1. 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)

2. Multi-Hop Wireless Networks
2km
Gateway
Gateways
2km
Dense b-based mesh, all sorts of loss rates
Goal is efficiency and high-throughput

3. The Traditional View Identify a route, forward over links
packet
packet
packet A B
src
dst
packet
packet C
Identify a route, forward over links
Use link level retransmissions

4. How Radios Actually WorkB
src
dst 1 1 1 1 2 2 2 2 3 3 3 3 4 4 4 4 5 5 5 5 6 6 6 6 C
Every packet is broadcast
No such thing as a link

5. ExOR: Exploiting the InsightA B
src
dst
packet
packet
packet
packet
packet C
packet
packet
packet
packet
Figure out which nodes rx’d broadcast
Node closest to destination forwards

6. ExOR: Exploiting the Insight
packet
packet
packet
packet A B
src
dst
packet
packet
packet
packet
packet C
Figure out which nodes rx’d broadcast
Node closest to destination forwards

7. ExOR’s Assumptions Many receivers hear every broadcast
Gradual distance-vs-reception tradeoff
Receiver losses are uncorrelated A B
src
dst C

8. 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
100%
75%
50%
25% 0% S

9. 2. Gradual Distance vs. Reception Tradeoff
Same Source
Delivery Ratio
Distance (meters)
Wide spread of ranges, delivery ratios
Transmissions may “get lucky” and travel long distances

10. 3. Receiver Losses are Uncorrelated
Example Broadcast trace:
Receiver 1 (38%):
Receiver 2 (40%):
Receiver 3 (74%):
Receiver 4 (12%):
Two 50% links don’t lose the same 50% of packets
Losses not due to common source of interference

11. 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

12. How ExOR might provide more throughputS N1 N2 N3 N4 N5 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

13. How ExOR might provide more throughput (contd..)
25%
100% N2
25%
100%
src
dst
25%
100% N3
25%
100% N4
Traditional routing: 1/ = 5 transmissions
ExOR: 1/(1 – (1 – 0.25)4) + 1 = 2.5 transmissions
Assumes independent losses

14. 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

15. 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

16. 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

17. 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

18. 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

19. Who Received the Packet?
Standard unicast frame with ACK:
src
dest
payload
ACK
src
dest
ExOR frame with slotted ACKs:
src
cand1
cand2
cand3
payload
ACK1
ACK2
ACK3
src
cand1
cand2
cand3
Slotting prevents collisions ( ACKs are synchronous)
Only 2% overhead per candidate, assuming 1500 byte frames

20. Slotted ACK Example X Packet to be forwarded by Node C
payload D C B A D
ACK C
ACK B
Packet to be forwarded by Node C
But if ACKs are lost, causes confusion

21. Agreeing on the Best CandidateX X X
A: Sends frame with (D, C, B) as candidate set
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

22. 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

23. Transmission Timeline for an ExOR transfer
N24 not able to listen to N5.
N8 does not send
N17 might have missed some batch-maps

24. 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

25. 65 Roofnet node pairs
1 kilometer

26. ExOR: 2x Improvement in throughput
1.0
0.8
0.6
Cumulative Fraction of Node Pairs
0.4
0.2
ExOR
Traditional
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: Kbits/sec for ExOR,
121 Kbits/sec for Traditional

27. 25 Highest throughput pairs
3 Traditional Hops
2.3x
2 Traditional Hops
1.7x
1 Traditional Hop
1.14x
1000
ExOR
800
Traditional Routing
600
Throughput (Kbits/sec)
400
200
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

28. 25 Lowest throughput pairs
1000
ExOR
4 Traditional Hops
3.3x
800
Traditional Routing
600
Throughput (Kbits/sec)
400
200
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

29. Retransmissions affected by selection of hops
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
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.

30. ExOR moves packets farther
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
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).

31. ExOR moves packets farther
58% of Traditional Routing transmissions
0.6
ExOR
Traditional Routing
0.2
25% of ExOR transmissions
Fraction of Transmissions
0.1
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

32. ExOR uses links in parallel
Traditional Routing
3 forwarders
4 links
ExOR
7 forwarders
18 links

33. 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

34. 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

35. 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

36. 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)

37. Dense Networks A C S E X B D

38. Dense Networks

39. More Critical Analysis Yao Zhao (Northwestern)
No TCP and hence proxy
Voice
Jitter
Web service
Is batch good?
May introduce large delay
Large file download
Best for ExOR

40. Cross Traffic Yao 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
use CSMA/CA, competition based MAC
If there is cross traffic, hard to estimate the transmission time of other nodes

41. 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 ?

42. Questions?

43. 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