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MIT Roofnet Robert Morris Daniel Aguayo, John Bicket, Sanjit Biswas, Douglas De Couto MIT Computer Science and Artificial Intelligence Laboratory http://pdos.lcs.mit.edu/roofnet
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MIT Roofnet Robert Morris Daniel Aguayo, John Bicket, Sanjit Biswas, Douglas De Couto MIT Computer Science and Artificial Intelligence Laboratory .

Jan 12, 2016

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Page 1: MIT Roofnet Robert Morris Daniel Aguayo, John Bicket, Sanjit Biswas, Douglas De Couto MIT Computer Science and Artificial Intelligence Laboratory .

MIT Roofnet

Robert MorrisDaniel Aguayo, John Bicket, Sanjit Biswas, Douglas De Couto

MIT Computer Science and Artificial Intelligence Laboratoryhttp://pdos.lcs.mit.edu/roofnet

Page 2: MIT Roofnet Robert Morris Daniel Aguayo, John Bicket, Sanjit Biswas, Douglas De Couto MIT Computer Science and Artificial Intelligence Laboratory .

2

Talk Outline

1. Roofnet Overview2. Link-Quality-Aware Routing (ETX)3. Roofnet Performance and Status4. Opportunistic Routing (ExOR)

Page 3: MIT Roofnet Robert Morris Daniel Aguayo, John Bicket, Sanjit Biswas, Douglas De Couto MIT Computer Science and Artificial Intelligence Laboratory .

3

The Roofnet Network54 nodes in students’ apartments

802.11 radios, antennas on roofs

Multi-hop routing to MIT’s campus net and the Internet

Gateways to DSL or campus net

2 km

Page 4: MIT Roofnet Robert Morris Daniel Aguayo, John Bicket, Sanjit Biswas, Douglas De Couto MIT Computer Science and Artificial Intelligence Laboratory .

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Existing Community Networks

• Goal: inexpensive sharing of Internet access

• Multi-hop mesh to extend reach

• Two+ directional antennas/node

• Use Internet routing protocols (OSPF)

photograph courtesy of BARWN.org

Page 5: MIT Roofnet Robert Morris Daniel Aguayo, John Bicket, Sanjit Biswas, Douglas De Couto MIT Computer Science and Artificial Intelligence Laboratory .

5

Our Key Design Choice

Omni-directional antennas:Easy to installInexpensiveCan self-configureMore choice of neighborsBut no notion of a “link”…

Goal: enable much larger mesh networks

Page 6: MIT Roofnet Robert Morris Daniel Aguayo, John Bicket, Sanjit Biswas, Douglas De Couto MIT Computer Science and Artificial Intelligence Laboratory .

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Self-Installation Kits

Computer ($340)533 Mhz PC, hard disk,CDROM

802.11b card ($155)Engenius Prism 2.5, 200mW

Software (“free”)Our networking software

Omni Antenna ($65)8dBi, 20 degree vertical

Miscellaneous ($75)Chimney Mount, LightningArrestor, Wrench, etc.

50 ft. Cable ($40)Low loss (3dB/100ft)

Takes about 45 minutes to install

Total: $685

Page 7: MIT Roofnet Robert Morris Daniel Aguayo, John Bicket, Sanjit Biswas, Douglas De Couto MIT Computer Science and Artificial Intelligence Laboratory .

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Roofnet/Internet Connectivity

Internet

Roofnet Nodes5.x.x.x(172.16.x.x)

Wired GatewaysMIT campus net, orusers’ DSL

User’s LANLiving-room nets192.168.x.x

Page 8: MIT Roofnet Robert Morris Daniel Aguayo, John Bicket, Sanjit Biswas, Douglas De Couto MIT Computer Science and Artificial Intelligence Laboratory .

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Roofnet Node Software Structure

802.11 eth

Linux TCP/IP

Click

Kernel

User-space

sshd apachedhcpd

antenna

Living roomethernet

NAT

srcrr

ETX

Page 9: MIT Roofnet Robert Morris Daniel Aguayo, John Bicket, Sanjit Biswas, Douglas De Couto MIT Computer Science and Artificial Intelligence Laboratory .

