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UFlood: High-Throughput Wireless Flooding

This work presents UFlood, an innovative flooding protocol designed for wireless multi-hop networks with a focus on real-time video distribution. The primary goal is to minimize the number of transmissions while maximizing throughput by selecting the best sender for every transmission. UFlood outperforms traditional controlled flooding methods, achieving twice the throughput and closely matching benchmark performance in practical evaluations. Key challenges addressed include efficient sender selection, probabilistic reception dynamics, and the optimization of packet utility to enhance delivery efficiency across the network.

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UFlood: High-Throughput Wireless Flooding

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

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

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

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

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

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

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

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

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

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

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

  13. UFlood is a Local Heuristic • 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. Outline of this talk • Existing Solutions • Key Idea of UFlood • Design of protocol • Evaluation

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

  16. Knowing Node States • Node states • Ni- number of neighbors of i • P – # packets flooded • node-state matrix – [NixP] • Method: • Maintain local version • Gossip packet availability using periodic status packets Packet # 1 1 2 2 3 3 Node # 1 1 1 1 1 1 0 2 2 0 0 1 1 0 3 3 0 0 0 0 0

  17. N N 1 2 3 1 1 0.3 0.5 2 0.4 1 1 3 0.9 0.6 1 Computing Delivery Probabilities • 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:

  18. Computing Packet Utility Node states Delivery probabilities

  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: • Update node-state matrix • Calculate utility • Transmit in burst – all packets whose utility is greater than neighbor’s utility • CSMA handles contention (Listen then send or back off)

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

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

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

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

  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. ExOR packet packet packet packet • Decide who forwards after reception • Goal: only closest receiver should forward A B src dst packet packet packet packet packet C

  29. Throughput of UFlood = Throughput of Estimated ExOR

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

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

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

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