SMART: A SpectruM-Aware clusteR-based rouTing scheme for distributed cognitive radio networks

Saleem, Yasir * and Yau, Alvin Kok-Lim * and Hafizal Mohamad, and Nordin, Ramli and Rehmani, Mubashir Husain (2015) SMART: A SpectruM-Aware clusteR-based rouTing scheme for distributed cognitive radio networks. Computer Networks, 91. pp. 196-224. ISSN 1389-1286

smart.pdf - Accepted Version
Available under License Creative Commons Attribution Non-commercial No Derivatives.

Download (3MB) | Preview
Official URL:


Cognitive radio (CR) is the next-generation wireless communication system that allows unlicensed users (or secondary users, SUs) to exploit the underutilized spectrum (or white spaces) in licensed spectrum while minimizing interference to licensed users (or primary users, PUs). This article proposes a SpectruM-Aware clusteR-based rouTing (SMART) scheme that enables SUs to form clusters in a cognitive radio network (CRN) and enables each SU source node to search for a route to its destination node on the clustered network. An intrinsic characteristic of CRNs is the dynamicity of operating environment in which network conditions (i.e., PUs’ activities) change as time goes by. Based on the network conditions, SMART enables SUs to adjust the number of common channels in a cluster through cluster merging and splitting, and searches for a route on the clustered network using an artificial intelligence approach called reinforcement learning. Simulation results show that SMART selects stable routes and significantly reduces interference to PUs, as well as routing overhead in terms of route discovery frequency, without significant degradation of throughput and end-to-end delay.

Item Type: Article
Additional Information: First and 2nd authors are with the Dept. Computing and Information Systems, Sunway University; 3rd and 4th authors are with Wireless Network and Protocol Research Lab, MIMOS Berhad; 5th author is with COMSATS Institute of Technology, Pakistan.
Uncontrolled Keywords: cognitive radio; clustering; routing; reinforcement learning; cluster merging; cluster splitting
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: Sunway University > School of Engineering and Technology [formerly School of Science and Technology until 2020] > Dept. Computing and Information Systems
Depositing User: Ms. Molly Chuah
Related URLs:
Date Deposited: 28 Mar 2016 02:34
Last Modified: 12 Oct 2020 07:39

Actions (login required)

View Item View Item