1. 1 UFlood: High-Throughput Wireless Flooding Jayashree Subramanian Collaborators: Robert Morris, Ramakrishna Gummadi, and Hari Balakrishnan

2. 2 Goal of this work To design a flooding protocol for wireless multi-hop networks Application: Real-time video distribution What is a good flooding protocol? Use least #transmissions Provide high-throughput for the nodes

3. 3 1 1 1 Every transmission is broadcast (Opportunistic Receptions!) Reception is probabilistic Challenge in Wireless Flooding 121 3 4 6 2 5 2 3 6 2 src AB D C 2 3 4 56 Delivery probability from Src to B 0.1

4. 4 Challenge: Who should transmit next? What packet to transmit? UFlood’s claim: Select best sender – to minimize total #transmissions to complete flooding Challenge in Wireless Flooding …contd

5. 5 Contribution of this work 1.UFlood – Choosing best sender for every transmission maximizes throughput 2.UFlood performance: Achieves 2x higher throughput than controlled flooding Performs close to the benchmark - ExOR unicast routing (multihop transfer to a single receiver)

6. 6 6 Existing Solutions Key Idea of UFlood Design of UFlood protocol Evaluation Outline of this talk

7. 7 Related Work 1. Flooding in routing Controlled flooding (AODV, DSR) × Does not maximize throughput 2. Tree-based flooding MCDS, LESS × Does not consider opportunism 3. Gossip-based flooding B C A S 0.1 Wasted transmission! 2. Tree-based flooding (static decision)

8. 8 Related Work … contd 3. Gossip-based flooding (dynamic decision) Trickle, Deluge × Does not choose best sender B C A S 0.9 0.1 Bad sender choice means more transmissions!

9. 9 9 Existing Solutions Key Idea of UFlood Design of UFlood protocol Evaluation Outline of this talk

10. 10 6 Select the best sender - to maximize throughput Select the best sender - to maximize Useful Receptions Key Idea Packet availability src AB D C 1 2 3 4 5 1 1 1 1 4 6 6 3 3 2 5 Delivery probability 0.9 0.1

11. 11 Calculating Useful Receptions 6 src AB D C 1 2 3 4 5 1 1 1 1 4 6 6 3 3 2 5 U(A,4)= 0.9+0.4+0.6=1.8 0.9 0.4 0.6 U(B,1)=0

12. 12 0.9 0.5 0.2 0.1 B D C A S U(S)=1.7 0.5 0.2 U(S)=0.7 U(A)=1.5 1 0.5 0.3 0.5 U(D)=0.8 S A D B C To Flood a Single Packet using UFlood To flood multiple packets calculate utility for every packet!s

13. 13 UFlood is a Local Heuristic 13 Difficulty: Knowing the packet availability at all nodes Solution: Local heuristic- Every node knows only about its neighbors (nodes whose packets it can hear!) Good news: Possibility of spatial reuse!

14. 14 Existing Solutions Key Idea of UFlood Design of protocol Evaluation Outline of this talk

15. 15 Design of UFlood Information required: 1.Node states - packets available with the neighbors 2.Delivery probabilities of all node pairs in the network

16. 16 Node states N i - number of neighbors of i P – # packets flooded node-state matrix – [N i xP] Method: Maintain local version Gossip packet availability using periodic status packets 0003 0102 0111 321 003 102 111 321 Knowing Node States Packet # Node #

17. 17 Delivery probabilities For all N nodes in the network Delivery prob matrix – [NxN] Method – offline experiment Each node broadcasts continuous probes and rest of the nodes compute: Computing Delivery Probabilities 10.60.93 110.42 0.50.311 321 N N

18. 18 Computing Packet Utility Delivery probabilities Node states

19. 19 Pseudo code of UFlood Source Node floods all packets All nodes periodically broadcast status packet On reception of a new data or status packet: 1.Update node-state matrix 2.Calculate utility 3.Transmit in burst – all packets whose utility is greater than neighbor’s utility CSMA handles contention (Listen then send or back off)

20. 20 Batching in UFlood The packets are sent in batches To reduce overhead To limit the size of the status packet Current design considers single batch!

21. 21 Existing Solutions Key Idea of UFlood Design of UFlood protocol Evaluation Outline of this Talk

22. 22 250 feet 200 feet 20-Node Indoor Test-bed Source Node

23. 23 Implementation Meraki mini, 802.11b/g 2dbi Omni-directional antenna Transmit power = 60mW Bit rate = 24Mbps CLICK software router toolkit Carrier Sense on

24. 24 Performance Comparison Method: Flood a single batch of 5000- 1500B packet Comparison: UFlood Vs. Controlled Flooding UFlood Vs. Unicast routing

25. 25 UFlood Vs. Controlled Flooding Used in routing protocols like AODV and DSR Method: Source broadcasts all packet Every node rebroadcasts only once Why we used? Aim: to quickly send the route information

26. 26 Throughput of UFlood = 2x Throughput of Controlled Flooding No Choice of Sender!

27. 27 UFlood Vs. Estimated Unicast Why unicast? Multihop transfer to one receiver Vs many receivers Method Setup independent unicast sessions to send a batch of packets from source (node 4) to each node

28. 28 packet ExOR src AB dst C packet Decide who forwards after reception Goal: only closest receiver should forward

29. 29 Throughput of UFlood = Throughput of Estimated ExOR

30. 30 Second best node transmits! Why does UFlood Perform good? Best node transmits! UFlood is a local heuristic – Occasional errors!

31. 31 Future Work Implement network-coding, bit-rate adaptation, and batching UFlood vs. existing high-throughput flooding protocols

32. 32 Conclusion 1.UFlood’s Key Idea – Choosing best sender for every transmission maximizes throughput 2.UFlood’s Performance: Achieves 2x higher throughput than controlled flooding Performs close to the benchmark - ExOR unicast routing