Keon Jang, SA Lab 2006. 10. 10

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

2. 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. 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. 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. Backbone Network 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. 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.

6. Related Work An algorithm that constructs a Connected Dominating Set (CDS) can be employed to synthesize the backbone layout. 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. Constructing CDS in a distributed manner can be classified into two categories: size-efficient algorithms, time-efficient algorithms

7. 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. 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. Multi-Radio Topology Synthesis Algorithm • Hello Message • Association Algorithm • BCN to BN Conversion Algorithm • BN to BCN Conversion Algorithm • 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. Hello Message • Every node has two timers: Short_Timer and Long_Timer. Contents of Hello Message Weight : based on its ID, degree, capability, or on some stability measure

11. 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 associationin 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. 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. Local3-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. BN to BCN Conversion Algorithm • BN to BCN conversion takes place if all of the following conditions are satisfied • Client Coverage Any client has a BN neighbor other than itself • Local 2-hop BNet connectivity Any pair of BN neighbors are directly connected or have at least one other BN common neighbor • Local 3-hop BNet connectivity Any BN-neighbor and BCN-neighbor are either directly connected or have at least one common BN neighbor.

14. 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. 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. Performance Analysis • Size of Backbone Network • Message Overhead • 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. 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. 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. Performance Behavior • Backbone Network Performance Features • Throughput Performance Features • 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. 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. 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. Throughput Performance

23. Throughput Performance

24. 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. 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. Comparison Because of the 2nd 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. Conclusion • 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.