1. Efficient Flooding for Wireless Mesh Networks
Jayashree Subramanian

2. Challenges in Wireless Flooding
Wireless receptions are probabilistic
How many packets to transmit?
Pattern of packet reception is non-deterministic
What packets are with each receiver?
Feedback is expensive
Wireless transmissions are inherently broadcast
Two near by transmissions cannot coexist
How to exploit opportunism?

3. Design of UFLOOD Design questions
Who should transmit next?
What to transmit?
UFlood’s claim: Selection of best sender
Higher throughput
Fewer #transmissions
What are the important question in designing wireless flooding protocols: (1) Who should transmit next? (2) And what packet a node should transmit? How does UFLOOD handle these – It selects the best sender and send coded packets.
Sender selection
Feedback

4. UFlood’s Sender Selection Strategies
Favor higher delivery Probabilities
Favor senders with large number of receivers
Favor senders with new information
Account for correlated receptions
Utility = Value of a node’s transmission
Best Sender  Highest Utility

5. Computing Packet Utility
Utility(A)
Bit rate of node A
Indicates if transmissions of node A are useful to node B
This is the same equation given in the UFLOOD paper.
P(b)
B(b) I?
Neighbors(A)
Delivery probability from node A to node B

6. How UFLOOD works? PA,B – Independent experiment
U(S)=1.7
U(S)=0.7
PA,B – Independent experiment
b(A) – Bit rate selection scheme
IA,B – Feedback packets B D C A S S
0.9
0.5
0.2
0.1
0.5
This slide explains how uflood works (assuming flooding of a single packet): “Red circle indicates that a node has the packet. Each node calculates utility based on: (1) Delivery probabilities and (2) availability of the packet”
First the source node S is the only node that has the packet and is the best sender. Source S transmits the packet. Nodes A and D receives the packet. Now, nodes S, A, and D compete for the best sender position. Each node calculates the utility of every other node in the network. Node A wins and sends the packet. This continues until all the nodes in the network receives the pcket
0.5
U(A)=1.5 A B 1
0.2
0.3 D
U(D)=0.8 C
0.5

7. Pseudo Code of UFlood Packet preparation:
All APs receive the file from multicast server.
Split file in to equal sized packets
Group in to batches of 64 packets.
Batches are flooded one at a time.

8. Pseudo Code for UFlood Random Network Coding
Source AP has “native” packets (n1,…n64)
Source constructs “coded” packets = Linear Combination or LC(n1,…n64)
P1= c1` n1+ c2` n2+…+ c64` n64
P2= c1`` n1+ c2`` n2+…+ c64`` n64 …
These are first generation packets

9. Pseudo Code of UFlood
UFlood is distributed and a local heuristic: Nodes periodically calculate utility of itself and all its neighbors
The best sender transmits coded packets in burst.
All nodes recode every time a packet is sent
Nodes broadcast feedback of the packets they possess.
Go to Step 6.

10. Implementation Issues: Feedback
I(A,B)=1 if transmissions of A are linearly independent to packets of B
How to construct feedback for Coded packets?
Coefficients of each coded packet – Huge!
Rank = # Linearly independent coded packets
bitmap identifying each distinct first-generation packet that contributed via coding to any of the packets B holds
Feedback  Rank(B)+bitmap+Rank(N(B))
How often to send feedback?
Smart feedback
Nodes interpolates feedback
Detects an idle channel for 3-pkt duration

11. Implementation Issues: Deadlocks
Feedback packets includes neighbor’s rank – Two hop information  Accurate utility calculation of neighbors
Sends burst of packets  Reduces #deadlocks

12. Implementation Issues: Burst size
Burst size = minBεN(A)(LA,B)
LA,B= # Packets A can send to B without causing utility(B) to be greater than utility(A)

13. Contributions of UFlood
Notion of Utility – Sender selcection
Smart feedback for coded packets
A distributed implementation

14. Lower Bit Rates are Slow but Strong
PA,B at b1 <= PA,B at b2, if b1>b2
PA,B at 1Mbps = 1, then PA,B at 54Mbps<=1
Cite the URL

15. Challenges in Bit Rate Selection
Single hop (Lower rate) Vs Multi-hop (Higher rate) A
54M
11M B
54M
Reword the challenge C

16. Challenges in Bit Rate Selection
Many senders and Many Receivers AP
54M
54M
54M C X Y
5.5M
11M
Complexity? B
11M
5.5M A
Net Rate = 11M
Net Rate = 5.5M

17. Bit Rate Selection for Node X
Step 1: ETT(X,C,b) = 1/(PX,C*b)
Step 2: Best bit rate for link XC = minbETT(X,C,b)
Step 2: Construct Dijkstra shortest path routes from AP to all the nodes, using ETT metric
Step 3: Pick the least bit rate to the next hop 1M
54M AP
54M C X Y
5.5M
11M B
11M
5.5M A

