Distributed Computation of Semi-Optimal Multicast Tree in MANET
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The document proposes a distributed method for constructing semi-optimal multicast trees in mobile ad hoc networks (MANETs) that satisfies quality of service constraints and optimizes a given objective such as power consumption or stability. The method uses genetic algorithms to compute local and global multicast trees in a hierarchical, distributed manner. Evaluation experiments found the method scales well to large networks and recomputes trees more efficiently than existing approaches in response to topology changes.
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Distributed Computation of Semi-Optimal Multicast Tree in MANET
- 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
- 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 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 info in each cluster Gathering topology info between 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 Computation of local tree e e e e e S
- 15. Phase2: Gathering local topology Info Cluster Division Gathering topology info in each cluster Gathering topology info between clusters Cluster re-division Inter cluster 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 e e e e e S
- 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 Computation of global tree Computation of 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 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 e e e e e S
- 18. Phase4: Computation of global tree Inter cluster Intra cluster 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 e e e e e S
- 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 Intra cluster Each cluster head computes local tree which can be grafted to global tree Computation of global tree Computation of local tree e e e e e S
- 20. Phase5: Computation of local tree Inter cluster Intra cluster 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 e e e e e S
- 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 Intra cluster Computation of global tree Computation of 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 info in each cluster Gathering topology info between clusters Cluster re-division Inter cluster 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. e e e e e S
- 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 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. e e e e e S
- 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 公
- 34. Result of power consumption Unit : Watt-second
Notas del editor
- Thank you chair. I'd like to have a talk titled A Method for Distributed Computation of Semi-Optimal Multicast Tree in MANET.
- This is the outline of this presentation. First, I’d like to explain the research background.
- Video streaming service is considered to be one of the most important application in mobile ad-hoc network. And, the objective of our study is to deliver video to many nodes on MANET. In order to deliver video to many mobile nodes in MANET, we use a multicast tree. Constraints regarding to bandwidth and delay have to be satisfied to deliver video smoothly. Since mobile nodes are operated on battery, we also want to optimize power consumption or other objectives.
- But, optimizing multicast tree in MANET is very hard since It is an NP-hard problem. Also, topology of MANET dynamically changes, and computation and communication capabilities of mobile nodes are limited
- There are some existing studies on multicast tree construction. The algorithm proposed by Sinha is a distributed algorithm with good scalability. But, this method does not handle more than one QoS constraints, and there is no optimization for a particular objective. Layuan has proposed a centralized algorithm which optimizes multiple QoS constraints. However, it lacks scalability due to centralized computation and the cost of gathering topology information from all nodes.
- Next, I would like to explain the proposed method.
- We are aiming at making an algorithm to construct multicast tree which satisfies all given QoS constraints and optimized for a given objective such as total power consumption or tree stability. We would like to realize good scalability by making our algorithm distributed.
- In order to achieve the goal, we use a genetic algorithm to make a semi-optimal multicast tree. Since GA is easy to parallelize, we use several GAs, each of which optimizes part of the whole tree. We divide the whole network into multiple clusters. The advantage of using GA is that it quickly computes a tree utilizing results of previous computations. This is prominent especially if topology change since the last computation is small.
- We find the multicast tree using two tier computation. The upper tier finds a global tree, and the lower tier finds local trees. A local tree connects nodes in a cluster, and the global tree connects clusters.
- Next, I will explain about target environment and assumption. We suppose to realize a service which delivers small video or audio data from a sender node to multiple receiver nodes in MANET. As a requirement, we suppose that transmission rate of video is B and tolerable end-to-end delay is D. As a MAC layer protocol of wireless communication, IEEE 802.11 is supposed. Nodes transmit and receive small video and/or voice data. We assume that mobile nodes are pedestrians and move at around four kilo meter per hour. We also assume that each mobile node can measure available bandwidth and delay to neighboring nodes, and can estimate approximate distances to neighboring nodes by strength of radio wave signals. Mobile nodes are pedestrians at speed of around 4 km/hour. Each node can send and receive packets through wireless network interface. QoS information (bandwidth, delay) is measurable. Each node can know its own moving speed and a rough distance to other nodes from the strength of their radio wave signals.
- Next, I will define the problem. The input of the problem is the topology information G, sender node s, and receiver nodes R. The output is a multicast tree T which is a sub graph of G. The multicast tree T has to satisfy the following constraints. That is, each link e has available bandwidth no less than B, and total delay time of each path in T is no more than D. There may be many trees satisfying the constraints. So, we set the objective function such as maximizing stability of T, maximizing service availability, minimizing total power consumption, and so on.
