CARS: Context Aware Rate Selection for Vehicular Networks Pravin Shankar [email protected]...
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Transcript of CARS: Context Aware Rate Selection for Vehicular Networks Pravin Shankar [email protected]...
![Page 1: CARS: Context Aware Rate Selection for Vehicular Networks Pravin Shankar spravin@cs.rutgers.edu Tamer Nadeem tamer.nadeem@siemens.com Justinian Rosca justinian.rosca@siemens.com.](https://reader030.fdocuments.net/reader030/viewer/2022032611/56649e8e5503460f94b91e96/html5/thumbnails/1.jpg)
CARS: Context Aware Rate Selection for Vehicular
Networks
Pravin [email protected]
Tamer [email protected]
Justinian [email protected]
Liviu [email protected]
![Page 2: CARS: Context Aware Rate Selection for Vehicular Networks Pravin Shankar spravin@cs.rutgers.edu Tamer Nadeem tamer.nadeem@siemens.com Justinian Rosca justinian.rosca@siemens.com.](https://reader030.fdocuments.net/reader030/viewer/2022032611/56649e8e5503460f94b91e96/html5/thumbnails/2.jpg)
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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
![Page 3: CARS: Context Aware Rate Selection for Vehicular Networks Pravin Shankar spravin@cs.rutgers.edu Tamer Nadeem tamer.nadeem@siemens.com Justinian Rosca justinian.rosca@siemens.com.](https://reader030.fdocuments.net/reader030/viewer/2022032611/56649e8e5503460f94b91e96/html5/thumbnails/3.jpg)
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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
![Page 4: CARS: Context Aware Rate Selection for Vehicular Networks Pravin Shankar spravin@cs.rutgers.edu Tamer Nadeem tamer.nadeem@siemens.com Justinian Rosca justinian.rosca@siemens.com.](https://reader030.fdocuments.net/reader030/viewer/2022032611/56649e8e5503460f94b91e96/html5/thumbnails/4.jpg)
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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
![Page 5: CARS: Context Aware Rate Selection for Vehicular Networks Pravin Shankar spravin@cs.rutgers.edu Tamer Nadeem tamer.nadeem@siemens.com Justinian Rosca justinian.rosca@siemens.com.](https://reader030.fdocuments.net/reader030/viewer/2022032611/56649e8e5503460f94b91e96/html5/thumbnails/5.jpg)
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Outline
Introduction Existing solutions CARS: Context Aware Rate Selection Evaluation Conclusion
![Page 6: CARS: Context Aware Rate Selection for Vehicular Networks Pravin Shankar spravin@cs.rutgers.edu Tamer Nadeem tamer.nadeem@siemens.com Justinian Rosca justinian.rosca@siemens.com.](https://reader030.fdocuments.net/reader030/viewer/2022032611/56649e8e5503460f94b91e96/html5/thumbnails/6.jpg)
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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?
![Page 7: CARS: Context Aware Rate Selection for Vehicular Networks Pravin Shankar spravin@cs.rutgers.edu Tamer Nadeem tamer.nadeem@siemens.com Justinian Rosca justinian.rosca@siemens.com.](https://reader030.fdocuments.net/reader030/viewer/2022032611/56649e8e5503460f94b91e96/html5/thumbnails/7.jpg)
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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)
![Page 8: CARS: Context Aware Rate Selection for Vehicular Networks Pravin Shankar spravin@cs.rutgers.edu Tamer Nadeem tamer.nadeem@siemens.com Justinian Rosca justinian.rosca@siemens.com.](https://reader030.fdocuments.net/reader030/viewer/2022032611/56649e8e5503460f94b91e96/html5/thumbnails/8.jpg)
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Existing schemes + vehicular networks: Results
Underutilization of link capacity
![Page 9: CARS: Context Aware Rate Selection for Vehicular Networks Pravin Shankar spravin@cs.rutgers.edu Tamer Nadeem tamer.nadeem@siemens.com Justinian Rosca justinian.rosca@siemens.com.](https://reader030.fdocuments.net/reader030/viewer/2022032611/56649e8e5503460f94b91e96/html5/thumbnails/9.jpg)
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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
![Page 10: CARS: Context Aware Rate Selection for Vehicular Networks Pravin Shankar spravin@cs.rutgers.edu Tamer Nadeem tamer.nadeem@siemens.com Justinian Rosca justinian.rosca@siemens.com.](https://reader030.fdocuments.net/reader030/viewer/2022032611/56649e8e5503460f94b91e96/html5/thumbnails/10.jpg)
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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
