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CARS: Context Aware Rate Selection for Vehicular
Networks
Pravin [email protected]
Tamer [email protected]
Justinian [email protected]
Liviu [email protected]
2
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
3
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
5
Outline
Introduction Existing solutions CARS: Context Aware Rate Selection Evaluation Conclusion
6
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?
7
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)
8
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
11
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
12
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
13
Outline
Introduction Existing solutions CARS: Context Aware Rate Selection Evaluation Conclusion
14
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
15
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
16
Proactive rate selection using Ec
EC = f(distance, speed, bitrate, len)
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
Introduction Existing solutions CARS: Context Aware Rate Selection Evaluation Conclusion
20
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)
23
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
27
Outline
Introduction Existing solutions CARS: Context Aware Rate Selection Evaluation Conclusion
28
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
29
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
31
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
32
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
33
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
34
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
35
Load and Overhead Comparison
Load Overhead
Load: average airtime needed to transmit one packet
Overhead: average non-useful airtime needed to transmit one packet
36
Effect of Collisions
Scenario: Stationary vehicles located close to hot-spot (to guarantee high-quality links)
37
Evaluation - Mobility - Scenarios
Elapsed Time (Sec) Elapsed Time (Sec)
Dis
tanc
e (m
)
Spe
ed (
mph
)
38
CARS multi-rate retry chain
39
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
40
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
41
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
42
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
43
Effect of collisions
Sequence Number
Tra
nsm
issi
on R
ate
(Mbp
s)
44
CARS Evaluation – Field Trial
Low Mobility: 25 mph
5 runs per rate algorithm
45
Context Aware Rate Selection (CARS) - Approach
Use context information to “learn” the link quality
EC = f(distance, speed, bitrate, len)
• 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
46
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)
47
CARS Algorithm
48
Experiment Trajectory
49
CARS Algorithm
50
Effect of vehicle density