Energy Minimization Approach for Cooperative Spectrum Sensing in Cognitive Radio Wireless Sensor Networks

  • I. Mustapha Department of Electrical and Electronics Engineering, University of Maiduguri, Nigeria
  • D. Mustapha Department of Computer Engineering University of Maiduguri, Nigeria
  • M. Abbagana Department of Electrical and Electronics Engineering, University of Maiduguri, Nigeria

Abstract

Cooperative spectrum sensing is a promising method for improving spectrum sensing performance in cognitive radio. Although it yields better spectrum sensing performance, it also incurs additional energy consumption that drains more energy from the sensor nodes and hence shortens the lifetime of sensor networks. This paper proposes energy minimization approach to reduce energy consumption due to spectrum sensing and sensed result reporting in a cooperative spectrum sensing. The approach determines optimal number of cooperative sensing nodes using particle swarm optimization. We derived mathematical lower bound and upper bound for the number of cooperative sensing nodes in the network. Then we formulate a constraint optimization problem and used particle swarm optimization to simultaneously optimize the two mathematical bounds to determine the optimal number of sensing nodes. Simulation results indicate viability of the proposed approach and show that significant amount of energy savings can be achieved by employing optimal number of sensing nodes for cooperative spectrum sensing. Performance comparison with conventional approach shows performance improvement of the proposed approach over the conventional method in minimizing spectrum sensing energy consumption without compromising spectrum sensing performance.

Published
Aug 1, 2017
How to Cite
MUSTAPHA, I.; MUSTAPHA, D.; ABBAGANA, M.. Energy Minimization Approach for Cooperative Spectrum Sensing in Cognitive Radio Wireless Sensor Networks. Arid Zone Journal of Engineering, Technology and Environment, [S.l.], v. 13, n. 4, p. 467-477, aug. 2017. ISSN 2545-5818. Available at: <http://azojete.com.ng/index.php/azojete/article/view/202>. Date accessed: 13 dec. 2017.
Section
Articles