CARS: Context Aware Rate Selection for Vehicular Networks Pravin Shankar [email protected]...

CARS: Context Aware Rate Selection for Vehicular

Networks

Pravin [email protected]

Tamer [email protected]

Justinian [email protected]

Liviu [email protected]

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Vehicular networks today Ubiquity of WiFi

• Cheaper, higher peak throughput compared to cellular

New applications• Traffic Management• Urban Sensing (eg. Cartel)• In-car Entertainment• Social Networking (eg.

RoadSpeak, MicroBlog)

Requirement: High throughput

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What is rate selection?

802.11 PHY: multiple transmission rates

• 8 bitrates in 802.11a/g (6 – 54 Mbps)

• 8 bitrates in 802.11p (3 – 27 Mbps) Different modulation and coding schemes

Low High

Low High Error Rate

High Underutilization

Link Quality

Bitrate

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High quality link

Low quality link

Rate selection problem in vehicular networks

54 Mbps 6 Mbps

Rate Selection: Select the best transmission rate based on link quality in real-time to obtain maximum throughput

Low quality link

6 Mbps

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Outline

Introduction Existing solutions CARS: Context Aware Rate Selection Evaluation Conclusion

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Existing rate selection algorithms

ARF (1996), RBAR (2001), OAR(2004), AMRR (2004), ONOE (2005), SampleRate (2005), RRAA (2006) (and many more…)

Basic scheme in all existing algorithms

• Estimation: Use physical layer or link layer metrics to estimate the link quality

• (Re)Action: Switch to lower/higher rate

Question: How well do these algorithms work in vehicular environments?

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Existing schemes + vehicular networks: Experiment Outdoor experiments comparing

• SampleRate [2005]

• AMRR [2004]

• ONOE [2005] 5 runs per rate algorithm 5 runs per fixed rate Slow Mobility: 25 mph Metrics

• Average goodput

• Supremum goodput (maximum among all runs for all rates)

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Existing schemes + vehicular networks: Results

Underutilization of link capacity

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Existing schemes + vehicular networks: Analysis

Rapid change in link quality due to distance, speed, density of cars

Problems:1. Estimation delay

2. Sampling requirement

3. Collisions vs. channel errors

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Problem 1: Estimation delay

6 Mbps24 Mbps

54 Mbps

Link conditions change faster than the estimation window - the rate adaptation lags behind

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Problem 2: Sampling Requirement

When an idle client starts transmitting,there are no recent samples in the estimation window

Packet scheduling causes bursty traffic Results in anomalous behavior

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Problem 3: Collisions vs. errors

Hidden-station induced losses should not trigger rate adaptation [CARA06, RRAA06]

Lower rate prolongs packet transmission time, aggravating channel collisions

Use of RTS/CTS causes additional overhead

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Outline


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CARS at a glance

Rapid change in link quality due to distance, speed (context)

Vehicular nodes already have this context information

Use this cross-layer information at the link layer to estimate link quality and perform proactive rate selection

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CARS: reactive + proactiveLink Quality: Error Function

EH = f(bitrate, len)

• Reactive

• Short-term loss statistics from estimation window

EC = f(distance, speed, bitrate, len)

• Proactive

• Predicted error as a function of context information

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Proactive rate selection using Ec


Model link error rate as a function of context information and transmission rate• Empirically derived using data from outdoor

experiments

Simple model is sufficient because of discrete rates in 802.11

Context recalculation frequency = 100 ms

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CARS Algorithm

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CARS Implementation

The CARS algorithm was implemented on the open-source MadWifi wireless driver• ~ 520 lines of C code

Context information obtained from TrafficView [2004]• Generic /proc interface:

• Any other app can be extended to provide a similar interface Extensively tested by means of vehicular field

trials and simulations

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Outline


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CARS Evaluation

Effect of Mobility: How does CARS adapt to fast changing link conditions? (Field trial)

Effect of Collisions: How robust is CARS to packet losses due to collisions? (Field trial)

Effect of Density of Vehicles: How does the throughput improvement scale over large number of vehicles? (Simulation study)

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Effect of mobility: Setup

Scenarios• Stationary: Base case

• Cars are stationary next to each other.

• SlowMoving: A simple moving scenario • Cars are driving around the Rutgers campus: ~25mph speeds

• FastMoving: A more stressful moving scenario• Cars are driving on New Jersey Turnpike: ~70mph speeds in high

car/truck traffic conditions

• Intermittent: A scenario with intermittent connectivity• Cars move in and out of each other's range periodically - Hot-spot

scenario

Workload:• UDP traffic from TX to RX using iperf• Duration of experiment - 5 minutes

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Effect of mobility: Results

SampleRate

CARS

Stationary SlowMoving FastMoving Intermittent

Scenario

0

10

20

50

40

30

Goo

dput

(M

bps)

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Effect of mobility: AnalysisScenario: Intermittent

Reactive vs. Proactive

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Effect of vehicle density - Setup

Hotspot scenario:• Road of length 5000 m with multiple lanes

• Base station in the middle of the road Workload:

• Video stream: 1500 packets of size 1000 bytes each

• UDP: transmission rate 100 packets per second

• RTS/CTS disabled

• Max_retransmits: 4 ns-2 with microscopic traffic generator

• Compared CARS with AARF and SampleRate

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Effect of vehicle density - Results

