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Sensor Assisted Wireless Communication

Sensor Assisted Wireless Communication. Naveen Santhapuri, Justin Manweiler, Souvik Sen, Xuan Bao, Romit Roy Choudhury Srihari Nelakuditi. Context. 4.2 billion mobile phones, 50 million iPhones, 1 million iPads in 28 days, Androids, Slates, etc …

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Sensor Assisted Wireless Communication

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  1. Sensor Assisted Wireless Communication Naveen Santhapuri, Justin Manweiler, Souvik Sen, Xuan Bao, Romit Roy Choudhury Srihari Nelakuditi

  2. Context 4.2 billion mobile phones, 50 million iPhones, 1 million iPads in 28 days, Androids, Slates, etc … Projection: 39x increase in mobile traffic by 2015

  3. Different from Laptops These devices are always-on, and always-with their human owners

  4. Wireless Wired Mobile Wireless Wireless

  5. Mobile Wireless brings Challenges • Humans move through various environments • Devices subject to diverse communication contexts Office Home

  6. Mobile Wireless brings Challenges Disconnected 3G/EDGE 4G/WiFi WiFi/Bluetooth WiFi/3G/4G Office Home High Mobility Stationary Low Mobility Stationary • Humans move through various environments • Devices subject to diverse communication contexts

  7. Great Expectations Disconnected 3G/EDGE 4G/WiFi WiFi/Bluetooth WiFi/3G/4G Office Home High Mobility Stationary Low Mobility Stationary • Users expect devices to adapt to the context

  8. Great Expectations Example1: The phone should turn itself off in the subway, turn back on at stations or at destination. Disconnected 3G/EDGE 4G/WiFi WiFi/Bluetooth WiFi/3G/4G Office Home High Mobility Stationary Low Mobility Stationary • Users expect devices to adapt to the context

  9. Great Expectations Example1: The phone will turn itself off in the subway, turn back on at stations or at destination. Example2: The phone should discern the RF environment, and jump to the optimal frequency channel Disconnected 3G/EDGE 4G/WiFi WiFi/Bluetooth WiFi/3G/4G Office Home High Mobility Stationary Low Mobility Stationary • Users expect devices to adapt to the context

  10. In General Phones expected to perform context-aware communication … much different from traditional laptop computing

  11. Context-Aware Communication • Innovative research on context-awareness • Handoffs, adaptive duty cycling, interference detection

  12. Context-Aware Communication • Innovative research on context-awareness • Handoffs, adaptive duty cycling, interference detection • However, most approaches are in-band i.e., RF signals used to assess RF context • In band methods often restrictive • When will train come to station (for WiFi connection) • Continuous WiFi probing requires high energy • Difficult to detect primary user in WhiteSpace system • No easy RF signature … hard to quickly switch channels • Even difficult to discriminate collision/fading in band

  13. Our Proposal Break away from in-band assessment Mobile phones equipped with multiple sensors Sensors offer multi-dimensional, out of band (OOB) information Exploit OOB information to assess context Make communication context-aware

  14. Examples • Accelerometer assistance • Detect user inside subway … turn off phone • Identify nature of movement … adapt bitrate • Detect user driving … block a phone call

  15. Examples • Accelerometer assistance • Detect user inside subway … turn off phone • Identify nature of movement … adapt bitrate • Detect user driving … block a phone call • Acoustic assistance • Microwave oven “hums” nearby … switch WiFi channel • Hear ambulance siren … escape from WhiteSpace freq.

  16. Examples • Accelerometer assistance • Detect user inside subway … turn off phone • Identify nature of movement … adapt bitrate • Detect user driving … block a phone call • Acoustic assistance • Microwave oven “hums” nearby … switch WiFi channel • Hear ambulance siren … escape from WhiteSpace freq. • Multi-dimensional assistance • Sense which users will leave WiFi hotspot sooner … priotitize WiFi traffic to save 3G

  17. Observe that … • Sensor assisted apps • Already in use E.g., Display off when talking on phone (proximity sensor) E.g., Ambience-aware ringtones 17

  18. Observe that … • Sensor assisted apps • Already in use E.g., Display off when talking on phone (proximity sensor) E.g., Ambience-aware ringtones • Sensor-assisted communications • Relatively unexplored 18

