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Extending the Lifetime of Multichannel Sensing Wireless Cognitive Sensor Networks Using Sensor Selection


Affiliations
1 Department of ECE, SET, Mody University of Science & Technology, Lakshmangarh, Sikar, Rajasthan, India
2 Department of CSE, SET, Mody University of Science & Technology, Lakshmangarh, Sikar, Rajasthan, India
 

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Wireless Cognitive Sensor Networks (WCSNs) consist of a combination of small, less energy and economical sensors. One of the major functions of these WCSNs is to sense the channels properly. The primary issue is that the sensors cannot sense the multiple channels simultaneously. So, to select cooperative sensors for sensing multiple channels simultaneously is a challenging issue. The focus should also be laid on extending the lifetime of WCSNs.

In this paper, for sensing different channels within different sensing periods tunable receiver is proposed and to extend the lifetime of the sensors, systematic node selection is suggested. With node selection, adequate nodes sense the channel while all the quality restrictions are to be maintained. A subset of nodes to sense every channelis chosen in a way that the remaining energy of the sensor is balanced properly which results in extending the lifetime of the network. Simulation results are obtained to discuss the benefits of the proposed process with other sensor choosing schemes.


Keywords

Energy Efficient, Lifetime Maximization, Multichannel Sensing.
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  • A. Ghasemi, and E. S. Sousa, “Spectrum sensing in cognitive radio networks: Requirements, challenges and design trade-offs,” IEEE Communications Magazine, vol. 46, no. 4, pp. 32-39, 2008.
  • 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.
  • Z. Quan, S. Cui, and A. H. Sayed, “Optimal linear cooperation for spectrum sensing incognitive radio networks,” IEEE Journal of Selected Topics in Signal Processing, vol. 2, no. 1, pp. 28-40, 2008.
  • K. Cichoń, A. Kliks, and H. Bogucka, “Energy-efficient cooperative spectrum sensing: A survey,” IEEE Communications Surveys & Tutorials, vol. 18, no. 3, pp. 1861-1886, 2016.
  • S. Maleki, S. P. Chepuri, and L. Geert, “Optimization of hard fusion based spectrum sensing for energy-constrained cognitive radio networks,” Physical Communication, vol. 9, pp. 193-198, 2013.
  • T. Cui, and K. S. Kwak, “Cooperative spectrum sensing with adaptive node selection for cognitive radio networks,” Wireless Personal Communications, vol. 78, no. 4, pp. 1879-1890, 2014.
  • X. Xu, J. Bao, H. Cao, Y. Yao, and S. Hu, “Energy-efficiency-based optimal relay selection scheme with a ber constraint in cooperative cognitive radio networks,” IEEE Transactions on Vehicular Technology, vol. 65, no. 1, pp. 191-203, 2015.
  • Q.-T. Vien, H. X. Nguyen, and A. Nallanathan, “Cooperative spectrum sensing with secondary user selection for cognitive radio networks over nakagami-m fading channels,” IET Communications, vol. 10, no. 1, pp. 91-97, 2016.
  • Y. Li, Y. Gao, Y. Tang, and C. Zhu, “Energy efficient cooperative spectrum sensing with twice selection of nodes,” 2016 8th International Conference on Wireless Communications & Signal Processing (WCSP), Yangzhou, China, IEEE, 2016.
  • M. Najimi, A. Ebrahimzadeh, S. M. H. Andargoli, and A. Fallahi, “Lifetime maximization in cognitive sensor networks based on the node selection,” IEEE Sensors Journal, vol. 14, no. 7, pp. 2376-2383, 2014.
  • G. Hattab, and M. Ibnkahla, “Multiband spectrum sensing: Challenges and limitations,” in Proc. WiSense Workshop, Ottawa, 2014.
  • Z. Quan, S. Cui, A. H. Sayed, and H. V. Poor, “Optimal multiband joint detection for spectrum sensing in cognitive radio network,” IEEE Transactions on Signal Processing, vol. 57, no. 3, pp. 1128-1140, 2009.
  • N. Rastegardoost, and B. Jabbari, “On channel selection schemes for spectrum sensing in cognitive radio networks,” 2015 IEEE Wireless Communications and Networking Conference (WCNC), New Orleans, LA, USA, 2015.
  • S. Qureshi, S. Ahmad, A. A. Ikram, and N. ul Hasan, “Joint energy and throughput based multichannel assignment in cognitive radio sensor network,” 2016 IEEE 3rd International Symposium on Telecommunication Technologies (ISTT), Kuala Lumpur, Malaysia, 2016.
  • D. Zhang, Z. Chen, J. Ren, N. Zhang, M. K. Awad, H. Zhou, and X. S. Shen, “Energy harvesting-aided spectrum sensing and data transmission in heterogeneous cognitive radio sensor network,” IEEE Transactions on Vehicular Technology, vol. 66, no. 1, pp. 831-843, 2017.

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  • Extending the Lifetime of Multichannel Sensing Wireless Cognitive Sensor Networks Using Sensor Selection

Abstract Views: 191  |  PDF Views: 102

Authors

Shobhit Verma
Department of ECE, SET, Mody University of Science & Technology, Lakshmangarh, Sikar, Rajasthan, India
Vikas Raina
Department of CSE, SET, Mody University of Science & Technology, Lakshmangarh, Sikar, Rajasthan, India
Partha Pratim Bhattacharya
Department of ECE, SET, Mody University of Science & Technology, Lakshmangarh, Sikar, Rajasthan, India

Abstract


Wireless Cognitive Sensor Networks (WCSNs) consist of a combination of small, less energy and economical sensors. One of the major functions of these WCSNs is to sense the channels properly. The primary issue is that the sensors cannot sense the multiple channels simultaneously. So, to select cooperative sensors for sensing multiple channels simultaneously is a challenging issue. The focus should also be laid on extending the lifetime of WCSNs.

In this paper, for sensing different channels within different sensing periods tunable receiver is proposed and to extend the lifetime of the sensors, systematic node selection is suggested. With node selection, adequate nodes sense the channel while all the quality restrictions are to be maintained. A subset of nodes to sense every channelis chosen in a way that the remaining energy of the sensor is balanced properly which results in extending the lifetime of the network. Simulation results are obtained to discuss the benefits of the proposed process with other sensor choosing schemes.


Keywords


Energy Efficient, Lifetime Maximization, Multichannel Sensing.

References