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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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