1. RBP: Robust Broadcast Propagation in Wireless Networks Fred Stann, John Heidemann, Rajesh Shroff, Muhammad Zaki Murtaza USC/ISI In SenSys 2006

2. Outline Introduction Related work RBP algorithm Analysis Implementation Simulation results Testbed results Further simulations Conclusion

3. What is Flooding? Every node broadcast To let every node receive the message

4. Flooding in Wireless Networks Route discovery  DSR, AODV Resource discovery  Directed diffusion Network-integrated database systems  TinyDB

5. Why is Broadcast Unreliable? Collision  Collision detection/avoidance PHY layer capture MAC layer TDMA Application layer jitter Unreliable wireless link  Retransmission May incur unnecessary overhead No RTS-CTS-data-ACK  Prevent control traffic implosion  Directly reflect the reliability of wireless channel

6. Related Work

7. Wired Networking Reliable multicast  SRM, RMTP  100% reliability, repair triggered by missing sequence number RBP relaxed 100% slightly for efficiency, and focus on individual flood

8. Wireless for improved reliability Probabilistic broadcast  Reduce collisions and energy consumption  Requires 8+ neighbors (high density) Gossiping  multiple rounds of exchanges  Local repair of missed data RBP adapts to density and recognizes failed delivery by overhearing

9. Wireless for improved reliability Area-based method in MANETs  Using knowledge of complete node locations/distances to suppress redundant broadcasts  Minimize the bandwidth consumed RBP does not focus on efficiency, and only requires local information

10. Wireless with perfect reliability Application such as reprogramming the entire network (Deluge) TDMA for contention free reliable broadcast RBP  Does not do TDMA because wireless is volatile  Does NOT focus on applications demanding perfect reliability

11. Short Summary Goals of RBP  Tries to improve reliability but not 100%  Adapts to neighborhood density  Does not focus on redundancy suppression

12. Algorithm

13. Ideas of RBP Detect delivery failure by overhearing  Implicit ACK Adapts retransmission threshold and times to neighborhood density  Reduce redundant broadcast Important link detection  Bottleneck!

14. RBP Algorithm – Step 1 A node knows the identity of its one hop neighbors  Neighbor must have inbound and outbound connectivity  No weak and asymmetric neighbors

15. RBP Algorithm – Step 2 Retransmission  Every node would forward the flooding packets at the first time  Nodes snoop and keep track of neighbor rebroadcast (implicit ACK)  If this rebroadcast record is lower than a percentage of neighbors, the node again retransmit the packet

16. RBP Algorithm – Step 3 Retransmission threshold and number of retries are adjusted based on neighborhood density

17. Detection of important links RBP Algorithm – Step 4 High density with one especially important link

18. RBP Algorithm – Step 4 Every node keeps a histogram of the neighbor first transmitting unheard broadcast If a single neighbor has the majority, sends a control message to inform this important link

19. Short Summary neighborthresholdretry 1-3100%3 4-666%2 7+50%1

20. Analysis

21. Uniformly Distributed Network Previous studies shown that the reliability will increase while network density increases In real deployment, uneven distribution is common High density around the source but low density away could give misleading reliability

22. Effect of Multi-path Assume per link reliability 85% P(e2e) = 85%*85%*85%=61% P(e2e) = 1-(1-61%) (1-61%) (1-61%) = 94%

23. Effect of Multi-path P(e2e) = 99.9% Flooding is inherently reliable

24. What if a bottleneck Bounded by bottleneck link  85%

25. Implementation

26. Settings Environment: EmStar Directed diffusion and B-MAC  One-phase-pull Resides between routing and MAC RBP timeout 10 sec  Diffusion has 1 sec forwarding delay and 800ms jitter

27. Neighborhood Discovery Modules provided by EmStar Broadcast packets have sequence number and inbound connectivity attached Compute over 12 broadcast pkts Use upper and lower reliability threshold (70% and 60 % in testbed)

28. Simulation Results

29. Settings Environment: EmSim Directed diffusion resource discovery flood every 60 sec No flooding overlap No data sources

30. Error Model EmSim provides an communication error model, but computed independently for each tx/rx pair Is real-world packet loss spatially correlated?

31. Error Model Experiment of 8 stargate node surrounding one sender. Independence of receiver errors Correlated transmission failures

32. Metrics Reliability: percentage of floods that traverse the network diameter Bytes-per-flood: sum of byte transmissions triggered by a single event

33. Reliability Cost Metric (RCM) Number of floods required to achieve near-perfect reliability Worst case: bottleneck link exits If propagation reliability is 85%, 20 nodes, 80 byte broadcast pakcet. F=2.4, measured BytesPerFlood=1200 RCM=1.8 F=1 for RBP Cost of a perfect flood

34. Topology

35. Results - Reliability TRP: rebroadcast whenever less than 99% of neighbors receiving up to 4 times (MAC-only)

36. Results - BytesPerFlood

37. Results - RCM RBP degrades to TRP in low density networks Unnecessary attempts to achieve reliability when density is high

38. Testbed Results

39. Testbed 20 stargate nodes Nonuniform connectedness

40. Results Close to the simulation result

41. Results They initially do not add the important link detection. RBP reliability is slightly better than single flood and RCM higher

42. Further Simulation

43. Effect of Density

44. High density reduce the advantage of RBP

45. Effect of Correlated Error Density fixed to 6 neighbors

46. Effect of Pair-wise Error With enough density, single link failure has less impact on flooding

47. Future work and conclusion State-limited version Focus on end-to-end reliability Variable density Real testbed results