005) (multi-hop V2V)– Bychkovsky et al. (2006)– Hadaller et al. (2007)

Varied environments– Singh et al. (2002)

DSRC 5.9 GHz band– Taliwal et al. (2004)– Cheng et al. (2007)

We focus on– Vehicle-to-vehicle (V2V)– Varied environments– Line-of-sight (LOS)

versus non-LOS communication



Modeling the physical layer

Deterministic models– Free space and two-ray ground– Ideal LOS (and ground reflection) signal strengths

• Do not account for variations in environment

Empirical models– Based on measurements taken in an environment– Ray Tracing1

• Requires detailed knowledge of the environment• Incurs significant computational cost• Does not scale

– Probabilistic empirical model• Two parameters used to describe the environment• Typically a good compromise between realism, scalability

1McKown & Hamilton. “Ray tracing as a design tool for radio networks.” 1991.



),0(log104

log1020

100

10 dBNd

ddPL

Parameters– Path Loss Exponent (β) : environment decay rate– Shadowing (σdB): variation due to obstacles

Can complex environments be modeled using just two parameters?

Log-normal path loss with shadowing


Free Space path loss

Environment path loss

Random variations

(obstacles)

),0(log104

log1020

100

10 dBNd

ddPL


Goals and methodology

Characterize signal propagation in urban settings– Pick representative environments– Measure signal propagation in

• line of sight (LOS) and• non-LOS (Around the Corner – ATC) settings

Pick a signal propagation model, a good simulator, and a simple application– Free-space, probabilistic shadowing …– ns, GloMoSim, JIST/SWANS …

Evaluate application-level impact of environment

This work appeared in Proc. of ICDCS, 2009



Roadmap

Overview of radio propagation models

Experimental characterization of radio propagation in an urban setting (Chicago)– Measurement platform – Measured environments– Data analysis

Understanding the impact of signal propagation parameters on application performance

Conclusion



Measurement platform

Set of equipped vehicles with– Soekris net4801-60 machines,

256 MB memory, 1GB flash storage

– Garmin GPS 18 USB for positioning

– Ubiquiti Networks 2.4 GHz 802.11b/g

– 7 dBi 2.4 GHz omni-directional antenna

Software– Linux (2.6.19 kernel)– iperf (CBR UDP stream)– tcpdump

GarminGPS 18 USB

Soekris net4801 running Linux

7 dBi omni-directionalantenna



Understanding the environment

Measurement in representative environments & times

Open field – Provides a baseline; no buildings or any other obstacles

Suburban – Residential area with trees, cars and houses set back from the road with space between them

Urban – Large and tall buildings, very close to the street, few gaps between buildings, etc


Run experiments:

• Daytime (high traffic)

• At night (low traffic)


Simple environment – Open field


No traffic

Path loss exponent stabilizes at 3.10

Line-of-Sight (LOS)

Communication

Same roadP

ath

loss

exp

onen

t

Distance (meters)

β / σ Open Field Suburban Urban

LOS 3.10 / 3.23

ATC




No traffic

Median path loss exponent = 3.29

Around the Corner(ATC) Communication

Perpendicular roads

Pat

h lo

ss e

xpon

ent

Distance from intersection (meters)

Dis

tanc

e (

met

ers)


LOS 3.10 / 3.23

ATC 3.29 / 3.35



Same road Perpendicular roads



LOS 3.10 / 3.23

ATC 3.29 / 3.35


More complex settings – Line of sight

Suburban Open field



LOS 3.10 / 3.23 3.14 / 7.28

ATC 3.29 / 3.35


Suburban

… Around the corner (ATC)

Open Field



LOS 3.10 / 3.23 3.14 / 7.28

ATC 3.29 / 3.35 3.87 / 8.44


… Line of sight and Around the corner




LOS 3.10 / 3.23 3.14 / 7.28

ATC 3.29 / 3.35 3.87 / 8.44

• At 50 meters apart, LOS and ATC β = 3.2

• At 80 meters apart, LOS β = 3.1… but ATC β > 4 !


Urban ATC

Urban



LOS 3.10 / 3.23 3.14 / 7.28 3.17 / 9.15

ATC 3.29 / 3.35 3.87 / 8.44 4.05 / 10.74

non-LOS communication, higher path loss exponent due to diffraction, reflection

50 meters apart, in LOS

> 100 meters apart, no communication possible


Suburban vs. Urban – Around the corner

Suburban Urban


Can be 20 meters from intersection before

observing PLE increaseDistance of obstructions from the road:• Suburban: wide front lawns• Urban: narrow sidewalks

Immediate increase in PLE after leaving intersection


Line-of-sight experimental summary

Obstacles increase signal variability (shadowing parameter)– e.g. from σ = 3.23 in an open field to 9.15 in an urban setting

