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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

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Outline

Introduction System Model Neighbor Discovery and Initial Cluster

Setup Topology Management Simulation Results Conclusion

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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 network using 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.

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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

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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.

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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.

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System Model

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.

Primary users on channel n

Cluster Head, master of channel n

Gateway

Ordinary node

Available channel list of a node

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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 clusterhead conveys 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.

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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.

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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

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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.

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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.

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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

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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.

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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.

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Simulation Result (cont’d)

Number of clusters under different algorithms, in stationary channel scenario.

Average cluster size under different algorithms, in stationary channel scenario

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In stationary channel condition, with various spectrum holes.

After ICS phase

Max Degree Algo

Lowest ID Algo

LMDS Algo

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Simulation Result (cont’d)

After LMDS AlgoBefore LMDS Algo

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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

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Comments

Combine the CR tech with wireless mesh network

But it focuses on the cluster formation Specific objective