CARS: Context Aware Rate Selection for Vehicular Networks

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 Liviu Iftode iftode@cs.rutgers.edu

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 Bitrate Link Quality

4. Rate selection problem in vehicular networks Low quality link Low quality link High quality link 54 Mbps 6 Mbps 6 Mbps Rate Selection:Select the best transmission rate based on link quality in real-time to obtain maximum throughput

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

9. Existing schemes + vehicular networks: Analysis • Rapid change in link quality due to distance, speed, density of cars • Problems: • Estimation delay • Sampling requirement • Collisions vs. channel errors

10. Problem 1: Estimation delay 54 Mbps 24 Mbps 6 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 + proactive Link Quality: Error Function • EC = f(distance, speed, bitrate, len) • Proactive • Predicted error as a function of context information • EH = f(bitrate, len) • Reactive • Short-term loss statistics from estimation window

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 derivedusing data from outdoor experiments • Simple model is sufficient because of discrete rates 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 to 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 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 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

22. Effect of mobility: Results SampleRate 50 CARS 40 30 Goodput (Mbps) 20 10 0 Stationary SlowMoving FastMoving Intermittent Scenario

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 100 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 • Evaluation • Conclusion

28. Conclusion • Existing rate adaptation algorithms under-utilize vehicular network capacity • CARS: uses context information to perform fastrate selection • Significant goodput improvement over existing algorithms

29. Backup Slides

30. 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 Moving Vehicles (25 mph) Stationary Vehicles • 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: average airtime needed to transmit one packetOverhead: average non-useful airtime needed to transmit one packet Load Overhead

36. Effect of Collisions Scenario: Stationary vehicles located close to hot-spot (to guarantee high-quality links)

37. Evaluation - Mobility - Scenarios Speed (mph) Distance (m) Elapsed Time (Sec) Elapsed Time (Sec)

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 250% improvement

43. Effect of collisions Transmission Rate (Mbps) Sequence Number

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 gain EH = 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