CARS- Context Aware Rate Selection for Vehicular Networks.ppt

1、CARS: Context Aware Rate Selection for Vehicular Networks,Pravin Shankar spravincs.rutgers.edu,Tamer Nadeem ,Justinian Rosca ,Liviu Iftode iftodecs.rutgers.edu,2,Vehicular networks today,Ubiquity of WiFi Cheaper, higher peak throughput compared to cellular New applications Traffic Management Urban S

2、ensing (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,Link Qual

3、ity,Bitrate,4,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: Conte

4、xt 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

5、 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 Mobi

6、lity: 25 mph Metrics Average goodput Supremum goodput (maximum among all runs for all rates),8,Existing schemes + vehicular networks: Results,Underutilization of link capacity,9,Existing schemes + vehicular networks: Analysis,Rapid change in link quality due to distance, speed, density of cars Probl

7、ems: Estimation delay Sampling requirement Collisions vs. channel errors,10,Problem 1: Estimation delay,6 Mbps,24 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

8、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 c

9、ollisions 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 cro

10、ss-layer information at the link layer to estimate link quality and perform proactive rate selection,15,CARS: reactive + proactive,Link Quality: Error Function,EH = f(bitrate, len) ReactiveShort-term loss statistics from estimation window,EC = f(distance, speed, bitrate, len)ProactivePredicted error

11、 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 rat

12、es in 802.11 Context recalculation frequency = 100 ms,17,CARS Algorithm,18,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 t

13、o provide a similar interface Extensively tested by means of vehicular field trials and simulations,19,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

14、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),21,Effect of mobility: Setup,Scenarios Stationary: Base case Cars are stationar

15、y 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 conne

16、ctivity Cars move in and out of each others range periodically - Hot-spot scenario Workload: UDP traffic from TX to RX using iperf Duration of experiment - 5 minutes,22,Effect of mobility: Results,SampleRate,CARS,Stationary,SlowMoving,FastMoving,Intermittent,Scenario,0,10,20,50,40,30,Goodput (Mbps),

17、23,Effect of mobility: Analysis,Scenario: Intermittent,Reactive vs. Proactive,24,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 10

18、0 packets per second RTS/CTS disabled Max_retransmits: 4 ns-2 with microscopic traffic generator Compared CARS with AARF and SampleRate,25,Effect of vehicle density - Results,26,Effect of vehicle density - Analysis,27,Outline,Introduction Existing solutions CARS: Context Aware Rate Selection Evaluat

19、ion 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,30,Limitations of CARS model,Other effects (non-modelled)

20、can cause packet loss, eg. multipath, shadowing, environmental effects (rain or snow), background interference Solution: Fall-back mode (=0)Enter Fall-back mode ifpredicted packet loss measured packet loss Threshold Future work: Better modeling,31,Signal strength based rate adaptation,Stationary Veh

21、icles,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 fadingOverhead due to 4-way RTS-CTS-DATA-ACK handshake Kemp08802.11 frame format (CTS) needs to be extended,32,Estimation window size,SampleRate d

22、efault 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

23、tuning,33,Capture Effect,When there is a collision between the transmitters 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 r: threshold ratio at transmission rate r Implications on r

24、ate adaptation: r 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

25、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 extens

26、ive 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 link

27、s),37,Evaluation - Mobility - Scenarios,Elapsed Time (Sec),Elapsed Time (Sec),Distance (m),Speed (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 succ

28、essful 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 Algor

29、ithms,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 adapt

30、ive 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 envir

31、onment 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

32、 traffic: iperf Duration: 5 mins TX rate - 3 Mbps IX is out of carrier sensing range of TX,250% improvement,43,Effect of collisions,Sequence Number,Transmission Rate (Mbps),44,CARS Evaluation Field Trial,Low Mobility: 25 mph,5 runs per rate algorithm,45,Context Aware Rate Selection (CARS) - Approach

33、,Use context information to “learn” the link qualityEC = 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-s

34、cale fading,46,Putting the two pieces together,Issue: When to use EC and when to use EH? Answer: Weighted decision function PER = . EC(ctx,rate,len)+(1-). EH(rate,len)Use context information (vehicle speed) to assign weights = 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,