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ExOR: Opportunistic Multi-Hop Routing for Wireless Networks. Sanjit Biswas and Robert Morris M.I.T. Computer Science and Artificial Intelligence Laboratory. Presented by Deepak Bastakoty. With slides from : Sanjit Biswas, Robert Morris, (MIT) Saurabh Gupta (WPI) Yao Zhao (Northwestern).

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slide1

ExOR: Opportunistic Multi-Hop Routing for Wireless Networks

Sanjit Biswas and Robert Morris

M.I.T. Computer Science and Artificial Intelligence Laboratory

Presented by Deepak Bastakoty

With slides from :

Sanjit Biswas, Robert Morris, (MIT)

Saurabh Gupta (WPI)

Yao Zhao (Northwestern)

multi hop wireless networks
Multi-Hop Wireless Networks
  • Dense 802.11b-based mesh, all sorts of loss rates
  • Goal is efficiency and high-throughput

2km

Gateway

Gateways

2km

the traditional view
The Traditional View

packet

packet

packet

  • Identify a route, forward over links
  • Use link level retransmissions

A

B

src

dst

packet

packet

C

how radios actually work
How Radios Actually Work
  • Every packet is broadcast

A

B

src

dst

1

2

2

2

3

3

3

4

4

4

5

5

5

6

6

6

1

1

2

3

4

5

6

1

C

No such thing as a link

exor exploiting the insight
ExOR: Exploiting the Insight
  • Figure out which nodes rx’d broadcast
  • Node closest to destination forwards

A

B

src

dst

packet

packet

packet

packet

packet

C

packet

packet

packet

packet

exor exploiting the insight6
ExOR: Exploiting the Insight

packet

packet

packet

packet

  • Figure out which nodes rx’d broadcast
  • Node closest to destination forwards

A

B

src

dst

packet

packet

packet

packet

packet

C

exor s assumptions
ExOR’s Assumptions
  • Many receivers hear every broadcast
  • Gradual distance-vs-reception tradeoff
  • Receiver losses are uncorrelated

A

B

src

dst

C

1 multiple receivers per transmission
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”

100%

1km

75%

50%

25%

0%

S

2 gradual distance vs reception tradeoff
2. Gradual Distance vs. Reception Tradeoff
  • Wide spread of ranges, delivery ratios
  • Transmissions may “get lucky” and travel long distances

Same Source

Delivery Ratio

Distance (meters)

3 receiver losses are uncorrelated
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%):

extremely opportunistic routing exor design goals
Extremely Opportunistic Routing (ExOR) Design Goals
  • Ensure only one receiver forwards the packet
  • The receiver “closest” to the destination should forward
  • Lost agreement messages may be common
  • Let’s not get eaten alive by overheads
slide12

How ExOR might provide more throughput

N5

S

N1

N2

N3

N4

N6

N7

N8

D

Traditional Path

  • Traditional routing must compromise between hops to choose ones that are long enough to make good progress but short enough for low loss rate
  • With ExOR each transmission may have more independent chances of being received and forwarded
  • It takes advantage of transmissions that reach unexpectedly far, or fall unexpectedly short
slide13

How ExOR might provide more throughput (contd..)

N1

25%

100%

N2

25%

100%

src

dst

100%

25%

N3

100%

25%

N4

  • Traditional routing: 1/0.25 + 1 = 5 transmissions
  • ExOR: 1/(1 – (1 – 0.25)4) + 1 = 2.5 transmissions
  • Assumes independent losses
slide14

ExOR: Protocol

  • How often should ExOR run?
    • Per packet is expensive
    • Use batches
  • Who should participate in the forwarding?
    • Too many participants cause large overhead
  • When should each participant forward?
    • Avoid simultaneous transmission
  • What should each participant forward?
    • Avoid duplicate transmission
  • How and When does the process complete?
    • Identify the convergence of the algorithm

Jump Ahead

slide15

Who should participate?

  • The source chooses the participants (forwarder list) using ETX-like metric
    • Only considers forward delivery rate
  • The source runs a simulation and selects only the nodes which transmit at least 10% of the total transmission in a batch
    • A background process collects ETX information via periodic link-state flooding
slide16

When should each participant forward?

  • Forwarders are prioritized by ETX-like metric to the destination
  • Receiving nodes buffer successfully received packets till the end of the batch
  • The highest priority forwarder transmits from its buffer when the batch ends
    • These transmissions are called the node’s fragment of the batch
  • The remaining forwarders transmit in prioritized order
  • Question: How does each forwarder know it is its turn to transmit
    • Assume other higher priority nodes send for five packet durations if not hearing anything from them
slide17

What should each participant forward?