9

Talk Outline

1. Roofnet Overview2. Link-Quality-Aware Routing (ETX)3. Roofnet Performance and Status4. Opportunistic Routing (ExOR)

Page 10: MIT Roofnet Robert Morris Daniel Aguayo, John Bicket, Sanjit Biswas, Douglas De Couto MIT Computer Science and Artificial Intelligence Laboratory .

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Routing: Best Path from A to D?

E

A

B

D

C

• Internet approach: minimize the hop count• A-E-D

Radio connectivity

Page 11: MIT Roofnet Robert Morris Daniel Aguayo, John Bicket, Sanjit Biswas, Douglas De Couto MIT Computer Science and Artificial Intelligence Laboratory .

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Roofnet Throughput (Original)

Best possible routes

Minimumhop-count

routes

Page 12: MIT Roofnet Robert Morris Daniel Aguayo, John Bicket, Sanjit Biswas, Douglas De Couto MIT Computer Science and Artificial Intelligence Laboratory .

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Problem 1: Long Links Work Badly

100%

50% 50%

E

A

B

D

C100%100%

packet deliveryprobability

• Minimizing hop-count uses low-quality links• But S/N vs. BER specs suggest this isn’t important

Page 13: MIT Roofnet Robert Morris Daniel Aguayo, John Bicket, Sanjit Biswas, Douglas De Couto MIT Computer Science and Artificial Intelligence Laboratory .

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Roofnet Link Quality Distribution

Wide range of delivery ratios

Hard to say a link is either good or bad

Forward and reverse rates are often different

Page 14: MIT Roofnet Robert Morris Daniel Aguayo, John Bicket, Sanjit Biswas, Douglas De Couto MIT Computer Science and Artificial Intelligence Laboratory .

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One Link Over 24 Hours

• Cannot use Prism S/N ratio to predict link quality

Page 15: MIT Roofnet Robert Morris Daniel Aguayo, John Bicket, Sanjit Biswas, Douglas De Couto MIT Computer Science and Artificial Intelligence Laboratory .

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Problem 2: Asymmetric Links

“hello”E

A

B

D

C

datadata

acknowledgment

Page 16: MIT Roofnet Robert Morris Daniel Aguayo, John Bicket, Sanjit Biswas, Douglas De Couto MIT Computer Science and Artificial Intelligence Laboratory .

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Problem 3: Radios Share a Channel

E

A

B

D

C

• Nodes A, B, and C interfere• A-B-C-D: throughput is 1/3• A-E-D: throughput is 1/2

Page 17: MIT Roofnet Robert Morris Daniel Aguayo, John Bicket, Sanjit Biswas, Douglas De Couto MIT Computer Science and Artificial Intelligence Laboratory .

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Solution: ETX Metric

• Need to balance link quality, asymmetry, interference

• Idea: throughput 1 / (number of transmissions)– One transmission for each hop– One for each lost data packet (since 802.11 re-sends)– One for each lost acknowledgment

• ETX: “Expected Transmission Count”• Routing protocol chooses route w/ minimum

ETX

Page 18: MIT Roofnet Robert Morris Daniel Aguayo, John Bicket, Sanjit Biswas, Douglas De Couto MIT Computer Science and Artificial Intelligence Laboratory .

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Calculating Per-Link ETX

• ETX = 1 / P(delivery)• P(delivery) = P(data OK) * P(ACK OK)

• So, ETX = 1 / (df * dr)

• Each node periodically broadcasts a probe

• Neighbors measure df from probes

• Neighbors exchange df to get dr

• Problems: packet size, bit rate

Page 19: MIT Roofnet Robert Morris Daniel Aguayo, John Bicket, Sanjit Biswas, Douglas De Couto MIT Computer Science and Artificial Intelligence Laboratory .

