1. A Method for Distributed Computation of Semi-Optimal Multicast Tree in MANET Eiichi Takashima, Yoshihiro Murata, Naoki Shibata*,Keiichi Yasumoto, and Minoru Ito.Nara Institute of Science and Technology, *Shiga University

2.

Background

Proposed method

Evaluation experiment

Conclusion

Outline of this presentation 3. Background

Video streaming - one of the most important application in mobile ad-hoc network (MANET)

Objective:Delivering video to many nodes in MANET

• Using a multicast tree

• SatisfyingQoS constraints

• • Bandwidth

• • Delay

• Optimized forany given objective

• • Power consumption (Mobile nodes are operated on battery)

• • Maximizing number of receiver nodes

4. Background

Optimizing multicast tree on MANET

• A hard task- an NP-hard problem

• Dynamicnetwork topology

• Limited capabilitiesof mobile terminals

• • Computation

• • Communication

5. Existing studies

P. Sinha, et al. [1]

• Distributed algorithm

• Good scalability

• Nohandling of multiple QoS constraints

• Nooptimization for a particular objective

Li Layuan, et al.[2]

• Centralized algorithm

• Optimizesany objectivewithmultiple QoSconstraints

• Poorscalability

• • Cost of gathering topology information

• • Centralized computation

[2] Li Layuan and Li Chunlin, "QoS Multicast Routing in Networks with Uncertain Parameters", APWeb, (2003). [1] P. Sinha and R. Sivakumar and V. Bharghavan, "MCEDAR: Multicast core extraction distributed ad-hoc routing", WCNC(1999), 6.

Background

Proposed method

Evaluation experiment

Conclusion

Outline of this presentation 7. Goal of this research

Constructingmulticast tree

• Satisfying allgiven QoS constraints

• Optimizing agiven objective

• • total power consumption

• • tree stability

Goodscalability

• Distributedcomputation

8. Our Approach

We use GA (Genetic Algorithm) to constructsemi-optimalmulticast tree

To realizedistributed computation

• we compute multicast tree onseveral nodes in parallelusing GA

• Each node solves a sub-tree for the whole multicast tree

We divide MANET intomultiple clusters

Advantageof using GA

• Quick computation using results of previous computation

• Especially when topology change is small

9. Hierarchical computation

Two tier computation : local trees and global tree

• A local tree connects nodes in a cluster

• The global tree connects clusters

cluster Global Tree node Local tree 10. Target Environment & Assumption

Service

• deliver smallvideo(oraudio ) datafroma sender nodetomultiple receiver nodesin MANET

• requirement : transmission rateB , tolerable end-to-end delayD

MAC protocolof wireless communication

• IEEE 802.11

Mobile nodes

• move at speed of 4 Km/hour (pedestrian)

• can measureavailablebandwidthanddelayto neighboring nodes

• can estimateapproximate distancesto neighboring nodes by strength of radio wave signals

11. Problem Definition

Input:

• topology info:G=(V,E ) , whereVis set of nodes,Eis set of links

• sender node:s V

• receiver nodes:R={r 1 ,r m } V

Output:

• Multicast tree:T=(V,E) ,whereV V, E E

Constraints:

• each linke Ehas availablebandwidth no less thanB

• total delayof each path in T isno more than D

Objective :

• maximizestabilityofT(links are connected for longer time)

• maximizeservice availability(more nodes can receive video)

• minimizetotal power consumption

• etc

12. Typical Objective Functions

Our method solves problem forintra-clusterandinter-clusterseparately usedifferentfunctions

Global Tree T: maximize F G

• F G= NumberOfReceivers ( T)

• NumberOf DelayViolation(T)

• +Stability (T)

Local Tree T: maximize F L

• F L=NumberOfReceivers (T)+ S tability(T)

• are coefficients.

