wireless “esp”: using sensors to develop better network protocols
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Wireless “ESP”: Using Sensors to Develop Better Network Protocols. Lenin Ravindranath Calvin Newport, Hari Balakrishnan, Sam Madden. Massachusetts Institute of Technology. Big Changes in Access Devices. 172M smartphones sold worldwide in 2009 25% of US phone market; 50% in two years - PowerPoint PPT PresentationTRANSCRIPT
Wireless “ESP”: Using Sensors to Develop Better Network Protocols
Lenin RavindranathCalvin Newport, Hari Balakrishnan, Sam Madden
Massachusetts Institute of Technology
Big Changes in Access Devices
• 172M smartphones sold worldwide in 2009– 25% of US phone market; 50% in two years
• Smartphones and tablets will exceed PC sales by 2011
• Mobile Internet growing at a tremendous pace
Big Changes in Access Devices
Dominant mode of data access in the future
“Truly Mobile” Devices• Often switch between static and mobile• Exhibit a variety of mobility modes• Move through different environments
• Protocols need to adapt to different settings – Mobility mode impacts wireless performance
The Problem
• Most protocols optimized for static settings– They perform poorly during mobility
• Protocols that compensate for mobility are not optimal in static settings
Static vs. Mobile• Channel constantly changing
– Channel assessments quickly outdated– Protocols should not maintain long
histories
• Channel relatively stable– Protocols can average estimates– Ignore short-term variations
• Topology is hardly changing– Probe for links less frequently– Compute routes over long time scales
• Topology changing rapidly– Probe for links more often– Compute routes over shorter time scales
Static vs. Mobile
Current Wireless Protocols
• Do not differentiate between mobility modes • Attempt to adapt to both settings implicitly
using measurements of packet loss, SNR, BER• Leading to suboptimal performance
• Lack of explicit knowledge about prevalent mobility mode
• Can we do better?
Proximity Sensor Camera
Ambient Light Sensor Microphone
Accelerometer
GPS
Compass
Gyro
Accelerometer
Proximity Sensor Camera
Ambient Light Sensor Microphone
GPS
Compass
Gyro
Many, many, applications…
Accelerometer
Proximity Sensor Camera
Ambient Light Sensor Microphone
GPS
Compass
Gyro
Ignored by Protocols!
Application
Transport
Network
MAC
PHY
Wireless Radio
Wireless Protocol Stack
Accelerometer
Proximity Sensor Camera
Ambient Light Sensor Microphone
GPS
Compass
Gyro
Ignored by Protocols!
Application
Transport
Network
MAC
PHY
Wireless Radio
Wireless Protocol Stack
Accelerometer
GPS
Compass
Gyro
Application
Transport
Network
MAC
PHY
Wireless Radio
Wireless Protocol Stack
GPS
Compass
Accl
Gyro
Hints
Application
Transport
Network
MAC
PHY
Wireless Radio
Wireless Protocol Stack
GPS
Compass
Accl
Gyro
Hints
• Movement• Direction• Speed
Use hints to adapt to different mobility
modes differently
Hints Protocol
Adapt to hints from neighbors
Application
Transport
Network
MAC
PHY
Wireless Radio
Wireless Protocol Stack
GPS
Compass
Accl
GyroRate AdaptationMovement
HeadingAP Association
Speed
Vehicular Routing
Walking
Application
Transport
Network
MAC
PHY
Wireless Radio
Wireless Protocol Stack
GPS
Compass
Accl
GyroRate AdaptationMovement
HeadingAP Association
Speed
Disassociation
Packet Scheduling
Power SavingPreamble
Network Monitoring
Speed
Walking
Location Vehicular Routing
Application
Transport
Network
MAC
PHY
Wireless Radio
Wireless Protocol Stack
GPS
Compass
Accl
GyroRate AdaptationMovement
HeadingAP Association
Speed
Walking
Vehicular Routing
Application
Transport
Network
MAC
PHY
Wireless Radio
Wireless Protocol Stack
GPS
Compass
Accl
GyroRate AdaptationMovement
Accl
Movement
Reliably detect movement within 100ms
Application
Transport
Network
MAC
PHY
Wireless Radio
Wireless Protocol Stack
GPS
Compass
Accl
GyroRate AdaptationMovement
Rate Adaptation in Wireless Networks
6 Mbps9 Mbps12 Mbps18 Mbps24 Mbps36 Mbps48 Mbps54 Mbps
802.11a/g bit rates
Packet encoded at a particular bit rate
Rate Adaptation:Finding the best bit rate to transmit a packet
Static vs. Mobile Performance• Static and walking traces
– Cycle through bit rates• 4 different environments
– 80 traces, 20 seconds long• Trace-driven simulation
– TCP throughput
Static Sample Rate 85 – 99%
RRAA 80 – 97%RBAR 70 – 80%
CHARM
MovingSample Rate 33 – 59%
RRAA 45 – 63%RBAR 60 – 75%
CHARM
Compare to optimal throughput
Static vs. Mobile Loss PatternsProbability that packet i+k is lost given packet i is lost
10 ms
Losses are more bursty when a node is mobilethan when a node is static