Vehicular traffic degrades signal strength

Overall, path-loss exponent is not significantly impacted– e.g. from 3.10 in an open field to 3.17 in an urban setting

Transmit range reduced by 14%– Open field: 1070 m– Urban: 915 m– (predicted with model)



Around-the-corner experimental summary

Path loss exponent varies significantly– e.g. 3.29 in an open field to 4.05 in an urban setting

Transmit range reduced by 70%– Open field: 715 m– Urban: 208 m– (predicted with model)

Non-LOS communication is possible– Reflection, diffraction– Gaps between buildings

Distance of obstacles from road is a significant factor



Experimental impact

Challenge assumption: one set of parameters is sufficient

Experiments contradict this assumption– For complex environments (suburban, urban)– LOS vs. non-LOS (ATC) is a key factor in communication– So, we actually need at least two sets of parameters:

• LOS and non-LOS (ATC)

What is the impact at the application layer?

Use simulations to evaluate application performance under– Environments– Parameter settings (e.g. LOS, ATC)



The impact of signal propagation parameters

Pick a signal propagation model, a good simulator, and a simple application

Signal propagation model– Log-normal path loss with shadowing

Sample application – Epidemic-based data dissemination– e.g. Communicating road (traffic) conditions– Push-based protocol, based on Vahdat & Becker (2000)

1. Beacon

2. Exchange digest

3. Send messages

Application performance metric: Delivery latency– e.g. Lower latency gives fresher data and better detouring ability



The impact of signal propagation parameters

For simple environments– LOS vs. ATC does not affect performance

However… for complex environments– LOS performance much higher than ATC– Combining data sets does not give average performance

We evaluate LOS&ATC– Switch between LOS and ATC parameters: same / different street– Gives expected intermediate performance– Compromise between scalability and realism



Simulation configuration

For simulation – JiST/SWANS++ – http://www.aqualab.cs.northwestern.edu/projects/swans++/

For vehicular mobility – STRAW– Using real cities’ road maps

• Lights, signals, speed limits

– IDM car-following– MOBIL lane-changing– http://sourceforge.net/projects/straw/

Parameters– Map: downtown Chicago (approximate Manhattan grid), 1.76 km2

– Radio settings: match experiment configuration• 26 dBm transmit power, 7 dBi antenna gain, 2 Mbps fixed data rate

– 150 vehicles– 2 hour duration


http://www.aqualab.cs.northwestern.edu/projects/swans++/

http://sourceforge.net/projects/straw/


Application performance in LOS vs. ATC


LOS

ATC

In an open field, the locations of the communicating vehicles (in line-of-sight or not) have no performance impact

Open fieldsetting

with traffic


Application performance in downtown area


LOS

ATC

In urban settings, around-the-corner parameters mean smaller transmit range, hence lower performance

Urban setting

β / σ Urban

LOS 3.17 / 9.15

ATC 4.05 / 10.74


Urban – Combining datasets


LOS

ATC

Combined

Averaging parameters – by combining datasets – doesn’t yield averaged performance

Urban setting

β / σ Urban

LOS 3.17 / 9.15

ATC 4.05 / 10.74

Combined 3.43 / 11.95

Intermediate PLE, but increased shadowing


LOS & ATC – A compromise


Urban setting LOS

ATC

LOS&ATC

Using two parameter sets and relative vehicle position,select LOS or ATC parameters based on node position


Application-level impact summary

Simple environments (open field)– One set of parameters is sufficient– No difference in performance between LOS and ATC parameters

Complex environments (suburban, urban)– Using one set of parameters (LOS or ATC) is not sufficient– Combining LOS and ATC gives worse than expected performance– LOS&ATC approach gives the expected intermediate performance

Possible extensions to LOS&ATC– Tolerance for distance from the intersection– Simulating heterogeneous environments on the same map– Utilizing LOS/ATC information at the protocol or application layers– …



Conclusion

LOS is a major factor of signal propagation characteristics in complex environments

Accounting for LOS versus non-LOS has a significant impact on application-level performance

LOS&ATC is a computationally scalable and more realistic approach for modeling complex environments

Part of C3R, a project on urban environmental monitoring through vehicular networks, working towards– Ensuring sustainable urban growth– Participatory sensing with a mobile platform– Applications including traffic advisory, air quality and noise monitoring






With traffic,Increased β (3.31) and σ


LOS 3.10 / 3.23

ATC 3.29 / 3.35


More complex settings – Urban LOS

Urban Open field



LOS 3.10 / 3.23 3.14 / 7.28 3.17 / 9.15

ATC 3.29 / 3.35 3.87 / 8.44

Similar to suburban: larger variations in path loss exponent

Http://aqualab.cs.northwestern.edu John Otto, Fabián Bustamante & Randall Berry EECS, Northwestern...

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