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ETX Improves Roofnet Throughput

With ETX

WithoutETX

Best Possible

Page 20: MIT Roofnet Robert Morris Daniel Aguayo, John Bicket, Sanjit Biswas, Douglas De Couto MIT Computer Science and Artificial Intelligence Laboratory .

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SrcRR Routing Protocol

• Source routing, link-state database– DSDV isn’t stable when network is busy

• DSR-like queries to populate link-state database

• Keeping the source’s ETX values up to date:– Data packets accumulate latest per-hop ETX– Data packets carry random sample of nearby link ETXs– Ten 802.11 transmit failures: send link’s ETX back to source– One-way traffic: periodically send link’s ETX back to source

• Source only re-floods if half as good as original

Page 21: MIT Roofnet Robert Morris Daniel Aguayo, John Bicket, Sanjit Biswas, Douglas De Couto MIT Computer Science and Artificial Intelligence Laboratory .

21

Talk Outline

1. Roofnet Overview2. Link-Quality-Aware Routing (ETX)3. Roofnet Performance and Status4. Opportunistic Routing (ExOR)

Page 22: MIT Roofnet Robert Morris Daniel Aguayo, John Bicket, Sanjit Biswas, Douglas De Couto MIT Computer Science and Artificial Intelligence Laboratory .

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End-to-end TCP Throughput

• Median about 1 mbit/s, max about 3.6 mbit/s

Page 23: MIT Roofnet Robert Morris Daniel Aguayo, John Bicket, Sanjit Biswas, Douglas De Couto MIT Computer Science and Artificial Intelligence Laboratory .

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End-to-end Ping Times

100-byte packets

1000-byte packets

Page 24: MIT Roofnet Robert Morris Daniel Aguayo, John Bicket, Sanjit Biswas, Douglas De Couto MIT Computer Science and Artificial Intelligence Laboratory .

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One-way Hop Counts

100-byte packets

1000-byte packets

Page 25: MIT Roofnet Robert Morris Daniel Aguayo, John Bicket, Sanjit Biswas, Douglas De Couto MIT Computer Science and Artificial Intelligence Laboratory .

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End-to-end Ping Loss Rates

100-byte packets

1000-byte packets

Page 26: MIT Roofnet Robert Morris Daniel Aguayo, John Bicket, Sanjit Biswas, Douglas De Couto MIT Computer Science and Artificial Intelligence Laboratory .

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Path Self-Interference?

A B C D

• A sends RTS, B sends CTS, A sends long packet• C heard the CTS and won’t send• What if D forwards a packet to E?• Will that repeatedly waste A’s entire transmission?

E

range

carrier sense range?

interference range?

Page 27: MIT Roofnet Robert Morris Daniel Aguayo, John Bicket, Sanjit Biswas, Douglas De Couto MIT Computer Science and Artificial Intelligence Laboratory .

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Token-Passing

• Goal: eliminate path self-interference• Send a token back and forth along active DSR

path• Holder can forward 10 packets, then forwards

token• Increases throughput a lot:

– Avoids interference loss?– Helps 802.11 firmware stay at high bit rates?

• Problems:– Doesn’t help much if two paths are active– Lost token? Duplicate token? Idling and re-creating the token?

• This is a major focus for us right now

Page 28: MIT Roofnet Robert Morris Daniel Aguayo, John Bicket, Sanjit Biswas, Douglas De Couto MIT Computer Science and Artificial Intelligence Laboratory .

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Other Current Roofnet Problems

1. Prism 2.5 MAC carrier sense ignores many packets– Declares “carrier” if signal strength above threshold– Threshold is too high, cannot be set lower– Result: two nodes transmit at the same time and interfere

2. Fix ETX to guess which links will run at 11 mbps

3. Prism 2.5 firmware is too timid about high rates

4. Only security problem: users running viruses/DDoS

5. We’ve lost one node to lightning

Page 29: MIT Roofnet Robert Morris Daniel Aguayo, John Bicket, Sanjit Biswas, Douglas De Couto MIT Computer Science and Artificial Intelligence Laboratory .

