Topology Management in CogMesh : A Cluster-Based Cognitive Radio Mesh Network

1. Topology Management in CogMesh: A Cluster-Based Cognitive Radio Mesh Network Tao Chen; Honggang Zhang; Maggio, G.M.; Chlamtac, I.; Communications, 2007. ICC '07. IEEE International Conference

2. Outline • Introduction • System Model • Neighbor Discovery and Initial Cluster Setup • Topology Management • Simulation Results • Conclusion

3. Introduction • Focus on the networking formation issue of a cognitive radio based ad hoc network • A number of primary users and secondary users form a mesh type networkusing the detected unoccupied frequency band • CogMesh network can be regarded as a multichannel multi-access network • in which the available channels of a node undergoes dynamic changes during the node’s life time.

4. Introduction (cont’d) • Topology management of a CogMesh network is affected by two main facts: • A common control channel may not always exist in the whole network • The network topology changes over time according to the presence of primary users and secondary users • Related work • In [5], a distributed grouping scheme is thus proposed to solve the common control channel problem. • Nevertheless, an efficient neighbor discovery process, which is critical for open spectrum access networks, is not found [5] J. Zhao, H. Zheng, and G. Yang, “Distributed Coordination In Dynamic Spectrum Allocation Networks,” Dyspan 2005

5. Introduction (cont’d) • Considering the nature of CogMesh, • we employ a cluster based approach to solve this problem for the following reason • the nodes in CogMesh networks can be grouped according to the spectrum hole distribution • therefore, the cluster based approach can ease the spectrum management task. • Related works’ problem [6][7] • They usually assume a single channel radio on each node • They are designed for fixed network topology • hence lack the ability to adapt to dynamic physical topology changes. • Most of them only guarantee the network connectivity. • The cluster configuration may not be optimized. • Some approaches need the full topology of the network. [6] C. Lin and M. Gerla, “Adaptive Clustering For Mobile Wireless Networks,” Selected Areas in Communications, IEEE Journal on, vol. 15, no. 7, pp. 1265–1275, 1997 [7] A. Amis, R. Prakash, T. Vuong, and D. Huynh, “Max-Min d-Cluster Formation In Wireless Ad Hoc Networks,” INFOCOM 2000, IEEE, vol. 1, 2000.

6. Introduction (cont’d) • In this paper, • we propose a distributed cluster-based approach to provide efficient communications in a large scale cognitive radio based mesh network. • The proposed mechanism is able to adapt the network topology to network and radio environment changes.

7. System Model  Primary users on channel n  Cluster Head, master of channel n  Gateway  Ordinary node  Available channel list of a node • Although this kind of model is not realistic in the physical world, it provides adequate abstract to study network formation issues at the link layer.

8. System Model (cont’d) • MAC protocol for CogMesh (mainly: forming the clusters) • Channel access time in each cluster is divided into a sequence of superframes • The beacon period issued by the clusterheadconveys the cluster ID and cluster control information • The neighbor broadcast period (NBP) is used by each cluster member to broadcast its node ID, cluster ID, cluster size, and 1-hop neighbor list in an allocated mini-slot. • The private random access period (RAP) is a slotted period for cluster members exchanging control messages. • The public RAP is used for clusterheads exchanging inter-cluster control messages • such as neighbor list exchanging.

9. Neighbor Discovery and Initial Cluster Setup • Initial cluster setup phase • Those nodes form clusters and make inter-connection • Each node listens and detects one of its spectrum holes during a given period of time, waiting for beacons on that spectrum hole. • Usually, the node orders its channels with frequency and starts its detecting process from the lowest one. • Three cases occurs during a listening interval • no message comes • a beacon comes in the listen interval • neighbor messages come but no beacon comes • The ICS phase stops when all initial nodes join clusters and clusters form interconnections.

10. Topology Management • Several motivations for topology management • The random nature of the ICS phase makes the formed clusters hardly being optimized in line with the physical topology. • In the cognitive radio scenario, the available channels for each node fluctuate with regard to the radio environment. • Since few cluster number means few inter-cluster communication and few hops to reach other nodes • reducing the cluster number becomes the optimization goal

11. Topology Management (cont’d) • The clustering optimization problem can be considered as a dominating set (DS) problem • consists of finding a subset of nodes with the following properties • each node is either in the DS, or is adjacent to a node in the DS • In CogMesh, the DS is the collection of clusterheads. • The cluster optimization problem • to find a minimal dominating set (MDS) of the CogMesh network according to its physical topology.

12. Topology Management (cont’d) • The heuristic algorithm is run periodically and distributively on each node • only relies on the discovered neighbor information • to determine the locally optimized cluster configuration • The physical topology changes due to the events such as • new nodes joining the network • nodes leaving the network • radio environment changing • The affected nodes or clusters are reconfigured to absorb the changes.

13. The objective is to construct clusters based on a MDS of the graph GA = (VA,EA), so that the number of clusters in VA can be minimized.

14.  Find the channel with maximal degree  Find the residual node with maximal degree  Form a new cluster

15. Topology Management (cont’d) • The stability and overhead of the algorithm • If the algorithm changes the network topology too often, the network may be unstable for service support • The frequent running of the algorithm produce additional control overhead • Solution • The time interval to activate the algorithm on each node can be properly chosen so that a balance is achieved among the agility, stability, overhead reduction.

16. Simulation Result • Setup: • Traffic: Poisson distribution, • CogMesh network are randomly placed in • a 600m × 600m 2-dimension square • The maximum transmission range of a node is set to 100m. • The square is divided into 16 equal size sub-squares. • Secondary users in the same sub-square share identical available channels.

17. Simulation Result (cont’d) Average cluster size under different algorithms, in stationary channel scenario Number of clusters under different algorithms, in stationary channel scenario.

18. In stationary channel condition, with various spectrum holes. Max Degree Algo After ICS phase LMDS Algo Lowest ID Algo

19. Simulation Result (cont’d) Before LMDS Algo After LMDS Algo

20. Conclusion • The CogMesh networks opportunistically utilize the spectra resources for communication • thus provide unique features different from traditional wireless mesh networks • propose a cluster-based approach for the neighbor discovery • provide a topology management algorithm for the topology optimization

21. Comments • Combine the CR tech with wireless mesh network • But it focuses on the cluster formation • Specific objective