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Sensor Assisted Wireless Communication

This research paper explores the use of sensors in mobile phones to assess context and enable context-aware communication. By utilizing out-of-band information, such as accelerometer and acoustic data, mobile devices can adapt to diverse communication environments and fulfill user expectations. The paper presents examples and case studies to demonstrate the potential of sensor-assisted wireless communication.

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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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