Joint Spectrum Sensing and Data Transmission Optimization for Energy Efficiency in Cognitive Radio Sensor Networks:  A Dynamic Cooperative Method

Yi Li; Jun Peng; Fu Jiang; Kaiyang Liu; Xiaoyong Zhang

doi:10.20965/jaciii.2015.p0197

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JACIII Vol.19 No.2 pp. 197-204

doi: 10.20965/jaciii.2015.p0197

(2015)

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Joint Spectrum Sensing and Data Transmission Optimization for Energy Efficiency in Cognitive Radio Sensor Networks: A Dynamic Cooperative Method

Yi Li, Jun Peng, Fu Jiang, Kaiyang Liu, and Xiaoyong Zhang

School of Information Science and Engineering, Central South University
Changsha, Hunan 400075, China

Received:

June 25, 2014

Accepted:

November 14, 2014

Published:

March 20, 2015

Keywords:

cooperative spectrum sensing, energy efficiency, dynamic censoring, cognitive radio sensor networks

Abstract

To address the inherent energy constraint in cognitive radio sensor networks, a novel joint optimization method of spectrum sensing and data transmission for energy efficiency is investigated in this paper. To begin with, a cooperative spectrum sensing scheme based on dynamic censoring is employed to shorten sensing time and save unnecessary spectrum sensing energy. Then to jointly optimize the energy efficiency, the distortion constrained probabilistic transmission scheme is utilized. Afterwards the sensing threshold solving issue can be formulated as a nonlinear minmax optimization problem with the detection probability and false alarm probability constraints. Solving by the Matlab software with the free OPTI toolbox, simulation results demonstrate that significant energy can be saved via the the proposed joint optimization method in various mobile cloud scenarios.

Cite this article as:

Y. Li, J. Peng, F. Jiang, K. Liu, and X. Zhang, “Joint Spectrum Sensing and Data Transmission Optimization for Energy Efficiency in Cognitive Radio Sensor Networks: A Dynamic Cooperative Method,” J. Adv. Comput. Intell. Intell. Inform., Vol.19 No.2, pp. 197-204, 2015.

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References

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This article is published under a Creative Commons Attribution-NoDerivatives 4.0 Internationa License.

[1] [1] A. Fehske, G. Fettweis, J. Malmodin, and G. Biczok, “The Global Footprint of Mobile Communications: The Ecological and Economic Perspective,” IEEE Communications Magazine, Vol.49, No.8, pp. 55-62, 2011.

[2] [2] O. B. Akan, O. Karli, and O. Ergul, “Cognitive radio sensor networks,” IEEE Network, Vol.23, No.4, pp. 34-40, 2009.

[3] [3] G. P. Joshi, S. Y. Nam, and S. W. Kim, “Cognitive Radio Wireless Sensor Networks: Applications, Challenges and Research Trends,” Sensors, Vol.13, No.9, pp. 11196-11228, 2013.

[4] [4] E. Hossain and V. Bhargava, “Cognitive wireless communication networks,” Berlin: Springer, 2007.

[5] [5] G. Ganesan and Y. Li, “Cooperative spectrum sensing in cognitive radio, part II: Multiuser networks,” IEEE Trans. on Wireless Communications, Vol.6, No.6, pp. 2214-2222, 2007.

[6] [6] I. F. Akyildiz, B. F. Lo, and R. Balakrishnan, “Cooperative spectrum sensing in cognitive radio networks: A survey,” Physical Communication, Vol.4, No.1, pp. 40-62, 2011.

[7] [7] S. Maleki, A. Pandharipande, and G. Leus, “Energy-efficient distributed spectrum sensing for cognitive sensor networks,” IEEE Sensors J., Vol.11, No.3, pp. 565-573, 2011.

[8] [8] S. Maleki and G. Leus, “Censored truncated sequential spectrum sensing for cognitive radio networks,” IEEE J. on Selected Areas in Communications, Vol.31, No.3, pp. 364-378, 2013.

[9] [9] S. Maleki, G. Leus, S. Chatzinotas, and B. Ottersten, “To AND or OR: How Shall the Fusion Center Rule in Energy-Constrained Cognitive Radio Networks?” IEEE Int. Conf. on Communications (ICC), 2014.(in press)

[10] [10] M. Najimi, A. Ebrahimzadeh, S. M. H. Andargoli, and A. Fallahi, “A Novel Sensing Nodes and Decision Node Selection Method for Energy Efficiency of Cooperative Spectrum Sensing in Cognitive Sensor Networks,” IEEE Sensor J., Vol.13, No.5, pp. 1610-1621, 2013.

[11] [11] X. Hao, M. H. Cheung, V. W. Wong, and V. C. Leung, “A coalition formation game for energy-efficient cooperative spectrum sensing in cognitive radio networks with multiple channels,” IEEE in Global Telecommunications Conf. (GLOBECOM), pp. 1-6, 2011.

[12] [12] A. S. Zahmati, X. Fernando, and A. Grami. “A Hybrid Spectrum Sensing Method for Cognitive Sensor Networks,” Wireless Personal Communications, Vol.74, No.2, pp. 953-968, 2014.

[13] [13] A. Jain, D. Gunduz, S. R. Kulkarni, H. V. Poor, and S. Verdu, “Energy efficient lossy transmission over sensor networks with feedback,” IEEE Int. Conf. on Acoustics Speech and Signal Processing (ICASSP), pp. 5558-5561, 2010.

[14] [14] A. Jain, D. Gunduz, S. R. Kulkarni, H. V. Poor, and S. Verdu, “Energy-Distortion Tradeoffs in Gaussian Joint Source-Channel Coding Problems,” IEEE Trans. on Information Theory, Vol.58, No.5, pp. 3153-3168, 2012.

[15] [15] H. Z. Zhang, Z. Y. Zhang, X. M. Chen, and R. Yin, “Energy efficient joint source and channel sensing in cognitive radio sensor networks,” IEEE Int. Conf. on Communications (ICC), pp. 1-6, 2011.

[16] [16] M. Naeem, K. Illanko, A. Karmokar, A. Anpalagan, and M. Jaseemuddin, “Energy-Efficient Cognitive Radio Sensor Networks: Parametric and Convex Transformations,” Sensors, Vol.13, No.8, pp. 11032-11050, 2013.

[17] [17] Y. Oohama, “Rate-distortion theory for Gaussian multiterminal source coding systems with several side informations at the decoder,” IEEE Trans. on Information Theory, Vol.51, No.7, pp. 2577-2593, 2005.

[18] [18] S. M. Kay, “Fundamentals of statistical signal processing: detection theory,” Upper Saddle River, New Jersey: Prentice Hall Signal Processing Series 1998.

[19] [19] C. R. Stevenson, C. Cordeiro, E. Sofer, and G. Ghouinard, “Functional requirements for the 802.22 WRAN standard,” IEEE Tech. Rep. 802.22-05/0007r46, 2005.