Top

Wireless Personal Communications

Published in:

06-01-2017

United Versus Cooperative Spectrum Sensing in Cognitive Wireless Sensor Networks (C-WSNs)

Authors: Morteza Shafiee, Vahid Tabataba Vakili

Published in: Wireless Personal Communications | Issue 3/2017

Activate our intelligent search to find suitable subject content or patents.

search-config

AI-assisted search

Off

Abstract

Cooperation is an effective method to increase the performance metrics of spectrum sensing in cognitive radio (CR). For spectrum sensing in cognitive wireless sensor networks (C-WSNs), low complexity and consequently low performance methods are applicable due to resource constraint. Also, we can profit the cooperation for overcoming the noise uncertainty, fading, shadowing, hidden primary user problem etc. But, low performance methods increase severely false alarm rate \((P_{Fa})\) and waste the precious resources of sensor nodes, because of collisions and retransmitions. In this paper, we propose two approaches for utilizing high performance spectrum sensing methods in C-WSNs. Then, we focus on our second approach i.e. United Spectrum Sensing, as a more comprehensive method than conventional cooperative spectrum sensing in CR, to solve the problem of high performance spectrum sensing in C-WSNs.

previous article Energy-Efficient Uplink Resource Allocation Based on Game Theory in Cognitive Small Cell Networks

next article GAZELLE: An Enhanced Random Network Coding Based Framework for Efficient P2P Live Video Streaming Over Hybrid WMNs

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Prakash, P., Lee, S. R., Noh, S. K., & Choi, D. Y. (2014). Issues in realization of cognitive radio sensor networks. International Journal of Control and Automation, 7(1), 141–152.CrossRef

Zahmati, A. S., Hussain, S., Fernando, X., & Grami, A. (2009). Cognitive wireless sensor networks: Emerging topics and recent challenges. IEEE International Conference on Science and Technology for Humanity (IEEE TIC-STH) (pp. 593–596).

Yau, K. L. A., Komisarczuk, P., & Teal, P. D. (2009). Cognitive radio-based wireless sensor networks: Conceptual design and open issues. In Proceedings of 2nd IEEE Workshop on Wireless and Internet Services (WISe).

Mitola, J., & Maguire, G. Q. (1999). Cognitive radio: Making software radios more personal. IEEE Personal Communications, 6(4), 13–18.CrossRef

Fragkiadakis, A., Angelakis, V., & Tragos, E. Z. (2014). Securing cognitive wireless sensor networks: A survey. International Journal of Distributed Sensor Networks. doi:10.1155/2014/393248.

Joshi, G. P., Seung, Y. N., & Sung, W. K. (2013). Cognitive radio wireless sensor networks: Applications, challenges and research trends. Journal of Sensors, 13(9), 11196–11228.CrossRef

Beneslu, S., & Shafiee, M. (2014). A high performance, low complexity method for spectrum sensing in cognitive wireless sensor network (C-WSN). In Proceedings od 4th International Conference on Information Technology Management, Communication and Computer (pp. 40–48).

Haykin, S., Thomson, D. J., & Reed, J. H. (2009). Spectrum sensing for cognitive radio. Proceedings of the IEEE, 97(5), 849–877.CrossRef

Shafiee, M., & Vakili, V. T. (2012). MTM-based spectrum sensing in cognitive wireless multimedia sensor networks (C-WMSNs). In Proceedings of 6th IEEE International Symposium on Telecommunications (IST).

10.

Shafiee, M., & Vakili, V. T. (2013). An approach to efficient spectrum sensing in cognitive wireless sensor networks (C-WSNs). Journal of Applied Mechanics and Materials (AMM), from 2nd International Conference on Civil Engineering and Transportation (Vol. 256–259, pp. 2303–2306).

11.

Viswanathan, R., & Ahsant, B. (2012). A review of sensing and distributed detection algorithms for cognitive radio systems. International Journal on Smart Sensing and Intelligent Systems, 5(1), 177–190.

12.

Kay, S. M. (1988). Modern spectral estimation: Theory and application. Englewood Cliffs: Prentice Hall.MATH

13.

Han, N., Shon, S. H., Joo, J. O., & Kim, J. M. (2006). Spectrum sensing method for increasing the spectrum efficiency in wireless sensor network. Springer Journal of Computer Science, 4239, 478–488.

14.

Fodor, V., & Glaropoulos, I. (2009). Sensor networks for spectrum sensing: Working assumptions and design goals. Sweden: Crops2 Project, Royal Institute of Technology (KTH University).

15.

Pham, H. N., Zhang, Y., Engelstad, P. E., Skeie, T., & Eliassen, F. (2009). Optimal cooperative spectrum sensing in cognitive sensor networks. International Conference on Wireless Communications and Mobile Computing (ICWCMC) (pp. 1073–1079).

16.

Izumi, S., Tsuruda, K., Takeuchi, T., Lee, H., Kawaguchi, H., & Yoshimoto, M. (2010). A low-power multi resolution spectrum sensing (MRSS) architecture for a wireless sensor network with cognitive radio. In Proceedings of 4th IEEE International Conference on Sensor Technologies and Applications (pp. 39–44).