k
6 Mbps9 Mbps12 Mbps18 Mbps24 Mbps36 Mbps48 Mbps54 Mbps
6 Mbps9 Mbps12 Mbps18 Mbps24 Mbps36 Mbps48 Mbps54 Mbps
RapidSample
6 Mbps9 Mbps12 Mbps18 Mbps24 Mbps36 Mbps48 Mbps54 Mbps
1. After a single loss Reduce rate
2. History - 10 ms Don’t retry a failed rate Or any higher rate
3. Channel not degrading, probably improving After few successes, sample
higher rate not failed If wrong, come back to the
original rate
[failed – within last 10ms]
[failed – within last 10ms]
RapidSample, when device is moving
Up to 75% better throughput than SampleRate25% better than other protocols
• Trace driven (ns3)• 30 traces• 20 seconds long• TCP throughput
But when static…
Up to 30% lower throughput than other schemes
• Trace driven (ns3)• 30 traces• 20 seconds long• TCP throughput
Application
Transport
Network
Rate Adaptation
PHY
Wireless Radio
Wireless Protocol Stack
GPS
Compass
Accl
Gyro Movement
• RapidSample when movement
• SampleRate when static
Movement
Hint-Aware Rate Adaptation
Hint-Aware Rate Adaptation
40-50% better than other schemes
• Trace driven (ns3)• 10 traces• 20 seconds long• Static + Moving• TCP throughput
Application
Transport
Network
MAC
PHY
Wireless Radio
Wireless Protocol Stack
GPS
Compass
Accl
GyroRate AdaptationMovement
HeadingAP Association
Speed
Walking
Vehicular Routing
Application
Transport
Network
MAC
PHY
Wireless Radio
Wireless Protocol Stack
GPS
Compass
Accl
Gyro
HeadingAP Association
Walking
AP Association
Scan Scan ScanInfrequent scans
AP Association
Suboptimal Association
Static
Movement-Aware Association
1. Static – Stop Scanning2. Moving – Scan Periodically
3. Moving to Static – Scan once
Movement-Aware Association
On median, 40% more throughput
• Android implementation• 30 traces• Static + Moving• Throughput
Heading-Aware Association
Minimize Handoff
Training based approachHeading
Heading-Aware Association
40% median reduction in handoffs
• Android implementation• Training (30 traces)• 30 traces• # Handoffs
Application
Transport
Network
MAC
PHY
Wireless Radio
Wireless Protocol Stack
GPS
Compass
Accl
GyroRate AdaptationMovement
HeadingAP Association
Speed
Walking
Vehicular Routing
Application
Transport
Network
MAC
PHY
Wireless Radio
Wireless Protocol Stack
GPS
Compass
Accl
Gyro
Heading
Speed
Vehicular Routing
Routing in Vehicular Mesh Networks
“V2V”
Routing in Vehicular Mesh Networks• Longevity of links useful –
avoids expensive repairs
• Link between nodes heading in the same direction tend to last longer
Connection Time Estimate (CTE)• Use heading, speed and
position to predict connection duration
Routing in Vehicular Mesh Networks
Heading [0, 9) [10, 19) [20, 29) [30, 180] All Links
Link Duration (s) 66 32 15 9 16
• Empirical evaluation on taxi traces• 15 networks, 100 vehicles each
Links with similar heading lasted 4 to 5 times longer than the median duration over all links
Application
Transport
Network
MAC
PHY
Wireless Radio
Wireless Protocol Stack
GPS
Compass
Accl
GyroRate AdaptationMovement
HeadingAP Association
Speed
Walking
Vehicular Routing
Application
Transport
Network
MAC
PHY
Wireless Radio
Wireless Protocol Stack
GPS
Compass
Accl
GyroRate AdaptationMovement
HeadingAP Association
Speed
Disassociation
Packet Scheduling
Power SavingPreamble
Network Monitoring
Speed
Walking
Location Vehicular Routing
Related Work• Wireless power saving
– WakeOnWireless, Cell2Notify, Blue-Fi
• Vehicular networking – use GPS– AP association
• Mobisteer, Breadcrumbs– Rate adaptation
• CARS: Adapt rate based on speed and heading
• Very recent work– Accelerometer-assisted rate adaptation
Take-Away Message
• Truly mobile devices will soon be dominant– Variety of mobility modes poses problems for
wireless protocols• Sensors on these devices give us a new
opportunity to develop network protocols• Protocol architecture using sensor hints can
significantly improve MAC, link, network layers
Backup
Probing
How frequently should nodes probe?
Delivery Probability• ETX, ETT
Probes
Infrequent Probing
Inaccurate link estimation leads to poor throughput
Frequent Probing
Probing wastes bandwidth
Delivery Probability
Mobility causes delivery probability tofluctuate with bigger jumps
Static vs. Mobile
Mobile case requires 20x more probesto maintain acceptable estimation error
Adaptive Probing Protocol
• Adapt probing based on movement hints• When a node is static
– Probe infrequently (1 probe every 2 seconds)• When a node is mobile
– Probe frequently (10 probes per second)
Adaptive Probing
Tracks the link accurately with fewer probes
Pruning association