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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 Slide 2 1.Background 2.Proposed method 3.Evaluation experiment 4.Conclusion Outline of this presentation Slide 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 Satisfying QoS constraints Bandwidth Delay Optimized for any given objective Power consumption (Mobile nodes are operated on battery) Maximizing number of receiver nodes Slide 4 Background Optimizing multicast tree on MANET A hard task - an NP-hard problem Dynamic network topology Limited capabilities of mobile terminals Computation Communication Slide 5 Existing studies P. Sinha, et al. [1] Distributed algorithm Good scalability No handling of multiple QoS constraints No optimization for a particular objective Li Layuan, et al.[2] Centralized algorithm Optimizes any objective with multiple QoS constraints Poor scalability 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), Slide 6 1.Background 2.Proposed method 3.Evaluation experiment 4.Conclusion Outline of this presentation Slide 7 Goal of this research Constructing multicast tree Satisfying all given QoS constraints Optimizing a given objective total power consumption tree stability Good scalability Distributed computation Slide 8 Our Approach 1.We use GA (Genetic Algorithm) to construct semi- optimal multicast tree 2.To realize distributed computation we compute multicast tree on several nodes in parallel using GA Each node solves a sub-tree for the whole multicast tree We divide MANET into multiple clusters Advantage of using GA Quick computation using results of previous computation Especially when topology change is small Slide 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 Slide 10 Target Environment & Assumption Service deliver small video (or audio) data from a sender node to multiple receiver nodes in MANET requirement: transmission rate B, tolerable end-to-end delay D MAC protocol of wireless communication IEEE 802.11 Mobile nodes move at speed of 4 Km/hour (pedestrian) can measure available bandwidth and delay to neighboring nodes can estimate approximate distances to neighboring nodes by strength of radio wave signals Slide 11 Problem Definition Input: topology info: G=(V,E ), where V is set of nodes, E is set of links sender node: s V receiver nodes: R={r 1,r m } V Output: Multicast tree: T=(V,E), where V V, E E Constraints: each link e E has available bandwidth no less than B total delay of each path in T is no more than D Objective: maximize stability of T (links are connected for longer time) maximize service availability (more nodes can receive video) minimize total power consumption etc Slide 12 Typical Objective Functions Our method solves problem for intra-cluster and inter-cluster separately use different functions Global Tree T: maximize F G F G = NumberOfReceivers(T) NumberOfDelayViolation(T) + Stability(T) Local Tree T: maximize F L F L = NumberOfReceivers(T) + Stability(T) are coefficients. Term for power consumption can also be added service availability Tree stability service availability Tree stability Slide 13 Procedure Phase1: Cluster division Cluster division Gathering topology info in each cluster Gathering topology info between clusters Computation of global tree Cluster re-division Computation of local tree Inter cluster e e e e e S e e e e e S Intra cluster Cluster head: responsible to local tree construction Top cluster head: responsible to global tree construction Slide 14 Phase2: Gathering Local Topology Info Cluster division Gathering topology info in each cluster Gathering topology info between clusters Cluster re-division Inter cluster e e e e e S Intra cluster (1) Cluster head floods request msg in its cluster e e e e e S Computation of global tree Computation of local tree Slide 15 Phase2: Gathering local topology Info Cluster Division Gathering topology info in each cluster Gathering topology info between clusters Cluster re-division Inter cluster e e e e e S Intra cluster (1) Cluster head floods request msg in its cluster (2) Each node received 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 Computation of local tree Slide 16 Phase3: Gathering global topology info Cluster Division Gathering topology info in each cluster Gathering topology info between 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 e e e e e S Computation of global tree Computation of local tree Slide 17 Phase4: Computation of global tree Inter cluster Intra cluster e e e e e S e e e e e S Cluster Division Gathering topology info in each cluster Gathering topology info between clusters Cluster re-division (1) Top cluster head (and some nodes) computes global tree by using island model GA. Computation of global tree Computation of local tree Slide 18 Phase4: Computation of global tree Inter cluster Intra cluster e e e e e S Cluster Division Gathering topology info in each cluster Gathering topology info between 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 Computation of local tree e e e e e S Slide 19 Phase5: Computation of local tree Cluster Division Gathering topology info in each cluster Gathering topology info between clusters Cluster re-division Inter cluster e e e e e S 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 Computation of local tree Slide 20 Phase5: Computation of local tree Inter cluster Intra cluster e e e e e S Cluster Division Gathering topology info in each cluster Gathering topology info between clusters Cluster re-division The island model GA is used for computation of local tree Computation of global tree Computation of local tree e e e e e S Slide 21 Phase5: Computation of local tree Cluster Division Gathering topology info in each cluster Gathering topology info between clusters Cluster re-division Inter cluster e e e e e S e e e e e S Intra cluster Computation of global tree Computation of local tree The info of local tree is sent to each node in the tree Slide 22 Phase5: Computation of local tree Cluster Division Gathering topology info in each cluster Gathering topology info between clusters Cluster re-division Inter cluster e e e e e S e e e e e S Intra cluster Computation of global tree Computation of local tree The semi-optimal multicast tree has been constructed among nodes. Slide 23 Phase6: Cluster re-division Cluster Division Gathering topology info in each cluster Gathering topology info between clusters Cluster re-division Inter cluster e e e e e S e e e e e S Intra cluster Computation of global tree Computation of local tree After a while, MANET is clustered again and procedure from phase2 is repeated to reflect change of topology. Slide 24 1.Background 2.Proposed method 3.Evaluation 4.Conclusion Outline of this presentation Slide 25 Evaluation Criteria Advantage of GA for computing multicast tree Feasibility in practical environment Superiority to existing method Slide 26 Advantage of the proposed algorithm Objective is to investigate scalability against 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. Slide 27 Result of (re)computation time of tree Computation time : 6 sec for 800 nodes 1 sec for 100 nodes Re-computation time shortened to 60% Seconds Computation time approximation of computation time Re-computation time approximation of recomputation time Number of nodes sufficient Slide 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. Slide 29 Superiority to existing method Investigated performance of our method Show superiority to existing method Index: transition of packet arrival rate as time progresses Experimental configuration Area size 3000 3000 Number of nodes 1000 Simulator GTNetS Radio Range 160m MAC layer protocol IEEE802.11 (Max. 2Mbps) Max of Speed 4 Km/hour Mobility model random waypoint Slide 30 Comparison with existing method AQM (on-demand multicast routing method)[3] Proposed method Optimized for communication stability Optimized for the number of receivers Optimized for power consumption number of receivers stabilityPower saving Stability Yes No #. of receivers Yes No Power-saving Yes No Yes [3]K. Bur and C. Ersoy. Ad Hoc Quality of Service Multicast Routing. Computer Communications, 29(1):136148, December 2005. Slide 31 Transition of packet arrival rate The proposed method is superior to AQM in terms of packet arrival rate second AQM Stability #. of receivers Power-saving Slide 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. Slide 33 The End Slide 34 Result of power consumption Unit : Watt-second Slide 35 Power consumption Compared item Transmission power consumption in 20 seconds 20 seconds : reconstruction interval of multicast tree