John Otto, Fabián Bustamante & Randall Berry
EECS, Northwestern University
http://aqualab.cs.northwestern.edu
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
Otto, Bustamante & Berry
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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
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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
Signal propagation varies
widely between open field
and urban settings
How does this impact our applications’ performance?
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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)
We focus on
– Vehicle-to-vehicle (V2V)
– Varied environments
– Line-of-sight (LOS)
versus non-LOS
communication
Varied environments
– Singh et al. (2002)
DSRC 5.9 GHz band
– Taliwal et al. (2004)
– Cheng et al. (2007)
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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.
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d 
 4d 0 
PL  2 10 log 10 
  10 log 10   N (0,  dB )
  
 d0 
Free Space
path loss
Environment
path loss
Random
variations
(obstacles)
Parameters
– Path Loss Exponent (β) : environment decay rate
– Shadowing (σdB): variation due to obstacles
Can complex environments be modeled
using just two parameters?
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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
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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
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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
Soekris net4801
running Linux
Software
– Linux (2.6.19 kernel)
– iperf (CBR UDP stream)
– tcpdump
Otto, Bustamante & Berry
Garmin
GPS 18 USB
Down the Block & Around the Corner
7 dBi omnidirectional
antenna
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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)
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Path loss exponent
Line-of-Sight
(LOS)
Communication
No traffic
Path loss exponent
stabilizes at 3.10
Same road
Distance (meters)
β / σ Open Field Suburban
LOS
Urban
3.10 / 3.23
ATC
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Perpendicular roads
Median path loss
exponent = 3.29
Distance from intersection (meters)
β / σ Open Field Suburban
Otto, Bustamante & Berry
Distance (meters)
Path loss exponent
Around the Corner
(ATC) Communication
No traffic
LOS
3.10 / 3.23
ATC
3.29 / 3.35
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Urban
13
Same road
Perpendicular roads
β / σ Open Field Suburban
Otto, Bustamante & Berry
LOS
3.10 / 3.23
ATC
3.29 / 3.35
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Urban
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Suburban
Open field
β / σ Open Field Suburban
Otto, Bustamante & Berry
LOS
3.10 / 3.23
ATC
3.29 / 3.35
Urban
3.14 / 7.28
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Suburban
Open Field
β / σ Open Field Suburban
Otto, Bustamante & Berry
LOS
3.10 / 3.23
3.14 / 7.28
ATC
3.29 / 3.35
3.87 / 8.44
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Urban
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Same road
Perpendicular roads
• At 50 meters apart,
LOS and ATC β = 3.2
• At 80 meters apart,
LOS β = 3.1… but
ATC β > 4 !
Otto, Bustamante & Berry
β / σ Open Field Suburban
LOS
3.10 / 3.23
3.14 / 7.28
ATC
3.29 / 3.35
3.87 / 8.44
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Urban
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Urban
50 meters apart, in LOS
non-LOS communication,
higher path loss exponent
due to diffraction, reflection
β / σ Open Field Suburban
> 100 meters apart, no
communication possible
Otto, Bustamante & Berry
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
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Suburban
Can be 20 meters from
intersection before
observing PLE increase
Urban
Immediate increase in PLE
after leaving intersection
Distance of obstructions from the road:
• Suburban: wide front lawns
• Urban:
narrow sidewalks
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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)
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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
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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)
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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
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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
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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
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In an open field, the locations of the communicating vehicles
(in line-of-sight or not) have no performance impact
Open field
setting
with traffic
Otto, Bustamante & Berry
LOS
ATC
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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
LOS
ATC
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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
LOS
Combined
ATC
Intermediate PLE, but
increased shadowing
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Using two parameter sets and relative vehicle position,
select LOS or ATC parameters based on node position
Urban
setting
LOS
LOS&ATC
ATC
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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
…
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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
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Same road
Perpendicular roads
With traffic,
Increased β (3.31) and σ
β / σ Open Field Suburban
Otto, Bustamante & Berry
LOS
3.10 / 3.23
ATC
3.29 / 3.35
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Urban
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Urban
Similar to suburban:
larger variations in
path loss exponent
Otto, Bustamante & Berry
Open field
β / σ Open Field Suburban
LOS
3.10 / 3.23
3.14 / 7.28
ATC
3.29 / 3.35
3.87 / 8.44
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Urban
3.17 / 9.15
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