![Page 12: CARS: Context Aware Rate Selection for Vehicular Networks Pravin Shankar spravin@cs.rutgers.edu Tamer Nadeem tamer.nadeem@siemens.com Justinian Rosca justinian.rosca@siemens.com.](https://reader030.fdocuments.net/reader030/viewer/2022032611/56649e8e5503460f94b91e96/html5/thumbnails/12.jpg)
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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
Introduction Existing solutions CARS: Context Aware Rate Selection Evaluation Conclusion
![Page 14: CARS: Context Aware Rate Selection for Vehicular Networks Pravin Shankar spravin@cs.rutgers.edu Tamer Nadeem tamer.nadeem@siemens.com Justinian Rosca justinian.rosca@siemens.com.](https://reader030.fdocuments.net/reader030/viewer/2022032611/56649e8e5503460f94b91e96/html5/thumbnails/14.jpg)
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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
![Page 15: CARS: Context Aware Rate Selection for Vehicular Networks Pravin Shankar spravin@cs.rutgers.edu Tamer Nadeem tamer.nadeem@siemens.com Justinian Rosca justinian.rosca@siemens.com.](https://reader030.fdocuments.net/reader030/viewer/2022032611/56649e8e5503460f94b91e96/html5/thumbnails/15.jpg)
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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
![Page 16: CARS: Context Aware Rate Selection for Vehicular Networks Pravin Shankar spravin@cs.rutgers.edu Tamer Nadeem tamer.nadeem@siemens.com Justinian Rosca justinian.rosca@siemens.com.](https://reader030.fdocuments.net/reader030/viewer/2022032611/56649e8e5503460f94b91e96/html5/thumbnails/16.jpg)
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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
![Page 17: CARS: Context Aware Rate Selection for Vehicular Networks Pravin Shankar spravin@cs.rutgers.edu Tamer Nadeem tamer.nadeem@siemens.com Justinian Rosca justinian.rosca@siemens.com.](https://reader030.fdocuments.net/reader030/viewer/2022032611/56649e8e5503460f94b91e96/html5/thumbnails/17.jpg)
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CARS Algorithm
![Page 18: CARS: Context Aware Rate Selection for Vehicular Networks Pravin Shankar spravin@cs.rutgers.edu Tamer Nadeem tamer.nadeem@siemens.com Justinian Rosca justinian.rosca@siemens.com.](https://reader030.fdocuments.net/reader030/viewer/2022032611/56649e8e5503460f94b91e96/html5/thumbnails/18.jpg)
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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
![Page 19: CARS: Context Aware Rate Selection for Vehicular Networks Pravin Shankar spravin@cs.rutgers.edu Tamer Nadeem tamer.nadeem@siemens.com Justinian Rosca justinian.rosca@siemens.com.](https://reader030.fdocuments.net/reader030/viewer/2022032611/56649e8e5503460f94b91e96/html5/thumbnails/19.jpg)
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Outline
Introduction Existing solutions CARS: Context Aware Rate Selection Evaluation Conclusion
![Page 20: CARS: Context Aware Rate Selection for Vehicular Networks Pravin Shankar spravin@cs.rutgers.edu Tamer Nadeem tamer.nadeem@siemens.com Justinian Rosca justinian.rosca@siemens.com.](https://reader030.fdocuments.net/reader030/viewer/2022032611/56649e8e5503460f94b91e96/html5/thumbnails/20.jpg)
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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)
![Page 21: CARS: Context Aware Rate Selection for Vehicular Networks Pravin Shankar spravin@cs.rutgers.edu Tamer Nadeem tamer.nadeem@siemens.com Justinian Rosca justinian.rosca@siemens.com.](https://reader030.fdocuments.net/reader030/viewer/2022032611/56649e8e5503460f94b91e96/html5/thumbnails/21.jpg)
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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
![Page 22: CARS: Context Aware Rate Selection for Vehicular Networks Pravin Shankar spravin@cs.rutgers.edu Tamer Nadeem tamer.nadeem@siemens.com Justinian Rosca justinian.rosca@siemens.com.](https://reader030.fdocuments.net/reader030/viewer/2022032611/56649e8e5503460f94b91e96/html5/thumbnails/22.jpg)
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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)
![Page 23: CARS: Context Aware Rate Selection for Vehicular Networks Pravin Shankar spravin@cs.rutgers.edu Tamer Nadeem tamer.nadeem@siemens.com Justinian Rosca justinian.rosca@siemens.com.](https://reader030.fdocuments.net/reader030/viewer/2022032611/56649e8e5503460f94b91e96/html5/thumbnails/23.jpg)
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Effect of mobility: AnalysisScenario: Intermittent
Reactive vs. Proactive
