Research Article
Construction of a Robust Clustering Algorithm for Cognitive Radio Ad-Hoc Network
@INPROCEEDINGS{10.1007/978-3-319-24540-9_63, author={Nafees Mansoor and A. Islam and Mahdi Zareei and Sabariah Baharun and Shozo Komaki}, title={Construction of a Robust Clustering Algorithm for Cognitive Radio Ad-Hoc Network}, proceedings={Cognitive Radio Oriented Wireless Networks. 10th International Conference, CROWNCOM 2015, Doha, Qatar, April 21--23, 2015, Revised Selected Papers}, proceedings_a={CROWNCOM}, year={2015}, month={10}, keywords={Cognitive radio networks Ad-hoc networks Cluster-based network Network architecture Re-clustering}, doi={10.1007/978-3-319-24540-9_63} }- Nafees Mansoor
A. Islam
Mahdi Zareei
Sabariah Baharun
Shozo Komaki
Year: 2015
Construction of a Robust Clustering Algorithm for Cognitive Radio Ad-Hoc Network
CROWNCOM
Springer
DOI: 10.1007/978-3-319-24540-9_63
Abstract
With the swift expansion of wireless technologies, the demand for radio spectrum is ever growing. Besides the spectrum scarcity issue, spectrums are also underutilized. Cognitive radio customs an open spectrum allocation technique, which ensures efficient handling of the frequency bands. However, suitable network architecture is must for the implementation of cognitive radio networks. This paper presents a robust cluster-based architecture for cognitive radio ad-hoc network. Considering the spatial variance of the spectrum, the proposed architecture splits the network into groups of cluster. Set of free common channels resides every cluster that enables smooth shifting among control channels. The paper also introduces a parameter called Cluster Head Determining Factor (CHDF) to select cluster-heads. Each cluster comprises of a secondary cluster-head to combat the re-clustering issue for mobile nodes. Conclusively, to evaluate the performance of the proposed architecture, simulation is conducted and comparative studies are performed. From the simulation result, it is found that the proposed cluster-based architecture outperforms other recently developed clustering approaches by upholding a reduced number of clusters in the network.
