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On Demand Routing in Large Ad Hoc Wireless Networks With Passive Clustering

On Demand Routing in Large Ad Hoc Wireless Networks With Passive Clustering. Mario Gerla, Taek Jin Kwon and Guangyu Pei Computer Science Department University of California, Los Angeles Los Angeles, CA, 90095. Clustering in Ad hoc Networks.

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On Demand Routing in Large Ad Hoc Wireless Networks With Passive Clustering

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  1. On Demand Routing in Large Ad Hoc Wireless Networks With Passive Clustering Mario Gerla, Taek Jin Kwon and Guangyu Pei Computer Science Department University of California, Los Angeles Los Angeles, CA, 90095 UCLA CSD Gerla, Kwon and Pei

  2. Clustering in Ad hoc Networks A natural way to provide some “structure” in an ad hoc network • Better Channel Efficiency(code diversity) • Bandwidth allocation & QoS support • Cluster based routing -> scalability • Suppress redundant transmissions in On-Demand Routing UCLA CSD Gerla, Kwon and Pei

  3. Example of Clustering  1 8 7 6 5 3 4 UCLA CSD Gerla, Kwon and Pei

  4. AODV: flooding O/H • AODV requires flood-search to find and establish routes • Flood-search: each node forwards Query pkt (RREQ) to neighbors • If network is “dense” (ie, several nodes within the tx range), this leads to a lot of redundant transmissions • Energy waste & throughput loss UCLA CSD Gerla, Kwon and Pei

  5. Clustering helps On-demand routing • The network is organized in clusters • All nodes in a cluster can communicate directly (one hop) with clusterhead • Gateways maintain communications between clusters • Only clusterheads and gateways forward search-flood queries • Suppress redundant transmissions! UCLA CSD Gerla, Kwon and Pei

  6. Example of Clustehead & Gateway Forwarding  1 8 7 6 5 3 4 UCLA CSD Gerla, Kwon and Pei

  7. Drawbacks of Conventional Clustering (eg,Least ID #) • Periodic neighbor connectivity monitoring may lead to high O/H • Periodic control traffic not desirable in military covert operations • Unstable behavior of “least ID cluster election” scheme: small move -> large change! UCLA CSD Gerla, Kwon and Pei

  8. Passive Clustering • Goals: no monitoring O/H, more stable.. • Approach: (a) No “Active” Control Packets: Cluster state information piggybacked on data packets (b) Clusters are built only when on-demand routes are opened (c) Soft state: when data transmissions cease, time-out clears stale clusters UCLA CSD Gerla, Kwon and Pei