  19. Sensor Assisted Wireless Communication 19

  20. Why Out-of-Band? Contexts have diverse fingerprints across multiple sensing dimensions Sound Wireless Motion Light Diversity improves context identification (at least one fingerprint easy to detect) In-band sensing unable to leverage this diversity 20

  21. Case Study 1: Microwave Oven Aware Channel Switching

  22. Problem • Microwave ovens operate at 2.4GHz • Interferes with WiFi receivers • WiFi transmitters carrier sense and don’t transmit • Throughput degrades • In-band detection difficult • Microwave interference similar to WiFi Channel 6 Channel 6 22

  23. Acoustic Fingerprint: “Hum” • Microwave “hum” is out of band signal • Detect this acoustic signature • Switch WiFi to different channel • When hum stops • Switch back to original channel Channel 6 Channel 11 Sound 23

  24. Signature Detection Microwave’s distinct acoustic signature in frequency domain 24

  25. Throughput Throughput comparison across 802.11b/g channels with and without Microwave 25

  26. Case Study 2: Activity Aware Call Admission

  27. Opportunity • Phone accelerometer detects user is driving • Discriminate between driver and passenger Initiate call 27

  28. Opportunity • Phone accelerometer detects user is driving • Discriminate between driver and passenger • Phone blocks call • Checks if call can be postponed for later • Can be generalized to other activities User Driving … Continue? Initiate call 28

  29. Accelerometer Signatures Accelerometer signatures different for driver and passenger 29

  30. Case Study 3: Behavior Aware 3G Offloading

  31. Problem and Opportunity • 3G networks overloaded • Exploit WiFi hotspots to offload 3G load • Sense user behavior via multiple sensors • Predict which users likely to exit the hotspot soon • Prioritize WiFi for soon to leave users • More WiFi traffic … less carry-over to 3G 31

  32. Dwell Time Prediction • Phones sense user behavior • Summarizes sensor readings to AP • AP runs machine learning algorithm • Classifies behavior into “dwell time” buckets • AP shapes traffic • Shorter dwell time … higher priority 32

  33. Studying (60+ minutes) Drive Through (3 minutes) Grocery Shop (15 minutes)

  34. 3G Offload 112 MB 3G data saved per hour 2 Behavior Aware AP = 1 new 3G user 34

  35. Exercise Caution • Count sensing overheads • Sensing is not free • However, sensors may be on … cost may amortize • Out-of-band should provide timely context • Suitable in our case studies • Inadequate for some applications • Treat SAWC as hint rather than solution • Complementary to in-band sensing 35

  36. Summary • Pervasive communication systems • Need to be agile to changing contexts • In band context-awareness may be feasible • But often expensive, inefficient • Mobile devices equipped with many sensors • Together enable a “broader” view • We propose to leverage this opportunity via • Sensor Assisted Wireless Communications (SAWC) 36

  37. Out-of-Band in Real Life … Out-of-band information provides useful hints 37

  38. Please stay tuned for more at http://synrg.ee.duke.edu Thank You

  39. Thank You!Questions? 39

  40. Continuous “in-band” context assessment incur overheads Today’s systems optimize for the common case … Sacrifices performance under atypical contexts 40

  41. In the perspective of related work …

  42. SAWC Classification RTS (Backoff) CTS RTS/CTS for reducing collisions Source Implicit Explicit Data In-band Wireless Radio fingerprinting: Mobicom08 Don’t Scan Out-of- band GPS-assisted rate control: ICNP08 Sensor assisted WiFi Scanning 42

  43. Context-Awareness • RF context assessment • Remains an elusive research problem • Several approaches use in-band analysis i.e., RF signals used to assess RF context • For example • Difficult to discriminate between collision/fading • No easy RF signature • When will train come to station (for WiFi connection) • Continuous RF scanning requires high evergy • Download more from WiFi before moving out of range • Hard to tell (using RF) how soon user will disconnect

  44. Mobility Demands Agility Office Home High Mobility Stationary Low Mobility Stationary • For example, from home to office • A user transitions through numerous environments

  45. Mobility Demands Agility Disconnected 3G/EDGE 4G/WiFi WiFi/Bluetooth WiFi/3G/4G Office Home High Mobility Stationary Low Mobility Stationary • For example, from home to office • A user transitions through numerous environments • Devices subject to various communication contexts

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