sense & sensibility for wireless networks
TRANSCRIPT
Sense & Sensibility for Wireless Networks
Hari Balakrishnan Department of EECS and CSAIL
M.I.T. nms.csail.mit.edu/~hari
The Era of Truly Mobile Computing
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Inte
rnet Us
ers
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ons
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Desktop Users
Source: Morgan Stanley Research
5B mobile phones in the world Exceeds #people with shoes, toilets, electricity! 1B with “broadband” capability
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Gigabytes/user/m
onth
Average Demand Per Cellular User
Projection: Rysavy Research, 2010
Survey by TeleNav
1 hr netflix
1 hr HD video
Average wired broadband @home: US: 18 GB/mo (AT&T) UK: 17-19 GB/month (2011)
Mobile Apps All Around Us
Smart Homes
Public Safety
Entertainment & Social Lives
Healthcare Transportation
Sensor-rich systems
Apps demand robust, high-performance wireless networks Difficult as load and node density increase
Difficult as channel conditions change with time
Truly Mobile Devices
• Often switch between static and mobile • Exhibit a variety of mobility modes • Move through different environments
Mobility à Time-varying Channels & Topology
Slow walk
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Mea
sure
d S
NR
(dB
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Time (s)
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Shaded area detail
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Bit
err
or
rate
Time (s)
How to determine the best bit rate to transmit a packet?
802.11n: 32 possible ���[constell’n X code X stream] ���
combinations
symbol sets (= constellation)
Example: 802.11a/g���8 bit rates: 6, 9, 12, 18, 24, 36, 48, 54 Mbps
Two possible codes���per constellation���
(Rate 1/2 or 3/4 conv. codes)
Bit Rate Selection
• Measure wireless channel – Frame loss rate at different bit rates ���
[SampleRate, RRAA, ARF, …]
– Signal-to-noise ratio (SNR) ���[RBAR, CHARM, …, 3GPP]
• Determine best bit rate given measurement
• Problem: Measurements quickly outdated over time-varying channels
Accelerometer
Proximity Sensor Camera
GPS
Compass
Gyro
Many, many, applications…
The Big Idea: Sensors on Devices
Ambient Light Sensor Microphone
Applica<on
Transport
Network
Link/MAC
PHY
Radio
Protocol Stack
Accelerometer
Proximity Sensor Camera
GPS
Compass
Gyro
Largely ignored by protocols!
The Opportunity: Sensors
Ambient Light Sensor Microphone
Applica<on
Transport
Network
MAC
PHY
Radio
Protocol Stack
GPS
Compass
Accl
Gyro
• Position • Movement • Direction • Speed
Use hints to adapt to different mobility modes differently
Integrate Sensor Hints into the Network Stack
Mobility hint protocol Adapt to hints from neighbors
Sensor Hints
Applica<on
Transport
Network
MAC
PHY
Radio
Protocol Stack
GPS
Compass
Accl
Gyro Rate Selec<on Movement
Heading AP Associa<on
Speed
Vehicular Rou<ng
Walking
Integrate Sensor Hints into the Network Stack
L. Ravindranath, HB, C. Newport, S. Madden, Improving wireless network performance using sensor hints, NSDI 2011
Applica<on
Transport
Network
MAC
PHY
Radio
Protocol Stack
GPS
Compass
Accl
Gyro Rate Selec<on Movement
Heading AP Associa<on
Speed
Topo maintenance
Packet Scheduling
Power Saving Cyclic Prefix
Network Monitoring
Speed
Walking
Loca<on Vehicular Rou<ng
Integrate Sensor Hints into the Network Stack
L. Ravindranath, HB, C. Newport, S. Madden, Improving wireless network performance using sensor hints, NSDI 2011
3-‐axis accel Movement
Reliably detect movement within 10-100 ms on ���
commodity Android devices
Is the device is static or moving?
Extracting Hints: ���Movement Example
Probability that packet i is lost given packet i-‐k is lost
Losses more bursty when node is moving than when static. RapidSample: protocol to adapt while moving (small history)
Static v. Mobile Loss Performance
Applica<on
Transport
Network
Rate Selec<on
PHY
Wireless Radio
Wireless Protocol Stack
Accl
Movement
• RapidSample when moving���Quick response to channel changes
• SampleRate when static ���Slower response to channel changes
Movement
Hint-Aware Rate Selection
Use two different protocols, switching between them using sensor hints
Rate selection: 40-60% faster than previous protocols
Hint-‐aware Experimental Results
Sensor-Augmented Protocols: Results
Access point selection 40% fewer handoff disruptions than current scheme
Vehicular network routing 2-5x longer connection time
Mobile topology maintenance 20x reduction in probe bandwidth for
Scan Scan Infrequent scans (because it wastes channel)
Picking a Good Access Point (AP)
Walking-Aware Association
1. Static – Stop Scanning 2. Walking – Scan Periodically
3. Walking to Static – Scan once
How to maximize mobile throughput?
Heading-Aware Association
Heading
How to minimize handoffs (disruptions)?
Use direc)on of movement to pick AP Ø Background Android applica<on Ø Training: WiFi scans + Heading hint Ø Query the model with current AP
and heading hint
Hint-Aware AP Selection
30% higher median throughput
• Android implementation • 30 traces; Static + Moving
40% median reduction in handoffs
• Throughput • # handoffs
• Energy – Accelerometer, compass are cheap but GPS is not – Triggered sensing + adapt sample rates
• Calibration across device types – Movement hint required no calibration – Walking hint required tuning
• Why not network measurement (SNR/RSSI)? – Variable and hard to measure reliably
• Privacy
Potential Concerns
Applica<on
Transport
Network
MAC
PHY
Radio
Protocol Stack
GPS
Compass
Accl
Gyro Rate Selec<on Movement
Heading AP Associa<on
Speed
Topo maintenance
Packet Scheduling
Power Saving Cyclic Prefix
Network Monitoring
Speed
Walking
Loca<on Vehicular Rou<ng
Sensor Hints���External Information in Network Protocols
L. Ravindranath, HB, C. Newport, S. Madden, Improving wireless network performance using sensor hints, NSDI 2011
• Truly mobile devices will soon be dominant – Variety of mobility modes poses problems
• Sensors on mobile devices give us a great opportunity to develop new protocols – On-going: apply activity abstraction more widely
• Sensor-augmented protocol architecture – Senseless to design mobile networks any other
way! Acks: Lenin Ravindranath, Sam Madden, Cal Newport (collaborators), NSF, and Om Malik for the title idea (http://gigaom.com/2011/04/15/why-apps-need-some-sense-and-sensibility/)
Embed Sense into Wireless Protocols!