an ieee 802.11p empirical performance model for cooperative systems applications
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An IEEE 802.11p Empirical Performance Model for Cooperative Systems Applications S. Demmel, G. Larue, D. Gruyer, A. Rakotonirainy ITSC 2013 – MoD2.5 – Presenter: S. Demmel / [email protected] 17:12-17:30, Monday 7 October 2013, The Hague, NL. Outline. Introduction - PowerPoint PPT PresentationTRANSCRIPT
Sébastien Demmel – 07/10/2013
An IEEE 802.11p Empirical Performance Model for Cooperative Systems Applications
S. Demmel, G. Larue, D. Gruyer, A. Rakotonirainy
ITSC 2013 – MoD2.5 – Presenter: S. Demmel / [email protected]:12-17:30, Monday 7 October 2013, The Hague, NL
Sébastien Demmel – 07/10/2013
Outline
• Introduction– Research goal & rationale– Model’s goals
• Experimental summary• General principles
– Frame loss model– Latency model
• Finer points– Parameterisation & profile generation– Performance improvements
• Limitations• Conclusion
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Sébastien Demmel – 07/10/2013
Introduction
• Research goal: provide a realistic, yet simple, network performance model for application-centric simulation environments (micro- or macroscopic).
• Why?– Facilitate study of Cooperative-ITS applications– Reduce complexity/computational load when network
topology is not overly complex– Solve lacking performance of some existing physical
layer models
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Introduction
• Model will:– Focus on a few critical performance metrics– Use empirical data to closely model actual on-road performance– Approximate the lower OSI layers and serve upper-layer
applications specifically
• Intended use in detailed microscopic simulations, for small group of vehicles
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VEINS microscopic traffic
simulation with complex network
topology
SiVICsensors & applications simulation focused on
smaller group of vehicles
Sébastien Demmel – 07/10/2013
Experimental summary
• 1st setup: empirical performance evaluation of IEEE 802.11p devices– Devices sourced from CVIS project + open-
source drivers– 2 devices (mobile & static)– 3 main metrics (range, frame loss, latency)– Satory test tracks (road-like environment)
from 09/2011 to 02/2012, 450+ km driven– Results presented at IEEE IV Symposium
2012• Measured performance was lower than
standard, the effects of several environmental factors were studied, good latencies
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Sébastien Demmel – 07/10/2013
Experimental summary
• 2nd setup: same hardware as first setup, with:– 3 devices (2 static, one mobile)– 1 day measurements (12/2012)– Metrics related to specific points of interest
for the model
• Findings detailed later in this presentation
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General principles
• OSI approximation: no complex topology, point-to-point connections
• Focus on closely matching experimental data and being able to generate new “plausible” data
• 2 modelled performance metrics:– Frame loss– Latency
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Sébastien Demmel – 07/10/2013
Frame loss model
• Frame loss model is based on profiles– a profile is a frame loss probability curve generated for each
single uninterrupted connection between 2 nodes– Temporally consistent– Parameterised on empirical data
• Includes multi-path reflections from the ground, objects and vegetation, weather, and hardware in-homogeneities
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Sébastien Demmel – 07/10/2013
Frame loss model
• Frame loss probability (t), a function of:– Distance– Relative speed
• A, B, ..., F are parameters estimated on empirical data
• The parameters values depend on speed– 4 classes (0-40, 40-60, 60-100, 100-160 km/h)– Each classes has its own set of cumulative distribution functions
for each parameters– Correlations between some parameters are present
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Frame loss model 10
C (F-E)/D (1-E)/D0
1
F
A
Range
Fram
e lo
ss ra
tio
profilelinear partbell curve
prop. to -B
Sébastien Demmel – 07/10/2013
Latency model
• Experimental evidences showed that latency does not depend on relative speed or distance
• Latency is a function of the packet size– Accounts only for direct connectivity– Accounts for delays caused by increased network
activity (up to 6 devices active together)
• Model is most appropriate for Basic Safety Messages (BSM) like packets
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Latency model
• Latency can be computed with continuous or digital distributions, depending on the simulation’s architecture
• Example: 1000 drawings per packet size class, 5 ms increments