29

Talk Outline

1. Roofnet Overview2. Link-Quality-Aware Routing (ETX)3. Roofnet Performance and Status4. Opportunistic Routing (ExOR)

Page 30: MIT Roofnet Robert Morris Daniel Aguayo, John Bicket, Sanjit Biswas, Douglas De Couto MIT Computer Science and Artificial Intelligence Laboratory .

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Routing: The Traditional View

src

A B

dst

C

• Measure all link qualities• Pick the best route• Forward data along that route’s links• This strategy is optimal for wired networks

Page 31: MIT Roofnet Robert Morris Daniel Aguayo, John Bicket, Sanjit Biswas, Douglas De Couto MIT Computer Science and Artificial Intelligence Laboratory .

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How Radios Actually Work

• Every packet is a radio broadcast…

src

A B

dst

C

3

3

3

1

1

1

2

2

4 4

4

Page 32: MIT Roofnet Robert Morris Daniel Aguayo, John Bicket, Sanjit Biswas, Douglas De Couto MIT Computer Science and Artificial Intelligence Laboratory .

32

Assumptions

1. Many receivers hear every broadcast2. Gradual distance-vs-reception tradeoff3. Receiver losses are uncorrelated

src

A B

dst

C

Page 33: MIT Roofnet Robert Morris Daniel Aguayo, John Bicket, Sanjit Biswas, Douglas De Couto MIT Computer Science and Artificial Intelligence Laboratory .

33

1. Multiple Receivers per Transmission

• Broadcast tests on rooftop network– Source sends

packets at max rate

– Receivers record delivery ratios

• Omni-directional antennas

• Multiple nodes in “radio range”

1km

S

100%75%50%25%0%

Page 34: MIT Roofnet Robert Morris Daniel Aguayo, John Bicket, Sanjit Biswas, Douglas De Couto MIT Computer Science and Artificial Intelligence Laboratory .

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2. Gradual Distance vs. Reception Tradeoff

• Wide spread of ranges, delivery ratios• Transmissions may “get lucky” and travel long distances

Distance (meters)

Deliv

ery

Rati

o

Same Source

Page 35: MIT Roofnet Robert Morris Daniel Aguayo, John Bicket, Sanjit Biswas, Douglas De Couto MIT Computer Science and Artificial Intelligence Laboratory .

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3. Receiver Losses are Uncorrelated

• Two 50% links don’t lose the same 50% of packets• Losses not due to common source of interference

Example Broadcast trace:

Receiver 1 (38%):

Receiver 2 (40%):

Receiver 3 (74%):

Receiver 4 (12%):

Page 36: MIT Roofnet Robert Morris Daniel Aguayo, John Bicket, Sanjit Biswas, Douglas De Couto MIT Computer Science and Artificial Intelligence Laboratory .

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Extremely Opportunistic Routing (ExOR) Design

Goals• Ensure only one receiver forwards the

packet• Receiver “closest” to the destination should

forward• Lost agreement messages may be common• Let’s not get eaten alive by overheads

Page 37: MIT Roofnet Robert Morris Daniel Aguayo, John Bicket, Sanjit Biswas, Douglas De Couto MIT Computer Science and Artificial Intelligence Laboratory .

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Who Received the Packet?

• Slotting prevents collisions (802.11 ACKs are synchronous)• Only 2% overhead per candidate, assuming 1500 byte frames

payload ACK

payload ACK1cand1

src dest

cand2 cand3src ACK2 ACK3

src cand1 cand2 cand3

src dest

Standard unicast 802.11 frame with ACK:

ExOR frame with slotted ACKs:

Page 38: MIT Roofnet Robert Morris Daniel Aguayo, John Bicket, Sanjit Biswas, Douglas De Couto MIT Computer Science and Artificial Intelligence Laboratory .

