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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 … - PowerPoint PPT Presentation
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Sensor Assisted Wireless Communication
Naveen Santhapuri, Justin Manweiler, Souvik Sen, Xuan Bao, Romit Roy Choudhury
Srihari Nelakuditi
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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
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Different from Laptops
These devices are always-on, andalways-with their human owners
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Wired Wireless
WirelessMobile
Wireless
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Mobile Wireless brings Challenges
HomeOffice
Humans move through various environments Devices subject to diverse communication contexts
Humans move through various environments Devices subject to diverse communication contexts
Office
Stationary High Mobility Low Mobility Stationary
WiFi/Bluetooth 3G/EDGE Disconnected 4G/WiFi WiFi/3G/4G
Home
Mobile Wireless brings Challenges
Great Expectations
Users expect devices to adapt to the context
Office
Stationary High Mobility Low Mobility Stationary
WiFi/Bluetooth 3G/EDGE Disconnected 4G/WiFi WiFi/3G/4G
Home
Great Expectations
Users expect devices to adapt to the context
Office
Stationary High Mobility Low Mobility Stationary
WiFi/Bluetooth 3G/EDGE Disconnected 4G/WiFi WiFi/3G/4G
Home
Example1: The phone should turn itself off in the subway, turn back on at stations or at destination.
Example1: The phone should turn itself off in the subway, turn back on at stations or at destination.
Example1: The phone will turn itself off in the subway, turn back on at stations or at destination.
Example1: The phone will turn itself off in the subway, turn back on at stations or at destination.
Great Expectations
Users expect devices to adapt to the context
Office
Stationary High Mobility Low Mobility Stationary
WiFi/Bluetooth 3G/EDGE Disconnected 4G/WiFi WiFi/3G/4G
Home
Example2: The phone should discern the RF environment,and jump to the optimal frequency channel
Example2: The phone should discern the RF environment,and jump to the optimal frequency channel
In General
Phones expected to perform context-aware communication …
much different from traditional laptop computing
Phones expected to perform context-aware communication …
much different from traditional laptop computing
Context-Aware Communication
Innovative research on context-awareness Handoffs, adaptive duty cycling, interference
detection
Innovative research on context-awareness Handoffs, adaptive duty cycling, interference
detection
However, most approaches are in-bandi.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
Context-Aware Communication
Our Proposal
Break away from in-band assessment
Mobile phones equipped with multiple sensorsSensors offer multi-dimensional, out of band (OOB) information
Exploit OOB information to assess contextMake communication context-aware
Examples
Accelerometer assistance Detect user inside subway … turn off phone Identify nature of movement … adapt bitrate Detect user driving … block a phone call
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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.
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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
Examples
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Observe that …
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Sensor assisted apps Already in use
E.g., Display off when talking on phone (proximity sensor)
E.g., Ambience-aware ringtones
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
Observe that …
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Sensor Assisted Wireless Communication
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Why Out-of-Band?
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Diversity improves context identification(at least one fingerprint easy to detect)
Diversity improves context identification(at least one fingerprint easy to detect)
Contexts have diverse fingerprints across multiple sensing dimensionsContexts have diverse fingerprints across multiple sensing dimensions
Sound Motion Light
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Wireless
In-band sensing unable to leverage this diversity
Case Study 1:Microwave Oven Aware Channel Switching
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
Problem
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Channel 6
Channel 6
Microwave “hum” is out of band signal Detect this acoustic signature Switch WiFi to different channel
When hum stops Switch back to original channel
Acoustic Fingerprint: “Hum”
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Sound
Channel 6
Channel 11
Signature Detection
Microwave’s distinct acoustic signature in frequency domain
Microwave’s distinct acoustic signature in frequency domain
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Throughput
Throughput comparison across 802.11b/g channels with and
without Microwave
Throughput comparison across 802.11b/g channels with and
without Microwave
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Case Study 2:Activity Aware Call Admission
Phone accelerometer detects user is driving Discriminate between driver and passenger
Opportunity
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Initiatecall
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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
Opportunity
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Initiatecall
User Driving … Continue?
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Accelerometer Signatures
Accelerometer signatures different for driver and passenger
Accelerometer signatures different for driver and passenger
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Case Study 3:Behavior Aware 3G Offloading
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
Problem and Opportunity
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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
Dwell Time Prediction
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Drive Through(3 minutes)
Grocery Shop(15 minutes)
Studying(60+ minutes)
3G Offload
112 MB 3G data saved per hour2 Behavior Aware AP = 1 new 3G user
112 MB 3G data saved per hour2 Behavior Aware AP = 1 new 3G user
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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
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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)
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Out-of-Band in Real Life …
Out-of-band information provides useful hintsOut-of-band information provides useful hints
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Please stay tuned for more athttp://synrg.ee.duke.edu
Thank You
Thank You!
Questions?
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40
Continuous “in-band” context assessment incur overheadsContinuous “in-band” context assessment incur overheads
Today’s systems optimize for the common case …Today’s systems optimize for the common case …
Sacrifices performance under atypical contextsSacrifices performance under atypical contexts
In the perspective ofrelated work …
SAWC Classification
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Implicit Explicit
In-bandWireless
Out-of-band
Radio fingerprinting: Mobicom08
RTS/CTS for reducing collisions
RTS
CTS
(Backoff)
GPS-assisted rate control: ICNP08Sensor assisted WiFi Scanning
Don’t Scan
Source Data
Context-Awareness
RF context assessment Remains an elusive research problem
Several approaches use in-band analysisi.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
Mobility Demands Agility
For example, from home to office A user transitions through numerous environments
Home
Stationary High Mobility Low Mobility Stationary
Office
Mobility Demands Agility
For example, from home to office A user transitions through numerous environments Devices subject to various communication contexts
Office
Stationary High Mobility Low Mobility Stationary
WiFi/Bluetooth 3G/EDGE Disconnected 4G/WiFi WiFi/3G/4G
Home