![Page 24: CARS: Context Aware Rate Selection for Vehicular Networks Pravin Shankar spravin@cs.rutgers.edu Tamer Nadeem tamer.nadeem@siemens.com Justinian Rosca justinian.rosca@siemens.com.](https://reader030.fdocuments.net/reader030/viewer/2022032611/56649e8e5503460f94b91e96/html5/thumbnails/24.jpg)
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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
![Page 25: CARS: Context Aware Rate Selection for Vehicular Networks Pravin Shankar spravin@cs.rutgers.edu Tamer Nadeem tamer.nadeem@siemens.com Justinian Rosca justinian.rosca@siemens.com.](https://reader030.fdocuments.net/reader030/viewer/2022032611/56649e8e5503460f94b91e96/html5/thumbnails/25.jpg)
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Effect of vehicle density - Results
![Page 26: CARS: Context Aware Rate Selection for Vehicular Networks Pravin Shankar spravin@cs.rutgers.edu Tamer Nadeem tamer.nadeem@siemens.com Justinian Rosca justinian.rosca@siemens.com.](https://reader030.fdocuments.net/reader030/viewer/2022032611/56649e8e5503460f94b91e96/html5/thumbnails/26.jpg)
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Effect of vehicle density - Analysis
![Page 27: CARS: Context Aware Rate Selection for Vehicular Networks Pravin Shankar spravin@cs.rutgers.edu Tamer Nadeem tamer.nadeem@siemens.com Justinian Rosca justinian.rosca@siemens.com.](https://reader030.fdocuments.net/reader030/viewer/2022032611/56649e8e5503460f94b91e96/html5/thumbnails/27.jpg)
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Outline
Introduction Existing solutions CARS: Context Aware Rate Selection Evaluation Conclusion
![Page 28: CARS: Context Aware Rate Selection for Vehicular Networks Pravin Shankar spravin@cs.rutgers.edu Tamer Nadeem tamer.nadeem@siemens.com Justinian Rosca justinian.rosca@siemens.com.](https://reader030.fdocuments.net/reader030/viewer/2022032611/56649e8e5503460f94b91e96/html5/thumbnails/28.jpg)
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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
![Page 29: CARS: Context Aware Rate Selection for Vehicular Networks Pravin Shankar spravin@cs.rutgers.edu Tamer Nadeem tamer.nadeem@siemens.com Justinian Rosca justinian.rosca@siemens.com.](https://reader030.fdocuments.net/reader030/viewer/2022032611/56649e8e5503460f94b91e96/html5/thumbnails/29.jpg)
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Backup Slides
![Page 30: CARS: Context Aware Rate Selection for Vehicular Networks Pravin Shankar spravin@cs.rutgers.edu Tamer Nadeem tamer.nadeem@siemens.com Justinian Rosca justinian.rosca@siemens.com.](https://reader030.fdocuments.net/reader030/viewer/2022032611/56649e8e5503460f94b91e96/html5/thumbnails/30.jpg)
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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
![Page 31: CARS: Context Aware Rate Selection for Vehicular Networks Pravin Shankar spravin@cs.rutgers.edu Tamer Nadeem tamer.nadeem@siemens.com Justinian Rosca justinian.rosca@siemens.com.](https://reader030.fdocuments.net/reader030/viewer/2022032611/56649e8e5503460f94b91e96/html5/thumbnails/31.jpg)
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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
![Page 32: CARS: Context Aware Rate Selection for Vehicular Networks Pravin Shankar spravin@cs.rutgers.edu Tamer Nadeem tamer.nadeem@siemens.com Justinian Rosca justinian.rosca@siemens.com.](https://reader030.fdocuments.net/reader030/viewer/2022032611/56649e8e5503460f94b91e96/html5/thumbnails/32.jpg)
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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
![Page 33: CARS: Context Aware Rate Selection for Vehicular Networks Pravin Shankar spravin@cs.rutgers.edu Tamer Nadeem tamer.nadeem@siemens.com Justinian Rosca justinian.rosca@siemens.com.](https://reader030.fdocuments.net/reader030/viewer/2022032611/56649e8e5503460f94b91e96/html5/thumbnails/33.jpg)
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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
![Page 34: CARS: Context Aware Rate Selection for Vehicular Networks Pravin Shankar spravin@cs.rutgers.edu Tamer Nadeem tamer.nadeem@siemens.com Justinian Rosca justinian.rosca@siemens.com.](https://reader030.fdocuments.net/reader030/viewer/2022032611/56649e8e5503460f94b91e96/html5/thumbnails/34.jpg)
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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