Term for power consumption can also be added service availability Tree stability service availability Tree stability 13. ProcedurePhase1: Cluster division Cluster division Gathering topology infoin each cluster Gathering topology infobetween clusters Computation of global tree Cluster re-division Computationof local tree Inter cluster e e e e e S Intra cluster Cluster head: responsible to local tree construction Top cluster head: responsible to global tree construction e e e e e S 14. Phase2:Gathering Local Topology Info Cluster division Gathering topology infoin each cluster Gathering topology infobetween clusters Cluster re-division Inter cluster Intra cluster (1) Cluster head floods request msg in its cluster e e e e e S Computation of global tree Computationof local tree e e e e e S 15. Phase2:Gathering local topology Info Cluster Division Gathering topology infoin each cluster Gathering topology infobetween clusters Cluster re-division Inter cluster Intra cluster (1) Cluster head floods request msg in its cluster(2) Each nodereceived the message sends back a message with its ID and link state info including B/W and delay to neighboring nodes. e e e e e S Computation of global tree Computationof local tree e e e e e S 16. Phase3:Gathering global topology info Cluster Division Gathering topology infoin each cluster Gathering topology infobetween clusters Cluster re-division Inter cluster e e e e e S (1) Each cluster head measures QoS info on paths to cluster heads of adjacent clusters. (2) Each cluster head sends the info to the top cluster head. Intra cluster Computation of global tree Computationof local tree e e e e e S 17. Phase4: Computation of global tree Inter cluster Intra cluster e e e e e S Cluster Division Gathering topology infoin each cluster Gathering topology infobetween clusters Cluster re-division (1) Top cluster head (and some nodes) computes global tree by using island model GA. Computation of global tree Computationof local tree e e e e e S 18. Phase4: Computation of global tree Inter cluster Intra cluster Cluster Division Gathering topology infoin each cluster Gathering topology infobetween clusters Cluster re-division (1) Top cluster head (and some nodes) computes global tree by using island model GA. (2) Information of global tree is sent to each cluster head in the tree. Computation of global tree Computationof local tree e e e e e S e e e e e S 19. Phase5: Computation of local tree Cluster Division Gathering topology infoin each cluster Gathering topology infobetween clusters Cluster re-division Inter cluster e e e e e S Intra cluster Each cluster head computes local tree which can be grafted to global tree Computation of global tree Computationof local tree e e e e e S 20. Phase5: Computation of local tree Inter cluster Intra cluster Cluster Division Gathering topology infoin each cluster Gathering topology infobetween clusters Cluster re-division The island model GA is used for computation of local tree Computation of global tree Computationof local tree e e e e e S e e e e e S 21. Phase5: Computation of local tree Cluster Division Gathering topology infoin each cluster Gathering topology infobetween clusters Cluster re-division Inter cluster e e e e e S Intra cluster Computation of global tree Computationof local tree The info of local tree is sent to each node in the tree e e e e e S 22. Phase5: Computation of local tree Cluster Division Gathering topology infoin each cluster Gathering topology infobetween clusters Cluster re-division Inter cluster e e e e e S Intra cluster Computation of global tree Computationof local tree The semi-optimal multicast tree has been constructed among nodes. e e e e e S 23. Phase6: Cluster re-division Cluster Division Gathering topology infoin each cluster Gathering topology infobetween clusters Cluster re-division Inter cluster e e e e e S Intra cluster Computation of global tree Computationof local tree After a while, MANET is clustered again and procedure from phase2 is repeated to reflectchange of topology. e e e e e S 24.

Background

Proposed method

Evaluation

Conclusion

Outline of this presentation 25. Evaluation

Criteria

• Advantage of GAfor computing multicast tree

• Feasibilityin practical environment

• Superiorityto existing method

26. Advantage of the proposed algorithm

Objectiveis to investigate

• scalabilityagainst number of nodes

• efficiency of re-computation when topology changes

Experimental Configuration

• Mobility model of nodes

• • Random way point,4 Km/hour

• PC (laptop) for executing algorithm

• • CPU Intel(R) Pentium(R) M processor 1500MHz Windows XP cygwin 1.5.18 gcc version 3.4.4.

27. Result of (re)computation time of tree

Computation time :

• 6 sec for 800 nodes

• 1 secfor 100 nodes

Re-computation time

• shortened to60%

Seconds Computation time approximation of computation time Re-computation time approximation of recomputation time Number of nodes sufficient 28. Feasibility in practical environment

Evaluated the following points with 1000 nodes on 30 clusters (33 nodes per cluster)

• Computation cost

• Required bandwidth for MANET

Experimental result

• Computation time 0.04 second

• Needed bandwidth 6.3K bps

Proposed method is feasible in practical environment.

29. Superiority to existing method

Investigated performance of our method

Showsuperiorityto existing method

Index : transition of packet arrival rate as time progresses

Experimental configuration

• Area size3000 3000

• Number of nodes1000

• Simulator GTNetS

• Radio Range160m

• MAC layer protocolIEEE802.11 (Max. 2Mbps)

• Max of Speed4 Km/hour

• Mobility modelrandom waypoint

30. Comparison with existing method

AQM (on-demand multicast routing method)[3]

Proposed method

• Optimized forcommunication stability

• Optimized forthe number of receivers

• Optimized forpower consumption

[3]K. Bur and C. Ersoy. Ad Hoc Quality of Service Multicast Routing.Computer Communications , 29(1):136148, December 2005. Power saving stability number of receivers Yes No Yes Power-saving No No Yes #. of receivers No Yes Yes Stability 31. Transition of packet arrival rate The proposed method issuperiorto AQM in terms of packet arrival rate second AQM Stability #. of receivers Power-saving 32. Conclusion

We proposed a new multicast routing method for MANET.

• To construct the semi-optimal multicast tree satisfying several QoS constraints for any given objective

We show that the proposed method is feasible in practical environment.

33.

The End

34. Result of power consumption Unit : Watt-second 35. Power consumption

Compared item

• Transmission power consumption in 20 seconds

• • 20 seconds : reconstruction interval of multicast tree