1. Backbone Topology Synthesis for Multi- Radio Meshed Wireless LANs Huei-jiun Ju and Izhak Rubin University of California, Los angeles(UCLA) Keon Jang, SA Lab 2006. 10. 10

2. IntroductionRelated WorkMR-TSAMR-MBNRAnalysisBehaviorConclusion Outline 1. Introduction 2. Related Work 3. Multi Radio Topology Synthesis Algorithm 4. Multi Radio MBN On-Demand Routing 5. Performance Analysis 6. Performance Behavior 7. Conclusion 8. Q & A

3. IntroductionRelated WorkMR-TSAMR-MBNRAnalysisBehaviorConclusion Introduction Wireless mesh networks are employed for the purpose of extending the wireless coverage scope of the network. Using multiple radios in a collaborative manner dramatically improves system performance and functionality. Use of two or more radio modules at a device is becoming economically feasible.

4. IntroductionRelated WorkMR-TSAMR-MBNRAnalysisBehaviorConclusion Introduction This paper presents two following things A Multi-Radio Topology Synthesis Algorithm (MR-TSA) which is scalable and fully distributed algorithm that serves to autonomously elect certain Access Points (APs) as Backbone nodes to construct mesh backbone network. A Multi Radio MBN on-demand Routing (MR-MBNR) that is based on the MR-TSA. Assumptions Each AP Nodes has two radio modules: A high capacity radio module that is used for communications with other AP nodes. A low capacity radio module for communications with non-AP clients.

5. IntroductionRelated WorkMR-TSAMR-MBNRAnalysisBehaviorConclusion Backbone Network AP Nodes BN : Backbone Node BCN : Backbone Capable Node Non-AP Nodes RN : Regular Node Backbone Network (BNet) is formed by dynamically electing BCNs to BNs to form backbone links. Access Network (ANet) is BNs and its client nodes. Mobile Backbone Network : A multi-tier hierarchical architecture is constructed and employed for routing messages in a mobile ad hoc network. - Izhak Rubin et al.

6. IntroductionRelated WorkMR-TSAMR-MBNRAnalysisBehaviorConclusion Related Work CDS : Each node is either in the dominating set, or is adjacent to a node in the dominating set. Finding minimum CDS is NP-Hard problem. An algorithm that constructs a Connected Dominating Set (CDS) can be employed to synthesize the backbone layout. Constructing CDS in a distributed manner can be classified into two categories: size-efficient algorithms, time-efficient algorithms

7. IntroductionRelated WorkMR-TSAMR-MBNRAnalysisBehaviorConclusion Size Efficient Algorithms In general, size efficient algorithms divided into two phases. - Clustering Initially all nodes are white. If a node has higher degree than all of its white neighbor color itself to black All its white neighbors join in the cluster and change their color to grey - Finding gateways to connect the cluster-heads. Drawback - Convergence time : O(n) -> not scalable

8. IntroductionRelated WorkMR-TSAMR-MBNRAnalysisBehaviorConclusion Time Efficient Algorithms Some time-efficient algorithm are also executed in two phases as described previously, but main difference is a node claims itself as a cluster-head if it finds itself to have the highest degree/ID in its 1-hop neighbor hood. Drawback Do not construct a CDS has a constant approximation ratio to the size of the MCDS. Note that all of the mentioned backbone formation algorithm previously have been designed for ad-hoc network in which all nodes uses single radio.

9. IntroductionRelated WorkMR-TSAMR-MBNRAnalysisBehaviorConclusion Multi-Radio Topology Synthesis Algorithm 1.Hello Message 2.Association Algorithm 3.BCN to BN Conversion Algorithm 4.BN to BCN Conversion Algorithm 5.Restricting Conversion of BCN to BN Assumptions Each AP Nodes has two radio modules: A high capacity radio module that is used for communications with other AP nodes. A low capacity radio module for communications with non-AP clients.

