9/25/2000ucla csd gerla, kwon and pei on demand routing in large ad hoc wireless networks with...
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9/25/2000UCLA CSD Gerla, Kwon and
Pei
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
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9/25/2000UCLA CSD Gerla, Kwon and
Pei
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
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9/25/2000UCLA CSD Gerla, Kwon and
Pei
Example of Clustering
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9/25/2000UCLA CSD Gerla, Kwon and
Pei
AODV: flooding O/H
AODV requires flood-search to find and establish routesFlood-search: each node forwards Query pkt (RREQ) to neighborsIf network is “dense” (ie, several nodes within the tx range), this leads to a lot of redundant transmissionsEnergy waste & throughput loss
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9/25/2000UCLA CSD Gerla, Kwon and
Pei
Clustering helps On-demand routing
The network is organized in clustersAll nodes in a cluster can communicate directly (one hop) with clusterheadGateways maintain communications between clustersOnly clusterheads and gateways forward search-flood queries Suppress redundant transmissions!
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9/25/2000UCLA CSD Gerla, Kwon and
Pei
Example of Clustehead & Gateway Forwarding
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9/25/2000UCLA CSD Gerla, Kwon and
Pei
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!
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9/25/2000UCLA CSD Gerla, Kwon and
Pei
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
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9/25/2000UCLA CSD Gerla, Kwon and
Pei
Passive Clustering: example
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Assume Node 1 initiates a search flood….
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9/25/2000UCLA CSD Gerla, Kwon and
Pei
Passive Clustering
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9/25/2000UCLA CSD Gerla, Kwon and
Pei
Passive Clustering
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Clusterhead_ready
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9/25/2000UCLA CSD Gerla, Kwon and
Pei
Passive Clustering
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Clusterhead
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9/25/2000UCLA CSD Gerla, Kwon and
Pei
Passive Clustering
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Ordinary Node
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9/25/2000UCLA CSD Gerla, Kwon and
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Passive Clustering
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9/25/2000UCLA CSD Gerla, Kwon and
Pei
Passive Clustering
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9/25/2000UCLA CSD Gerla, Kwon and
Pei
Passive Clustering
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9/25/2000UCLA CSD Gerla, Kwon and
Pei
Passive Clustering
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9/25/2000UCLA CSD Gerla, Kwon and
Pei
Passive Clustering
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9/25/2000UCLA CSD Gerla, Kwon and
Pei
Passive Clustering
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Gateway
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9/25/2000UCLA CSD Gerla, Kwon and
Pei
Passive Clustering
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9/25/2000UCLA CSD Gerla, Kwon and
Pei
Passive Clustering
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9/25/2000UCLA CSD Gerla, Kwon and
Pei
Passive Clustering
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9/25/2000UCLA CSD Gerla, Kwon and
Pei
Passive Clustering
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9/25/2000UCLA CSD Gerla, Kwon and
Pei
Passive Clustering
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9/25/2000UCLA CSD Gerla, Kwon and
Pei
Passive Clustering
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9/25/2000UCLA CSD Gerla, Kwon and
Pei
Passive Clustering
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9/25/2000UCLA CSD Gerla, Kwon and
Pei
Passive Clustering
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9/25/2000UCLA CSD Gerla, Kwon and
Pei
Passive Clustering
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9/25/2000UCLA CSD Gerla, Kwon and
Pei
Passive Clustering
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9/25/2000UCLA CSD Gerla, Kwon and
Pei
Passive Clustering
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Resulting cluster structure.
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9/25/2000UCLA CSD Gerla, Kwon and
Pei
Lowest ID Clustering result
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3 isolated clouds – 1, 2, and the rest
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9/25/2000UCLA CSD Gerla, Kwon and
Pei
Simulation Environment (GloMoSim)
100 nodes in 1000m x 1000mTransmission range : 150mMobility model: Random Waypoint AODV unicast routingRandom Source/Destination Pairs CBR traffic. 512 bytes per packet, 0.4 packets per
sec
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9/25/2000UCLA CSD Gerla, Kwon and
Pei
Normalized Routing Overhead
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9/25/2000UCLA CSD Gerla, Kwon and
Pei
Mean End-to-End Delay
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9/25/2000UCLA CSD Gerla, Kwon and
Pei
Mean End-to-End Delay
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9/25/2000UCLA CSD Gerla, Kwon and
Pei
Throughput
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9/25/2000UCLA CSD Gerla, Kwon and
Pei
Throughput
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9/25/2000UCLA CSD Gerla, Kwon and
Pei
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
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Thank You!
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9/25/2000UCLA CSD Gerla, Kwon and
Pei
Chain Reaction (contd)
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9/25/2000UCLA CSD Gerla, Kwon and
Pei
Chain Reaction (contd)
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9/25/2000UCLA CSD Gerla, Kwon and
Pei
Chain Reaction (contd)
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9/25/2000UCLA CSD Gerla, Kwon and
Pei
Chain Reaction (contd)
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9/25/2000UCLA CSD Gerla, Kwon and
Pei
Passive Clustering
Pros and Cons Little line overhead ↔ Longer Convergence
time Free Neighbor info. ↔ Partial Neighbor Info. Better Structure Easy to Implement Energy Efficiency
Continued ..
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9/25/2000UCLA CSD Gerla, Kwon and
Pei
AODV (Ad Hoc On Demand DV) Routing application
AODV version with Hello messagesHello 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
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9/25/2000UCLA CSD Gerla, Kwon and
Pei
Passive Clustering features
Passive clustering with 802.11 Data traffic activated process
Clusterhead election rule – FDW Cluster time out : 2 sec
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9/25/2000UCLA CSD Gerla, Kwon and
Pei
Mean End-to-End Delay
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9/25/2000UCLA CSD Gerla, Kwon and
Pei
Chain Reaction set off by motion of node 1
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9/25/2000UCLA CSD Gerla, Kwon and
Pei
Final Clusters very different from the initial
ones
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