1. Wireless “ESP”: Using Sensors to Develop Better Network Protocols Hari Balakrishnan Lenin Ravindranath, Calvin Newport, Sam Madden M.I.T. CSAIL

2. Big Changes in Access Devices Smartphones will generate half of mobile data traffic this year Smartphones and tablets will exceed PC sales by 2011 172M smartphones sold worldwide in 2009 –25% of US phone market; 50% in two years

3. The Problem Users with “truly mobile” devices Switch between static and mobile modes (and move across different environments) Most protocols optimized for static settings Protocols that compensate for mobility are not optimal for static settings Need protocols to adapt to both settings over short periods of time Need protocols to adapt to both settings over short periods of time

4. Static v. Mobile Channel relatively stable –Protocols can average estimates –Protocols should ignore short-term variations Network topology is unchanging –Protocols can probe less frequently Channel changes fast – Channel assessments quickly outdated – Protocols should not maintain long histories Network topology changes more rapidly – Probe more often Optimal protocols are different for static and mobile settings

5. Example: Different Loss Patterns Probability that packet i+k is lost given packet i is lost 10 ms k

6. Pop Quiz Client 2 Client 1 Client 2 leaves range of AP at around t=35 seconds Client 1’s throughput drops for several seconds Why? AP

7. Today’s Protocols Attempt to adapt implicitly using measurements of packet loss, BER, SNR Difficult adaptation problem for truly mobile devices Lack explicit knowledge about the prevalent mobility mode Can we do better?

8. The Opportunity Modern mobile devices have many sensors Used by applications Ignored by protocols today Accelerometer Proximity Sensor Camera Ambient Light Sensor Microphone GPS WiFi Wireless protocols can use hints from sensors to significantly improve performance Bluetooth

9. Sensor Movement Hints Has there been movement? Heading (direction) Speed Position 50-500 Hz 3-axis force “Jerk” metric detects movement reliably within 10 ms

10. Architecture Communicate hints to neighbors Adapt to neighbor mobility, not just node’s own movement Radio

11. Hint-Aware Protocols Bit Rate Adaptation Topology Maintenance Access Point Policies –Association –Packet scheduling –Pruning Vehicular network route selection And more…

12. Bit Rate Adaptation 6 Mbps 9 Mbps 12 Mbps 18 Mbps 24 Mbps 36 Mbps 48 Mbps 54 Mbps 802.11g/a bit rates Packet encoded at a particular bit rate Finding the best bit rate to transmit a packet Depends on movement, indoors/outdoors, speed

13. RapidSample A frame-based protocol for mobile scenarios 1. When a packet fails, probability that the next few packets at the same bit rate will fail is high  Immediately reduce bit rate on packet loss 2. Coherence time of the channel is a few ms (depends on velocity)  Never retry a failed rate and any rate higher than the failed rate for this period of time

14. RapidSample 3. If the channel is not degrading, it is probably improving  After a few successes at the current bit rate, sample higher rates that have not recently failed (in the last few milliseconds)  If we are wrong about the channel improving and the sampled higher rate fails, revert to the original rate

15. RapidSample, when device is moving… Up to 75% higher throughput than SampleRate 25% better than SNR-based protocols that have been trained

16. RapidSample with vehicular mobility 28-36% higher throughput than SampleRate & RRAA 2x higher throughput than SNR-based schemes

17. But when static… Up to 30% lower throughput than other schemes

18. Putting It All Together: Hint-Aware Bitrate Adaptation Up to 40%-50% better than all other schemes RapidSample when moving SampleRate when static

19. Large difference in headings predicts short- lived link Small difference in headings predicts long-lived link Routing in Vehicular Mesh Networks Longevity of links useful – avoids expensive repairs Links between nodes (vehicles) heading in the same direction tend to last longer Use heading, position, and speed to obtain link’s connection time estimate (CTE) metric “V2V”

20. The median link duration in seconds for different intervals of heading differences in degrees (180 indicates nodes headed in opposite directions). Links with similar heading lasted 4 to 5 times longer than the median duration over all links Empirical Evaluation on Taxi Traces

21. Take-Away Message Truly mobile devices will soon be dominant –Variety of mobility modes poses problems for wireless protocols Sensors on these devices give us a new opportunity to develop network protocols Protocol architecture using sensor hints can significantly improve MAC, link, network layers