38

Slotted ACK Example

• Packet to be forwarded by Node C• But if ACKs are lost, causes confusion

payloadD C BA

A D

ACK

C

ACK

B

A B C D

X

Page 39: MIT Roofnet Robert Morris Daniel Aguayo, John Bicket, Sanjit Biswas, Douglas De Couto MIT Computer Science and Artificial Intelligence Laboratory .

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Agreeing on the Best Candidate

A: Sends frame with (D, C, B) as candidate set

A B C D

C: Broadcasts ACK “C” in second slot (not rx’d by D)

D: Broadcasts ACK “D” in first slot (not rx’d by C, A)

B: Broadcasts ACK “D” in third slot

Node D is now responsible for forwarding the packet

XXX

Page 40: MIT Roofnet Robert Morris Daniel Aguayo, John Bicket, Sanjit Biswas, Douglas De Couto MIT Computer Science and Artificial Intelligence Laboratory .

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Putting it all Together

• ExOR Protocol in a nutshell:– Forwarder picks candidate set (using n2 matrix of loss

rates)– Forwarder broadcasts packet– Candidates send slotted ACKs– Single candidate responsible for forwarding

• Backup Duplicate Detection

Page 41: MIT Roofnet Robert Morris Daniel Aguayo, John Bicket, Sanjit Biswas, Douglas De Couto MIT Computer Science and Artificial Intelligence Laboratory .

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Protocol Simulation

• Methodology– Use Roofnet delivery ratios and topology– Every node has full matrix of inter-node loss rates– Loss rates constant over time

• Performance Measure: Total Transmissions– Simulator cannot compute throughput properly– Total transmission count is probably inverse of

throughput

Page 42: MIT Roofnet Robert Morris Daniel Aguayo, John Bicket, Sanjit Biswas, Douglas De Couto MIT Computer Science and Artificial Intelligence Laboratory .

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ExOR Outperforms Best Static Route

• Performance of all 402 possible routes (sorted)• Gains up to 2x on longer routes

Node Pair

Num

ber

of

Tra

nsm

issi

ons

Best Static Route

ExOR (4 ACKs)

Page 43: MIT Roofnet Robert Morris Daniel Aguayo, John Bicket, Sanjit Biswas, Douglas De Couto MIT Computer Science and Artificial Intelligence Laboratory .

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Transmission Distance

• Best static route: 5.94tx, ExOR: 3.3tx• ExOR moves packets farther using a variety of links

Distance (100m bins)

Num

ber

of

Tra

nsm

issi

ons

Static Route, Hops 2,4

ExOR

Static Route, Hops 1,3

Page 44: MIT Roofnet Robert Morris Daniel Aguayo, John Bicket, Sanjit Biswas, Douglas De Couto MIT Computer Science and Artificial Intelligence Laboratory .

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Roofnet Summary

• Roofnet provides useful broadband Internet access

• Wireless breaks standard routing assumptions

• Current areas of research:• Scheduling to avoid inter-hop

interference• ExOR

• Future areas of research:• Transmit power control• Routing-aware carrier sense

http://pdos.lcs.mit.edu/roofnet

Page 45: MIT Roofnet Robert Morris Daniel Aguayo, John Bicket, Sanjit Biswas, Douglas De Couto MIT Computer Science and Artificial Intelligence Laboratory .

http://pdos.lcs.mit.edu/roofnet/

Page 46: MIT Roofnet Robert Morris Daniel Aguayo, John Bicket, Sanjit Biswas, Douglas De Couto MIT Computer Science and Artificial Intelligence Laboratory .

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Simulated ExOR Performance

Simulation of 50 nodes using loss rates from UCLA sensor networkReduces transmissions by nearly 2x over best predetermined path

Num

ber

of

Tra

nsm

issi

ons

Node Pair

Single Route

Opportunistic

Page 47: MIT Roofnet Robert Morris Daniel Aguayo, John Bicket, Sanjit Biswas, Douglas De Couto MIT Computer Science and Artificial Intelligence Laboratory .

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RSSI vs. Delivery Ratio