10. IntroductionRelated WorkMR-TSAMR-MBNRAnalysisBehaviorConclusion Hello Message Every node has two timers: Short_Timer and Long_Timer. High capability channel Low capability channel Node’s ID Status Weight Associated BN ID Predecessor node ID BN neighbor list Node’s ID Status Weight Associated BN ID Predecessor node ID Number of hops to closest BN Contents of Hello Message Short_TimerLong_Timer BN,BCN Sends Hello Message on both channelUpdate neighbor list and clients list based on messages received. RN Sends Hello Message on the low channel Weight : based on its ID, degree, capability, or on some stability measure

11. IntroductionRelated WorkMR-TSAMR-MBNRAnalysisBehaviorConclusion Association Algorithm 1. Association in the high capacity channel BCN try to find a BN node with highest weight If no neighboring BN found select highest weight BCN as BN Selected node is identified as BN in the subsequent message 2. Multi-hop association in the low capacity channel BCN/RN try to find neighboring BN If no neighboring BN found the node attempts to identify a BCN v, the one that has the lowest advertised hop count to BN

12. IntroductionRelated WorkMR-TSAMR-MBNRAnalysisBehaviorConclusion BCN to BN Conversion Algorithm BCN to BN conversion will take place if the two BCN-to-BN conversion restriction rules are satisfied and one of following conditions are satisfied 1. Client Coverage If no neighboring BN neighbor and itself has highest weight among neighboring BCNs 2. Local 2-hop BNet connectivity At least a pair’s of BN neighbor of itself do not connect through common neighbor or directly 3. Local 3-hop BNet connectivity At least a pairs of indirect BN neighbor which means BN neighbor of its BCN neighbors do not connect directly or through common neighbor

13. IntroductionRelated WorkMR-TSAMR-MBNRAnalysisBehaviorConclusion BN to BCN Conversion Algorithm BN to BCN conversion takes place if all of the following conditions are satisfied 1.Client Coverage Any client has a BN neighbor other than itself 2.Local 2-hop BNet connectivity Any pair of BN neighbors are directly connected or have at least one other BN common neighbor 3.Local 3-hop BNet connectivity Any BN-neighbor and BCN-neighbor are either directly connected or have at least one common BN neighbor.

14. IntroductionRelated WorkMR-TSAMR-MBNRAnalysisBehaviorConclusion Restricting Conversions of BCN to BN Rule 1: A BCN should not convert to a BN if the number of its BN neighbors is higher than a threshold level, denoted as the BN_Neighbor_Limit Rule 2: A BCN should not convert to a BN if the number of its BN neighbors increases by at least one within the previous Short_Timer period.

15. IntroductionRelated WorkMR-TSAMR-MBNRAnalysisBehaviorConclusion Multi Radio MBN on-demand Routing Existing Approach Ad hoc On-demand Distance Vector (AODV) Dynamic Source Routing (DSR) Source initiates a flow to discover a source-destination route across network. Broadcast route request (PREQ) packet across the entire network. Proposed Approach Only BNs forward PREQ packets across both channels Only the BCN predecessors BCNs forward the PREQ packets across the low capacity channel Underlying route discovery overhead can be significantly reduced.

16. IntroductionRelated WorkMR-TSAMR-MBNRAnalysisBehaviorConclusion Performance Analysis 1.Size of Backbone Network 2.Message Overhead 3.Convergence and Time-complexity Assumptions The number and the distribution of backbone capable nodes is such that the sub-networks that contain only backbone capable nodes are topologically connected. The network graph topology stays unchanged during the time that it takes the MR-TSA to reach completion.

17. IntroductionRelated WorkMR-TSAMR-MBNRAnalysisBehaviorConclusion The Size of Backbone Network Theorem 3: The number of BN neighbors that a node can have is upper bounded by a constant value that is, with high probability, independent of the number of nodes in the network. (The probability that a BN has more than 11 BN neighbor is less than 5.1% in a very dense network.) Theorem 4: The size of the backbone network synthesized by MR-TSA is of the order of O(A), where A represents the size of the operational area, and is independent of the nodal density. According to theorem 3, the maximum number of BN neighbors of any BN is bounded by 11 at 95-percentile. Which also bounds the number of BNs covering the area.