  • Packets it receives yet not received by higher priority forwarders
  • Each packet includes a copy of the sender’s batch map, containing the sender’s best guess of the highest priority node to have received each packet in the batch
  • Question: How does a node know the set of packets received by higher priority nodes?
    • Using batch map
slide18

How and When does the process complete?

  • If a node’s batch map indicates that over 90% of the batch has been received by higher priority nodes, the node sends nothing when its turn comes
  • When ultimate destination’s turn comes to send, it transmits 10 packets including only its batch map and no data
  • Question: How is the remaining 10% data delivered?
    • Using traditional routing
who received the packet
Who Received the Packet?

Standard unicast 802.11 frame with ACK:

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

src

dest

payload

ACK

src

dest

ExOR frame with slotted ACKs:

src

cand1

cand2

cand3

payload

ACK1

ACK2

ACK3

src

cand1

cand2

cand3

slotted ack example

X

A

D

C

B

payload

ACK

ACK

A

D

C

B

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

A

B

C

D

agreeing on the best candidate

X

Agreeing on the Best Candidate

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

A

B

C

D

X

X

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

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

B: Broadcasts ACK “D” in third slot

Node D is now responsible for forwarding the packet

slide22

ExOR: Packet Format

  • HdrLen & PayloadLen indicate size of ExOR header and payload respectively
  • PktNum is current packet’s offset in the batch, corresponding to the current batch-map entry
  • FragSz is size of currently sending node’s fragment (in packets)
  • FragNum is current packet’s offset within the fragment
  • FwdListSise is is number of forwarders in list
  • ForwarderNum is current sender’s offset within the list
  • Forwarder List is copy of sender’s local forwarder list
  • Batch Map is copy of sending node’s batch map, where each entry is an index into Forwarder List
slide23

Transmission Timeline for an ExOR transfer

N24 not able to listen to N5.

N8 does not send

N17 might have missed some batch-maps

preliminary concept evaluation
Preliminary Concept Evaluation
  • Strengths
    • ExOR is nimble
    • Efficient in total number of packet transmissions
  • Weaknesses
    • Requires (partial) link-state graph
    • Candidate selection is tricky
    • Requires changes to MAC
slide26

ExOR: 2x Improvement in throughput

1.0

0.8

0.6

Cumulative Fraction of Node Pairs

0.4

0.2

ExOR

Traditional

0

0

200

400

600

800

Throughput (Kbits/sec)

Figure 8: The distribution of throughputs of ExOR and traditional routing between the 65 node pairs. The plots shows the median throughput achieved for each pair over nine experimental runs.

Median throughputs: 240 Kbits/sec for ExOR,

121 Kbits/sec for Traditional

slide27

25 Highest throughput pairs

3 Traditional Hops

2.3x

2 Traditional Hops

1.7x

1 Traditional Hop

1.14x

1000

ExOR

TraditionalRouting

800

600

Throughput (Kbits/sec)

400

200

0

Node Pair

Figure 9: The 25 highest throughput pairs, sorted by traditional routing throughput. The bars show each pair's median throughput, and the error bars show the lowest and highest of the nine experiments.

  • For single hop pairs ExOR provides the advantage of lower probability of source resending packets, as there’s higher probability of source receiving the destination’s 10 batch-map packets
slide28

25 Lowest throughput pairs

1000

ExOR

4 Traditional Hops

3.3x

TraditionalRouting

800

600

Throughput (Kbits/sec)

400

200

0

Node Pair

Longer Routes

Figure 10: The 25 lowest throughput pairs. The bars show each pair's median throughput, and the error bars show the lowest and the highest of the nine experiments. ExOR outperforms traditional routing by a factor of two or more.

  • As number of node pairs increases along a route, the likelihood of increased choice of forwarding nodes and multiple ways to ‘gossip’ back batch-maps, increases
  • With greater routing length ExOR is able to take advantage of asymmetric links also
slide29

Traditional routing has to select the ‘shortest’ path which results in compromise on selecting drop probability, thus increasing the number of transmissions

ExOR has no limitations on number of nodes, from the forwarder list, that can forward the packet. Hence it uses both nodes closer to source and nodes closer to destination, irrespective of their drop probability

Retransmissions affected by selection of hops

Figure 11: The number of transmissions made by each node during a 1000-packet transfer from N5 to N24. The X axis indicates the sender's ETX metric to N24. The Y axis indicates the number of packet transmissions that node performs. Bars higher than 1000 indicate nodes that had to re-send packets due to losses.

slide30

Big chunk of transmission, in traditional routing, takes place over shorter distances