  9. Passive Clustering: example Assume Node 1 initiates a search flood…. 1 8 7 6 9 3 4 2 UCLA CSD Gerla, Kwon and Pei

  10. Passive Clustering  1 8 7 6 9 3 4 2 UCLA CSD Gerla, Kwon and Pei

  11. Passive Clustering Clusterhead_ready  1 8 7 6 9 3 4 2 UCLA CSD Gerla, Kwon and Pei

  12. Passive Clustering Clusterhead  1  8 7 6 9 3 4 2 UCLA CSD Gerla, Kwon and Pei

  13. Passive Clustering Ordinary Node  1  8 7 6 9 3 4 2 UCLA CSD Gerla, Kwon and Pei

  14. Passive Clustering  1   8 7 6 9 3 4  2 UCLA CSD Gerla, Kwon and Pei

  15. Passive Clustering  1   8 7 6 9 3 4  2 UCLA CSD Gerla, Kwon and Pei

  16. Passive Clustering  1   8 7 6 9 3  4  2 UCLA CSD Gerla, Kwon and Pei

  17. Passive Clustering  1   8 7 6 9 3  4  UCLA CSD Gerla, Kwon and Pei

  18. Passive Clustering  1   8 7 6 9 3  4   2 UCLA CSD Gerla, Kwon and Pei

  19. Passive Clustering Gateway  1   8 7 6 9 3  4   2 UCLA CSD Gerla, Kwon and Pei

  20. Passive Clustering  1   8 7  6 9 3  4   2 UCLA CSD Gerla, Kwon and Pei

  21. Passive Clustering  1   8 7  6 9 3  4   2 UCLA CSD Gerla, Kwon and Pei

  22. Passive Clustering  1    8 7  6 9 3  4   2 UCLA CSD Gerla, Kwon and Pei

  23. Passive Clustering  1    8 7  6 9 3  4    2 UCLA CSD Gerla, Kwon and Pei

  24. Passive Clustering  1    8  7  6 9 3  4    2 UCLA CSD Gerla, Kwon and Pei

  25. Passive Clustering  1    8  7  6 9 3  4    2 UCLA CSD Gerla, Kwon and Pei

  26. Passive Clustering  1     8  7  6 9 3  4    2 UCLA CSD Gerla, Kwon and Pei

  27. Passive Clustering  1     8  7  6 9 3  4    2 UCLA CSD Gerla, Kwon and Pei

  28. Passive Clustering  1      8  7  6 9 3  4    2 UCLA CSD Gerla, Kwon and Pei

  29. Passive Clustering  1      8   7  6 9 3  4    2 UCLA CSD Gerla, Kwon and Pei

  30. Passive Clustering Resulting cluster structure.  1      8   7  6 9 3  4    2 UCLA CSD Gerla, Kwon and Pei

  31. Lowest ID Clustering result 3 isolated clouds – 1, 2, and the rest  1      8   7  6 9 3  4    2 UCLA CSD Gerla, Kwon and Pei

  32. Simulation Environment (GloMoSim) • 100 nodes in 1000m x 1000m • Transmission range : 150m • Mobility model: Random Waypoint • AODV unicast routing • Random Source/Destination Pairs • CBR traffic. • 512 bytes per packet, 0.4 packets per sec UCLA CSD Gerla, Kwon and Pei

  33. Normalized Routing Overhead UCLA CSD Gerla, Kwon and Pei

  34. Mean End-to-End Delay UCLA CSD Gerla, Kwon and Pei

  35. Mean End-to-End Delay UCLA CSD Gerla, Kwon and Pei

  36. Throughput UCLA CSD Gerla, Kwon and Pei

  37. Throughput UCLA CSD Gerla, Kwon and Pei

  38. Summary • Passive clustering • Realistic, “overhead free” mechanism • First Declaration Wins rule • Stable clusterhead election • AODV application • Efficient search-flood; higher thoughput; Next: try Passive Clustering on DSR, ODMRP and other search-flood schemes UCLA CSD Gerla, Kwon and Pei

  39. Thank You!

  40. Chain Reaction (contd) 1 8 7 6 5 3 4 UCLA CSD Gerla, Kwon and Pei

  41. Chain Reaction (contd) 1 8 7 6 5 3 4 UCLA CSD Gerla, Kwon and Pei

  42. Chain Reaction (contd) 1 8 7 6 5 3 4 UCLA CSD Gerla, Kwon and Pei

  43. Chain Reaction (contd) 1 8 7 6 5 3 4 UCLA CSD Gerla, Kwon and Pei

  44. Passive Clustering Continued .. • Pros and Cons • Little line overhead ↔ Longer Convergence time • Free Neighbor info.↔ Partial Neighbor Info. • Better Structure • Easy to Implement • Energy Efficiency UCLA CSD Gerla, Kwon and Pei

  45. AODV (Ad Hoc On Demand DV) Routing application • AODV version with Hello messages • Hello messages exchanged every 1.5 seconds • Hello message reduction • No Hello if the node is Ordinary node • RREQ, RREP, REER cancel scheduled Hello • Reduced Flooding • Ordinary nodes do not forward the RREQ packets UCLA CSD Gerla, Kwon and Pei

  46. Passive Clustering features • Passive clustering with 802.11 • Data traffic activated process • Clusterhead election rule – FDW • Cluster time out : 2 sec UCLA CSD Gerla, Kwon and Pei

  47. Mean End-to-End Delay UCLA CSD Gerla, Kwon and Pei

  48. Chain Reaction set off by motion of node 1 1 8 7 6 5 3 4 UCLA CSD Gerla, Kwon and Pei

  49. Final Clusters very different from the initial ones  1 8 7 6 5 3 4 UCLA CSD Gerla, Kwon and Pei

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