18. Implementation
6 APs and 20 nodes on a 250x150meters 3-floor office building
Nodes: 500 MHz AMD Geode LX800 CPU
802.11b/g, Omni-directional antenna
Transmit power = 12 mW
CLICK software router toolkit
Carrier Sense on

19. Performance Comparison
Metrics:

20. Protocols used for Comparison
UFlood Vs MORE
Statically assigns the number of packets a node sends for each packet reception
No detailed feedback
High throughput but wasted transmissions
UFlood Vs MNP
Save Energy
Too slow but efficient transmissions

21. UFlood: Throughput
MNP
MORE
UFlood

22. UFlood: Airtime
UFlood
MORE
MNP

23. Why UFlood Wins?
Each UFLOOD transmission benefit twice as many receivers as MORE and 20% more than MNP

24. Protocols used for Comparison
UCast
Constant Bitrate of 5.5Mbps
Ucast/Rate
Use Bit rate selections
Strawman
Traditional WiFi multicasting
N/w coding
Dircast
AP sends packets until the poorest receiver receives all the packets
N/W coding
Rate selection for APs

25. UCast Vs Dircast VS Strawman: Throughput
Insensitive to AP connection
Ucast/Rate
Ucast
Dircast
Strawman
Client coopertion
Few APs never send!

26. UCast Vs Dircast VS Strawman: Airtime
Inherent properties of UFlood helps reduce #transmissions
Strawman
Dircast
Ucast
Ucast/Rate

27. Why Client Cooperation?
UCast
Only best APs send ?
Strawman
One line result at the end
# Cooperating clients/Total #clients

28. Contributions of this work
UCast: Client cooperation multicasting and experiments show a huge benefit
UFlood: High-throughput distributed flooding scheme
Introduce notion of Utility
Smart feedback for coded packets
Increases throughput and uses fewer transmissions
A novel bit rate selection for flooding protocols

29. Questions?

31. Sender Selection Strategy 1: Favor High Delivery ProbabilitiesAP A C
0.8
0.3
0.9
0.2 B D
Back…

32. UFlood Strategy 2: Favor Large Number of ReceiversAP A B
0.4
0.5
0.4
0.4
0.4
0.4 C D E F G
Back…

33. UFlood Strategy 3: Favor Senders with New InformationAP A C
0.2
0.1
0.9
0.2
Feed back B D
Back…

34. UFlood Strategy 4: Account for Correlated Receptions
P1 … P50 AP
0.5
0.5
P`1 … P`25 F1 F2
P``1 … P``25 1 1
But in realistic wireless network, feedback .
Change animationLast two requires dynamic updates R
P`1 … P`25 == P``1 … P``25?
Feedback?
Back…

35. Inherent Wireless Characteristics
Independent Receptions
Links Invariance

36. What is Random Network Coding(RNC)?
In Wired Networks… P1 P2 F1 F2 P1 P2 F3
F - Forwarders
R - Receivers P1
P1+P2 P2
P1+P2
P1+P2 F4
P1,?
?,P2
P1,P1+P2 R1 R2
P1+P2,P2

37. What is RNC? In Wired Networks…
P1…P10
P11…P20
P``1…P``10 F1 F2
P`1…P`10 F3
F - Forwarders
R - Receivers
P`1…P`10
P``1…P``10
P```1…P```10
P`1…P` LC(P1,…,P10)
P``1…P`` LC(P11,…,P20)
P```1…P``` LC(P`1,…,P`10,
P``1,…,P``10) F4 R1 R2
P`1…P`10
P```1…P```10
P``1…P``10 P```1…P```10
How many packets to transmit ? - connectivity

38. Calculation of IA,B
Max # of linearly independent coded packets that B can receive from A
This is based on Section 4.6 in the paper.

39. Factors Affecting Performance of UFLOOD: Network size
Large network has many potential senders and receivers with hard-to-predict states and choosing best sender is a challenge

40. Factors Affecting Performance of UFLOOD: Node Density
Low delivery probability cause different nodes to receive different packets, which increases importance of sender selection

41. Factors Affecting Performance of UFLOOD: Batchsize
Larger batches allow coding to be effective. Smaller batches decrease throughput because of per-batch overhead

42. Factors Affecting Performance of UFLOOD: Bitrates
UFLOOD’s performance is consistently higher than MORE at various bitrates

43. Background on Bit rates
If delivery probability of a link at bit rate R1 Mbps = P(R1), then
P(R2)>P(R1) for all R2<R1
Bit error rate
Cite the URL
Signal to Noise Ratio