Slide - SyNRG
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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
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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
Different from Laptops
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These devices are always-on, and
always-with their human owners
Wireless
Wired
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Wireless
Mobile
Wireless
Mobile Wireless brings Challenges
Humans move through various environments
Devices subject to diverse communication contexts
Home
Office
Mobile Wireless brings Challenges
Humans move through various environments
Devices subject to diverse communication contexts
WiFi/Bluetooth
3G/EDGE
Disconnected
WiFi/3G/4G
Office
Home
Stationary
4G/WiFi
High Mobility
Low Mobility Stationary
Great Expectations
Users expect devices to adapt to the context
WiFi/Bluetooth
3G/EDGE
Disconnected
WiFi/3G/4G
Office
Home
Stationary
4G/WiFi
High Mobility
Low Mobility Stationary
Great Expectations
Users expect devices to adapt to the context
Example1: The phone should turn itself off in
the subway, turn back on at stations or at destination.
WiFi/Bluetooth
3G/EDGE
Disconnected
WiFi/3G/4G
Office
Home
Stationary
4G/WiFi
High Mobility
Low Mobility Stationary
Great Expectations
Users expect devices to adapt to the context
Example1: The phone will turn itself off in
Example2:
should
discernorthe
environment,
the subway,The
turnphone
back on
at stations
at RF
destination.
and jump to the optimal frequency channel
WiFi/Bluetooth
3G/EDGE
Disconnected
WiFi/3G/4G
Office
Home
Stationary
4G/WiFi
High Mobility
Low Mobility Stationary
In General
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
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
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
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
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Acoustic assistance
Microwave oven “hums” nearby … switch WiFi channel
Hear ambulance siren … escape from WhiteSpace freq.
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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
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Acoustic assistance
Microwave oven “hums” nearby … switch WiFi channel
Hear ambulance siren … escape from WhiteSpace freq.
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Multi-dimensional assistance
Sense which users will leave WiFi hotspot sooner …
priotitize WiFi traffic to save 3G
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Observe that …
Sensor assisted apps
Already in use
E.g., Display off when talking
on phone (proximity sensor)
E.g., Ambience-aware ringtones
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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
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Sensor Assisted Wireless Communication
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Why Out-of-Band?
Contexts have diverse fingerprints
across multiple sensing dimensions
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Wireless
Sound
Motion
Light
Diversity improves context identification
(at least one fingerprint easy to detect)
In-band sensing unable to leverage this diversity
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Case Study 1:
Microwave Oven Aware Channel Switching
Problem
Microwave ovens operate at 2.4GHz
Interferes with WiFi receivers
WiFi transmitters carrier sense and don’t transmit
Throughput degrades
Channel 6
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Channel 6
In-band detection difficult
Microwave interference similar to WiFi
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Acoustic Fingerprint: “Hum”
Microwave “hum” is out of band signal
Detect this acoustic signature
Switch WiFi to different channel
Channel 6
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Sound
Channel 11
When hum stops
Switch back to original channel
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Signature Detection
Microwave’s distinct acoustic signature in frequency domain
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Throughput
Throughput comparison across
802.11b/g channels with and
without Microwave
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Case Study 2:
Activity Aware Call Admission
Opportunity
Phone accelerometer detects user is driving
Discriminate between driver and passenger
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Initiate
call
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Opportunity
Phone accelerometer detects user is driving
Discriminate between driver and passenger
User Driving
… Continue?
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Initiate
call
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Phone blocks call
Checks if call can be postponed for later
Can be generalized to other activities
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Accelerometer Signatures
Accelerometer signatures different for driver and passenger
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Case Study 3:
Behavior Aware 3G Offloading
Problem and Opportunity
3G networks overloaded
Sense user behavior via multiple sensors
Exploit WiFi hotspots to offload 3G load
Predict which users likely to exit the hotspot soon
Prioritize WiFi for soon to leave users
More WiFi traffic … less carry-over to 3G
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Dwell Time Prediction
Phones sense user behavior
AP runs machine learning algorithm
Summarizes sensor readings to AP
Classifies behavior into “dwell time” buckets
AP shapes traffic
Shorter dwell time … higher priority
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Drive Through
(3 minutes)
a dna ™emiTk ciuQ
ros se rpmo ced
.erut cip sih t ee s ot dedeen era
Grocery Shop
(15 minutes)
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Studying
(60+ minutes)
3G Offload
112 MB 3G data saved per hour
2 Behavior Aware AP = 1 new 3G user
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Exercise Caution
Count sensing overheads
Out-of-band should provide timely context
Sensing is not free
However, sensors may be on … cost may amortize
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
In band context-awareness may be feasible
But often expensive, inefficient
Mobile devices equipped with many sensors
Need to be agile to changing contexts
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 hints
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Please stay tuned for more at
http://synrg.ee.duke.edu
Thank You
Thank You!
Questions?
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Continuous “in-band” context assessment incur overheads
Today’s systems optimize for the common case …
Sacrifices performance under atypical contexts
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In the perspective of
related work …
SAWC Classification
Source
Data
Implicit
In-band
Wireless
Explicit
RTS
(Backoff)
CTS
Radio fingerprinting: Mobicom08
RTS/CTS for reducing collisions
Don’t Scan
Out-ofband
Sensor assisted WiFi Scanning
GPS-assisted rate control: ICNP08
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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
Mobility Demands Agility
For example, from home to office
A user transitions through numerous environments
Office
Home
Stationary
High Mobility
Low Mobility Stationary
Mobility Demands Agility
For example, from home to office
A user transitions through numerous environments
Devices subject to various communication contexts
WiFi/Bluetooth
3G/EDGE
Disconnected
WiFi/3G/4G
Office
Home
Stationary
4G/WiFi
High Mobility
Low Mobility Stationary