Max. distance traveled by hops in traditional routing

Distance traveled by transmissions in ExOR

But cumulative transmission is substantial

Number of packets carried over individual long distance links is small

ExOR moves packets farther

Figure 12: Distance traveled towards N24 in ETX space by each transmission. The X axis indicates the di®erence in ETX metric between the sending and receiving nodes; the receiver is the next hop for traditional routing, and the highest-priority receiving node for ExOR. The Y axis indicates the number of transmissions that travel the corresponding distance. Packets with zero progress are not received by the next hop (for traditional routing) or by any higher-priority node (for ExOR).

slide31

58% of Traditional Routing transmissions

25% of ExOR transmissions

ExOR moves packets farther

0.6

ExOR

Traditional Routing

0.2

Fraction of Transmissions

0.1

0

0

100

200

300

400

500

600

700

800

900

1000

Distance (meters)

  • Delivery Probability decreases with distance
  • ExOR average: 422 meters/transmission
  • Traditional Routing average: 205 meters/tx
slide32

ExOR uses links in parallel

Traditional Routing

3 forwarders

4 links

ExOR

7 forwarders

18 links

slide33

Batch Size

  • ExOr header grows with the batch size
  • Large batches work well for low-throughput pairs due to redundant batch map transmissions
  • Small batches work well for high throughput pairs due to lower header overhead
critical analysis
Critical Analysis
  • Static
    • No mobility
  • Small Scale
    • Tens of nodes
  • Dense network

- Maybe Only Rooftop Networks

  • File downloading application
    • No voice, maybe not web (No reliable guarantee)
  • No Cross Traffic
static
Static
  • Knowing the whole topology
    • In a mobile network, this is expensive
  • EXT is costly
    • Measure link states of all possible links
  • Route change
    • During a batch, route may change
small scale
Small Scale
  • Knowing the whole topology
    • In a mobile network, this is expensive
  • EXT is costly
    • Measure link states of all possible links
  • Large overhead in ExOR packet header
    • All the forwarders are included in ExOR header
    • Long vain waiting of forwarding timer
    • The larger the network, the longer the average distance between S and D, the more forwarders in the list
      • Traditional header (24~48 -> 8 if AODV)
      • ExOR header (44~114 for 38-node network)
more critical analysis yao zhao northwestern
More Critical AnalysisYao Zhao (Northwestern)
  • No TCP and hence proxy
  • Voice
    • Jitter
  • Web service
    • Is batch good?
    • May introduce large delay
  • Large file download
    • Best for ExOR
cross traffic yao zhao northwestern
Cross TrafficYao Zhao (Northwestern)
  • Forwarding timer
    • Give higher-priority nodes enough time to send?
    • Assume 5 packets sent if a node cannot hear another node with higher priority – hard to justify heuristic. Also, forwarders could be consistently mutually inaccessible
    • 802.11 use CSMA/CA, competition based MAC
    • If there is cross traffic, hard to estimate the transmission time of other nodes
unfair comparison yao zhao northwestern
Unfair Comparison ?Yao Zhao (Northwestern)
  • Bad Traditional routing (DSR)
    • Don’t think about link state changing
    • Long packet header
    • Send the entire file to the next node before the next node starts sending
  • Bad MAC Selection
    • Retransmit packet if ACK is lost
    • Why not packet train?
  • A Paper in 2005 compared to some works before 1999 ?
acknowledgements
Acknowledgements

Many sketches, animated-diagrams, as well as some text have been sourced from the following materials-

  • Course material on “Net Centric Systems” taught at TECHNISCHE UNIVERSITÄT DARMSTADT
  • Presentation on “A High Throughput Route-Metric for Multi-Hop Wireless Routing” by Eric Rozner of University of Texas, Austin
  • Presentation on “ExOR: Opportunistic Multi-Hop Routing for Wireless Networks”, by Sanjit Biswas and Robert Morris at Siggcomm
  • “ExOR: Opportunistic Multi-Hop Routing for Wireless Networks” - Sanjit Biswas and Robert Morris
  • Presentation on “ExOR: Opportunistic Multi-Hop Routing for Wireless Networks”, by Avijit of University of California, Santa Barbara
  • Presentation on “ExOR: Opportunistic Multi-Hop Routing for Wireless Networks”, by Yu Sun of University of Texas, Austin
  • Presentation on “ExOR: Opportunistic Multi-Hop Routing for Wireless Networks”, by Gaurav Gupta, University of Southern California
  • Presentation on “ExOR: Opportunistic Multi-Hop Routing for Wireless Networks”, by Ao-Jan Su, Northwestern University