Clustering algorithms for Cognitive Radio networks: A survey

Cognitive Radio (CR) networks enable unlicensed or Secondary Users (SUs) to sense for and operate in the underutilized spectrum (or white spaces) owned by licensed or Primary Users (PUs) without causing unacceptable interference to the PUs' activities. Clustering, which is a topology management mechanism, organizes nodes into logical groups in order to provide network-wide performance enhancement. Clustering aims to achieve network scalability and stability, as well as to support cooperative tasks, such as channel sensing and channel access, which are essential to CR operations. While clustering has been well investigated in traditional networks such as mobile ad hoc networks, similar investigations in CR networks remain in the infancy stage. New clustering algorithms must be designed to address new challenges associated with the intrinsic characteristics of CR, namely the dynamicity of channel availability that changes with time and location. This article reviews clustering algorithms, and they are characterized by clustering objectives, metrics and the number of hops in each cluster. We also present complexity analysis, performance enhancements achieved by the clustering algorithms, as well as open issues, in order to establish a foundation for further research and to spark new research interests in this area. Clustering organizes nodes into logical groups to provide scalability, stability and cooperative tasks support, and the dynamicity of channel availability in Cognitive Radio networks has brought about challenges to clustering. This article presents an extensive review on various aspects of clustering algorithms in Cognitive Radio networks, including clustering objectives, characteristics, performance enhancements, complexity analysis, and open issues. Of particular focus is clustering metrics and how these metrics have been applied to form clusters in Cognitive Radio networks.Display Omitted We review clustering algorithms for Cognitive Radio networks.We present taxonomy of the attributes of clustering algorithms.We present advantages, challenges, objectives and characteristics of clustering.We compare algorithms in terms of complexity and performance enhancement.We propose open issues for further research.