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Sensors 2013, 13(8), 11032-11050; doi:10.3390/s130811032

Energy-Efficient Cognitive Radio Sensor Networks: Parametric and Convex Transformations

ELCE Department, Ryerson University, 350- Victoria Street, Toronto, ON M5B 2K3, Canada
Department of Electrical Engineering, COMSATS Institute of IT, Wah Campus, Wah, Pakistan
Author to whom correspondence should be addressed.
Received: 8 July 2013 / Revised: 6 August 2013 / Accepted: 12 August 2013 / Published: 21 August 2013
(This article belongs to the Section Sensor Networks)
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Designing energy-efficient cognitive radio sensor networks is important to intelligently use battery energy and to maximize the sensor network life. In this paper, the problem of determining the power allocation that maximizes the energy-efficiency of cognitive radio-based wireless sensor networks is formed as a constrained optimization problem, where the objective function is the ratio of network throughput and the network power. The proposed constrained optimization problem belongs to a class of nonlinear fractional programming problems. Charnes-Cooper Transformation is used to transform the nonlinear fractional problem into an equivalent concave optimization problem. The structure of the power allocation policy for the transformed concave problem is found to be of a water-filling type. The problem is also transformed into a parametric form for which a ε-optimal iterative solution exists. The convergence of the iterative algorithms is proven, and numerical solutions are presented. The iterative solutions are compared with the optimal solution obtained from the transformed concave problem, and the effects of different system parameters (interference threshold level, the number of primary users and secondary sensor nodes) on the performance of the proposed algorithms are investigated.
Keywords: power allocation; nonlinear optimization; cognitive radio sensor network power allocation; nonlinear optimization; cognitive radio sensor network
This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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MDPI and ACS Style

Naeem, M.; Illanko, K.; Karmokar, A.; Anpalagan, A.; Jaseemuddin, M. Energy-Efficient Cognitive Radio Sensor Networks: Parametric and Convex Transformations. Sensors 2013, 13, 11032-11050.

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