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Parameterisation
• FL profile has 6 parameters
• Procedure:– Each parameters is first estimated with a Levenberg-Marquardt
algorithm for non-linear least squares– Then we extract a continuous PDF via a smoothed non-
parametric probability density estimate– PDF are transformed to Cumulative distributions
• Inverse transform sampling can be used to drawn a value (per parameter) for each generation of a new profile
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Sébastien Demmel – 07/10/2013
Parameterisation
• Parameters E and D show correlation– with: where α,β are estimated
from experimental data– α,β are used to subdivide the [100-160] km/h class for improved
fidelity to experimental data
• Influence of air humidity(hence weather) was considered, but not enoughvariation in the dataset toconclude
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Speed (km/h)
Absolute air humidity (g/m3)
Ran
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Profiles generation 15
• 100 profiles in the [60-100] km/h class
Sébastien Demmel – 07/10/2013
Profiles generation 16
0 200 400 600 800 10000
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Range (metres)
Fram
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Very low speeds
0 200 400 600 800 10000
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High speeds
0-40 km/h 40-60 km/h
60-100 km/h 100-160 km/h
Sébastien Demmel – 07/10/2013
Performance improvements
• Question: when is it appropriate to generate new profiles?
• The “neighbours problem”– Two nearby nodes connected to a same 3rd node can have
widely diverging performance– Within which distance can we generate only a single profile for
several nodes?
• What is the “shelf life” of a profile?– Will 2 nodes connecting to a same 3rd node a few minutes apart
from the same direction be able to re-use the same profile?
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Sébastien Demmel – 07/10/2013
Performance improvements
• The “neighbours problems”
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Profile 1(F-E)/D = 225 mmax range 349 m
Profile 2(F-E)/D = 706 mmax range 789 m
Sébastien Demmel – 07/10/2013
Performance improvements
• If two nodes are, at max, 25 metres apart, can they use the same profile?
• We use data from the 2nd setup– Metrics: (F-E)/D and (1-E)/D, the boundaries of the increasing
FL slope / average (F-E)/D = 303 metres– 2nd dataset confirms earlier findings for motion direction
symmetry and expectations about antennas effects
• Yes– Accounting for the 25 metres intra-nodes distance, we find at
worst a 10% (37 metres) error between each nodes for each lap
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Performance improvements
• Profile’s “shelf life”– Can we re-use a same profile for 2 consecutives
vehicles?– Would be especially useful with a significant traffic
density
• No, we probably should not
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9 laps over about 20 minutes, a third showed large lap-to-lap changes
Sébastien Demmel – 07/10/2013
Performance improvements
• Summary, through an example:– A roadside unit connecting to a flow of incoming vehicles arriving
with random interdistances– Isolated vehicles have their own profile– Compact group of vehicles less than a minute apart can use the
same profile
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Profile 4
RSUProfile 3 Profile 2
Profile 2
Sébastien Demmel – 07/10/2013
Limitations
• Limited set of environmental conditions– Accurate for open freeway, or rural and suburban environments– Cannot be used for urban scenarios– Weather is present in the data, but cannot be used as a
parameter (no weather-bases classes)
• Limited number of nodes– No routing, groups management– Needs refinements for “grouped” FL profiles generation
• Focused on packets up to 1 kB (no specific mechanism for larger multi-packets payloads)
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Conclusion
• We created a resource-inexpensive empirically-based model for key IVC metrics– Provides realistic networking for application-centric
simulation, e.g. for the evaluation of C-ITS applications such as CCW or road trains
• The model still needs to be extended to include a wider range of environmental conditions (urban, different weathers,...), different hardware types and improve the management of profiles with larger number of nodes
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Sébastien Demmel – 07/10/2013
Conclusion
Thank you for attending this presentation!
Dr. Sébastien DemmelResearch associate, Centre for Accident Research and RoadSafety – [email protected] / +61 7 3138 7783130 Victoria Park Road, QLD 4059 Kelvin GroveAustralia.