18. IntroductionRelated WorkMR-TSAMR-MBNRAnalysisBehaviorConclusion Message Overhead Convergence Time and Time Complexity Theorem 5: The message complexity of the MR-TSA scheme is of the order of O(1) per node. From theorem 3 number of neighbor BN is bounded with high probability, thus message length is also bounded. Theorem 6: The convergence time of MBN topology synthesis algorithm is bounded by a constant value that is independent of the number of nodes in the network. Association process takes 12 cycle, analytically 9 cycle for BCN to BN conversion and experimentally 3 cycles for BN to BCN. (1 cycle is Long_Timer period) Multi-hop association process takes R/r cycles since RN needs BN information through at most R/r hops. (‘R’ represents a radius of high capacity radio and ‘r’ represents that of low capacity radio.

19. IntroductionRelated WorkMR-TSAMR-MBNRAnalysisBehaviorConclusion Performance Behavior 1.Backbone Network Performance Features 2.Throughput Performance Features 3.Performance Comparison Environment QualNet v3.6.1 as simulation environment Distributed Coordination Function of IEEE 802.11 as MAC-layer protocol The channel data rate is set to 2Mbps. The radio transmission range is about 300m. The Short_Timer is set to 2 seconds, the Long_Timer is set to 6 seconds. High capacity radio power is about 15dBm while 3 to 15dBm for low. Mesh network consists of 100 to 500 nodes in 1500m by 1500m area.

20. IntroductionRelated WorkMR-TSAMR-MBNRAnalysisBehaviorConclusion Backbone Network Performance From the graph (a) size of Backbone network stays almost constant as the number of nodes grows from 100 to 500 confirms scalability of MR-TSA From the second graph convergence time of MR-TSA scheme is not higher than 10 cycles, nicely under the upper bound 12 when r=R, and is independent of the number of network nodes or nodal density.

21. IntroductionRelated WorkMR-TSAMR-MBNRAnalysisBehaviorConclusion Backbone Network Performance Observed that average BN neighbor is stays constant under growing nodal density. As the transmit power is lowered, the maximum number of hops that it takes for a non-BN node to reach a BN across the low capacity channel increases.

22. IntroductionRelated WorkMR-TSAMR-MBNRAnalysisBehaviorConclusion Throughput Performance

23. IntroductionRelated WorkMR-TSAMR-MBNRAnalysisBehaviorConclusion Throughput Performance

24. IntroductionRelated WorkMR-TSAMR-MBNRAnalysisBehaviorConclusion Comparison Dai & Wu algorithm uses complete 2-hop neighbor information while MR-TSA uses complete 1-hop neighbor and 2-hop BN neighbor information. Results shows 2-hop complete neighbor information doesn’t have a noticeable impacts on size of the backbone network.

25. IntroductionRelated WorkMR-TSAMR-MBNRAnalysisBehaviorConclusion Comparison The high control overhead (Hello message rate) generated by the DW algorithm in case of dense network causes the data delivery ratio to drop (by about 30%) and the average end-to-end delay to increase.

26. IntroductionRelated WorkMR-TSAMR-MBNRAnalysisBehaviorConclusion Comparison Because of the 2 nd condition in BCN-TO-BN conversion rule, which ensures a path that is no longer than 2 hops between any pair of BN neighbors of a backbone capable node.

27. IntroductionRelated WorkMR-TSAMR-MBNRAnalysisBehaviorConclusion Presented a scalable and fully distributed algorithm for constructing mesh backbone network. Mathematically proved algorithms control overhead and convergence time bound. Exhibit result of performance evaluations that confirm the scalability and delay-throughput efficiency of the underlying multi-radio hierarchical network operation.