- Here, I show the typical objective function which were also used for our experiments. Since the proposed method solves the problem for intra-cluster and inter-cluster, we use different objective functions. For global tree T’, we maximize the following function F_G. For local tree T’’, we maximize the following function F_L. Both functions consider the service availability and tree stability with appropriate ratio. The difference is the existence of this term in F_G. This term excludes paths with longer delay since delay is more sensitive in global tree. A term for power consumption can also be added to these functions.
- Next, I will explain the procedure to compute multicast tree. The proposed method carries out the following six phases, The phase 1 divides MANET to clusters with existing clustering algorithm. In this phase, a top cluster head is selected for each cluster and a top cluster head is selected among the cluster heads. Cluster head is responsible to local tree construction, and top cluster head is responsible to global tree construction.
- In the phase 2, topology information in each cluster is gathered to the cluster head. For this purpose, first cluster head floods request message in its cluster.
- Then, each node received the message sends back a message with its ID and link state info including B/W and delay to neighboring nodes.
- In the phase 3, topology information between clusters is gathered to the top cluster head. For the purpose, first, each cluster head measures QoS information on paths to cluster heads of the adjacent clusters. Next, each cluster head sends the information to the top cluster head.
- In the phase 4, the top cluster head and some nodes compute the global tree by using island model GA.
- Then, the information of the global tree is sent to each cluster head in the tree.
- In the phase 5, each cluster head computes the local tree which can be grafted to the global tree computed in phase 4.
- The island model GA is used for computation of the local tree.
- The information of the local tree is sent to each node in the tree.
- At this time, the semi-optimal multicasting tree has been constructed among nodes like this.
- After a while, MANET is clustered again and procedure from phase 2 is repeated to reflect change of topology.
- Next, I will explain about evaluation of the proposed method.
- Criteria of evaluation are as follows. First, we evaluated advantage of GA for computing multicast tree. Second, we evaluated Feasibility on practical environment . Finally, we evaluated superiority of the proposed method to existing multicast routing method.
- Here, I will talk about the advantage of our GA-based algorithm for computing multicast tree. Objective here is to investigate the scalability against number of nodes, and the efficiency of re-computation of tree when topology changes In the experiment, we used the following configuration. As mobility model of nodes, we used random way point where maximum moving speed is four kilo meter per hour. We used a PC of this specification (指しながら) for executing the algorithm.
- The figure shows that result of computation time. Here, horizontal axis indicates the number of nodes and the vertical axis shows the time in seconds. Blue line shows computation time of tree, and red line shows re-computation time. We see that even when the number of nodes is 800, the computation time is about six seconds. Since we divide the MANET to clusters, each cluster contains at most 100 nodes. In this case, our algorithm compute the tree in one second. Therefore, we believe that the computation time is short enough. Also, we see that re-computation time is shortened to 60 percent of the computation of tree from scratch. This is one of the advantages of using GA.
- Next I will talk about the feasibility of proposed method on practical environment. Here, we supposed MANET consisting of 1000 nodes divided to 30 clusters. According to experimental result, required computation time and communication cost were 0.04 seconds and 6.3 kilo bytes, respectively. This shows that proposed method is feasible on practical environment.
- Finally, I will talk about the superiority of the proposed method to existing method. We conducted experiments to investigate performance of our method and show superiority to existing method. As the index, we used transition of The Packet Arrival Rate as time progresses. Experimental configuration is as follows. The number of nodes is 1000. We used network simulator as GTNetS. moving speed of nodes is 4 kilo meter / hour.
- We compared packet arrival rate of the proposed method with existing method AQM. AQM is an on-demand multicast routing method which constructs a multicast tree with required bandwidth. Since our method can use any objective function, we used the following three different objective functions called stable, non-stable, and power-saving. Here, stable is a function which has heavier weight in tree stability than non-stable. power-saving is a function with a new term for power consumption.
- This figure shows Transition of The Packet Arrival Rate over time at 4 km/h. Vertical axis shows packet arrival rate, and Horizontal axis represents simulation time. Red line represents AQM. Green line, blue line and pink line represent proposed method using these objective functions, stable, non-stable and power-saving, respectively. We see that the proposed method of any objective function is superior to AQM in terms of packet arrival rate. Especially, we see that our method with function stable constructed the most stable multicast tree.
- we proposed a new multicast routing method for MANET, which constructs the semi-optimal multicast tree satisfying QoS constraints for any given objective, which is satisfying two or more QoS restrictions. We show that proposed method is feasible in practical environment.
- This result shows that The power consumption among those methods was that stable : AQM : power-saving = 10.095 : 6.7639 : 6.04317. Consequently, the result of the power-saving method showed power consumption fewer than AQM and the stable method.
- We compared the power consumption for data streaming through the multicast trees constructed by our method and AQM for 20 seconds. 20 seconds means reconstruction interval of multicast tree in proposed method.