Transcript Slide - SyNRG

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
18
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
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
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