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Effect of vehicle density - Analysis

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Outline


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Conclusion

Existing rate adaptation algorithms under-utilize vehicular network capacity

CARS: uses context information to perform fast rate selection

Significant goodput improvement over existing algorithms

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Backup Slides

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Limitations of CARS model

Other effects (non-modelled) can cause packet loss, eg. multipath, shadowing, environmental effects (rain or snow), background interference

Solution: Fall-back mode (α=0) Enter Fall-back mode if predicted packet loss – measured packet loss > Threshold

Future work: Better modeling

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Signal strength based rate adaptation

Stationary Vehicles Moving Vehicles (25 mph)

• RSSI Spikes (average 5 dB, peaks of upto 14 dB)

• Moving vehicles: large-scale path loss is more significant than small-scale fading

• Overhead due to 4-way RTS-CTS-DATA-ACK handshake [Kemp08]

• 802.11 frame format (CTS) needs to be extended

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Estimation window size

SampleRate default ew_size = 10 sec We modify SampleRate to ew_size = 1 sec

• Vehicle with speed 65 mph moves 30m in 1 sec

• Optimal rate could be different for distances separated by 30m

Problem with very small estimation window: Insufficient samples in estimation window [RRAA06]

Future work: Estimation window size tuning

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Capture Effect When there is a collision between the transmitter's frame

and a frame sent by a hidden node, the transmitted frame will be successfully demodulated if

• Pt and Pj are the received power from transmitter and hidden node

• αr: threshold ratio at transmission rate r Implications on rate adaptation: αr varies with r Existing collision-aware rate adaptation algorithms

do not consider capture effect Future work: model capture effect and use it to guide

our rate adaptation scheme

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Existing Models Existing models in literature

• Effect of Distance:• Free space path loss model

• Two ray propagation model in LOS environment

• More complex fading models (Rician, Rayleigh, …)

• Effect of Mobility:• Delay tap model

• Ray models with Rician delay profiles It is unclear how closely the outdoor VANET environment

resembles the existing models Our model is empirically derived using data from

extensive outdoor experiments

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Load and Overhead Comparison

Load Overhead

Load: average airtime needed to transmit one packet

Overhead: average non-useful airtime needed to transmit one packet

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Effect of Collisions

Scenario: Stationary vehicles located close to hot-spot (to guarantee high-quality links)

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Evaluation - Mobility - Scenarios

Elapsed Time (Sec) Elapsed Time (Sec)

Dis

tanc

e (m

)

Spe

ed (

mph

)

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CARS multi-rate retry chain

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Existing Rate Adaptation Algorithms

Auto Rate Fallback [Kamerman et al. ‘97]

• Drop the transmission rate on successive packet losses and increase it on successive successful packet transmits

Adaptive ARF [Lacage et al. ‘04]

• Uses dynamic instead of fixed frame error thresholds to decrease/increase rate

Robust Rate Adaptation Algorithm [Wong et al. ‘06]

• Uses a short-term loss ratio to opportunistically adapt to dynamic channel variations

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Existing Rate Adaptation Algorithms

SampleRate [Bicket et al. ‘06]

• Throughput-based scheme

• Goal is to minimize the mean packet transmission time

• Sends periodic probe packets at other rates

Collision-Aware Rate Adaptation [Kim et al. ‘06]

• Goal is to distinguish different causes of packet loss

• Collisions

• Channel Errors

• Proposes an adaptive RTS/CTS scheme to prevent hidden-station induced collisions

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What is context in vehicular networks?

Typical vehicular applications make use of location and neighbor information obtained using• GPS device

• Traffic/Safety application

Vehicles thus have real-time context information about the environment

Examples of context information• Distance between transmitter and receiver

• Relative speed between transmitter and receiver

Direct and predictable source of information about link quality

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Effect of collisions Scenarios:

• Base: Base case• Hidden-Node:

Collisions due to hidden node

Workload:• UDP traffic: iperf• Duration: 5 mins• TX rate - 3 Mbps• IX is out of carrier

sensing range of TX

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Effect of collisions

Sequence Number

Tra

nsm

issi

on R

ate

(Mbp

s)

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CARS Evaluation – Field Trial

Low Mobility: 25 mph

5 runs per rate algorithm

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Context Aware Rate Selection (CARS) - Approach

Use context information to “learn” the link quality


• Proactive

• Predicts large-scale path loss due to mobility Use short-term loss statistics to exploit short-

term opportunistic gainEH = f(bitrate, len)

• Reactive at very small time scale

• Handles loss due to small-scale fading

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Putting the two pieces together Issue:

• When to use EC and when to use EH? Answer:

• Weighted decision function

PER = α. EC(ctx,rate,len)+(1-α). EH(rate,len)

• Use context information (vehicle speed) to assign weights

α = max(0,min(1,speed/S))

S = 30 m/s (= 65 mph)

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CARS Algorithm

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Experiment Trajectory

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CARS Algorithm

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Effect of vehicle density

CARS: Context Aware Rate Selection for Vehicular Networks Pravin Shankar [email protected]...

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