![Page 35: CARS: Context Aware Rate Selection for Vehicular Networks Pravin Shankar spravin@cs.rutgers.edu Tamer Nadeem tamer.nadeem@siemens.com Justinian Rosca justinian.rosca@siemens.com.](https://reader030.fdocuments.net/reader030/viewer/2022032611/56649e8e5503460f94b91e96/html5/thumbnails/35.jpg)
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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
![Page 36: CARS: Context Aware Rate Selection for Vehicular Networks Pravin Shankar spravin@cs.rutgers.edu Tamer Nadeem tamer.nadeem@siemens.com Justinian Rosca justinian.rosca@siemens.com.](https://reader030.fdocuments.net/reader030/viewer/2022032611/56649e8e5503460f94b91e96/html5/thumbnails/36.jpg)
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Effect of Collisions
Scenario: Stationary vehicles located close to hot-spot (to guarantee high-quality links)
![Page 37: CARS: Context Aware Rate Selection for Vehicular Networks Pravin Shankar spravin@cs.rutgers.edu Tamer Nadeem tamer.nadeem@siemens.com Justinian Rosca justinian.rosca@siemens.com.](https://reader030.fdocuments.net/reader030/viewer/2022032611/56649e8e5503460f94b91e96/html5/thumbnails/37.jpg)
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Evaluation - Mobility - Scenarios
Elapsed Time (Sec) Elapsed Time (Sec)
Dis
tanc
e (m
)
Spe
ed (
mph
)
![Page 38: CARS: Context Aware Rate Selection for Vehicular Networks Pravin Shankar spravin@cs.rutgers.edu Tamer Nadeem tamer.nadeem@siemens.com Justinian Rosca justinian.rosca@siemens.com.](https://reader030.fdocuments.net/reader030/viewer/2022032611/56649e8e5503460f94b91e96/html5/thumbnails/38.jpg)
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CARS multi-rate retry chain
![Page 39: CARS: Context Aware Rate Selection for Vehicular Networks Pravin Shankar spravin@cs.rutgers.edu Tamer Nadeem tamer.nadeem@siemens.com Justinian Rosca justinian.rosca@siemens.com.](https://reader030.fdocuments.net/reader030/viewer/2022032611/56649e8e5503460f94b91e96/html5/thumbnails/39.jpg)
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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
![Page 40: CARS: Context Aware Rate Selection for Vehicular Networks Pravin Shankar spravin@cs.rutgers.edu Tamer Nadeem tamer.nadeem@siemens.com Justinian Rosca justinian.rosca@siemens.com.](https://reader030.fdocuments.net/reader030/viewer/2022032611/56649e8e5503460f94b91e96/html5/thumbnails/40.jpg)
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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
![Page 41: CARS: Context Aware Rate Selection for Vehicular Networks Pravin Shankar spravin@cs.rutgers.edu Tamer Nadeem tamer.nadeem@siemens.com Justinian Rosca justinian.rosca@siemens.com.](https://reader030.fdocuments.net/reader030/viewer/2022032611/56649e8e5503460f94b91e96/html5/thumbnails/41.jpg)
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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
![Page 42: CARS: Context Aware Rate Selection for Vehicular Networks Pravin Shankar spravin@cs.rutgers.edu Tamer Nadeem tamer.nadeem@siemens.com Justinian Rosca justinian.rosca@siemens.com.](https://reader030.fdocuments.net/reader030/viewer/2022032611/56649e8e5503460f94b91e96/html5/thumbnails/42.jpg)
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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
![Page 43: CARS: Context Aware Rate Selection for Vehicular Networks Pravin Shankar spravin@cs.rutgers.edu Tamer Nadeem tamer.nadeem@siemens.com Justinian Rosca justinian.rosca@siemens.com.](https://reader030.fdocuments.net/reader030/viewer/2022032611/56649e8e5503460f94b91e96/html5/thumbnails/43.jpg)
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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
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
![Page 46: CARS: Context Aware Rate Selection for Vehicular Networks Pravin Shankar spravin@cs.rutgers.edu Tamer Nadeem tamer.nadeem@siemens.com Justinian Rosca justinian.rosca@siemens.com.](https://reader030.fdocuments.net/reader030/viewer/2022032611/56649e8e5503460f94b91e96/html5/thumbnails/46.jpg)
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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)
![Page 47: CARS: Context Aware Rate Selection for Vehicular Networks Pravin Shankar spravin@cs.rutgers.edu Tamer Nadeem tamer.nadeem@siemens.com Justinian Rosca justinian.rosca@siemens.com.](https://reader030.fdocuments.net/reader030/viewer/2022032611/56649e8e5503460f94b91e96/html5/thumbnails/47.jpg)
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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