17.

Akbari, M., & Falahati, A. (2011). A fault-tolerant cooperative spectrum sensing algorithm over cognitive radio network based on wireless sensor network. The Journal of Wireless Sensor Network (WSN), 3(3), 83.CrossRef

18.

Chatterjee, S. R., Hazra, R., Deb, A., & Chakraborty, M. (2011). Cyclostationary spectral analysis approach to spectrum sensing for mobile radio signals. In 2nd Annual IEEE International Conference on Innovative Techno-Management Solutions for Social Sector.

19.

Yan, J., & Inwhee, J. (2016). Markov model-based energy efficiency spectrum sensing in cognitive radio sensor networks. Hindawi Journal of Computer Networks and Communications.

20.

Subhedar, M., & Birajdar, G. (2011). Spectrum sensing techniques in cognitive radio networks: A survey. International Journal of Next-Generation Networks, 3, 37–51.CrossRef

21.

Yucek, T., & Arslan, H. (2009). A survey of spectrum sensing algorithms for cognitive radio applications. IEEE Communications Surveys and Tutorials, 11(1), 116–130.CrossRef

22.

Kay, S. M. (1993). Fundamentals of statistical signal processing: Estimation theory. Englewood Cliffs: Prentice Hall.MATH

23.

Cabric, D., Mishra, S., & Brodersen, R. (2004). Implementation issues in spectrum sensing for cognitive radios. Asilomar Conference on Signals, Systems and Computers (Vol. 1, pp. 772–776).

24.

Akyildiz, I. F., Lo, B. F., & Balakrishnan, R. (2011). Cooperative spectrum sensing in cognitive radio networks: A survey. Elsevier Journal on Physical Communication, 4(1), 40–62.CrossRef

25.

Selim, B., Alhussein, O., Karagiannidis, G. K., & Muhaidat, S. (2015). Optimal cooperative spectrum sensing over composite fading channels. IEEE International Conference on Communication Workshop (ICCW).

26.

Do, N. T., & An, B. (2015). A soft-hard combination-based cooperative spectrum sensing scheme for cognitive radio networks. Journal of Sensors, 15(2), 4388–4407.CrossRef

27.

Kyperountas, S., Correal, N., & Shi, Q. (2010). A comparison of fusion rules for cooperative spectrum sensing in fading channels. In Proceedings of 20th Virginia Tech Symposium on Wireless Personal Communications (pp. 1–6).

28.

Guimaraes, D. A., & Aquino, G. P. (2015). Resource-efficient fusion over fading and non-fading reporting channels for cooperative spectrum sensing. Journal of Sensors, 15(1), 1861–1884.CrossRef

29.

Weiss, T., Hillenbrand, J., & Jondral, F. (2003). A diversity approach for the detection of idle spectral resources in spectrum pooling systems. In Proceedings of 48th International Scientific Colloquium (p. 3738).

30.

Lunden, J., Koivunen, V., Huttunen, A., & Poor, H. V. (2007). Spectrum sensing in cognitive radios based on multiple cyclic frequencies. In IEEE International Conference on Cognitive Radio Oriented Wireless Networks and Communication (Crowncom).

31.

Dressler, F. (2008). Self-organization in sensor and actor networks. Chichester: Wiley.

32.

Rizvi, S., Karpinski, K., & Razaque, A. (2015). Novel architecture of self-organized mobile wireless sensor networks. Journal of Computing Science and Engineering, 9(4), 163–176.CrossRef

33.

Shah, M., Zhang, S., & Maple, C. (2013). Cognitive radio networks for internet of things: Applications, challenges and future. In Proceedings of 19th IEEE International Conference on Automation and Computing (ICAC) (pp. 1–6).

34.

Hossain, E., Rasti, M., Tabassum, H., & Abdelnasser, A. (2014). Evolution toward 5G multi-tier cellular wireless networks: An interference management perspective. IEEE Wireless Communications, 21(3), 118–127.CrossRef

35.

Thomson, D. J. (1982). Spectrum estimation and harmonic analysis. Proceedings of the IEEE, 70(9), 1055–1096.CrossRef

36.

Haykin, S. (2005). Cognitive radio: Bain-empowered wireless communications. IEEE Selected Areas on Communincation, 23(2), 201–220.CrossRef

37.

Hu, G., Muqing, W., Chunxiu, X., & Qianqian, W. (2010). An improved multitaper method for spectrum sensing in cognitive radio networks. In Proceedings of 3rd IEEE International Conference on Computer Science and Information Technology (ICCSIT) (pp. 393–396).

38.

Alghamdi, O. A., & Ahmed, M. Z. (2011). New optimization method for cooperative spectrum sensing in cognitive radio networks. IEEE Wireless Advanced (WiAd).

39.

Alghamdi, O. A., Abu-Rgheff, M. A., & Ahmed, M. Z. (2010). MTM parameters optimization for 64-FFT cognitive radio spectrum sensing using monte carlo simulation. In Proceedings of 2nd International Conference on Emerging Network Intelligence (pp. 107–113).

40.

Qian, Z., Lu, C., An, M., & Tolimieri, R. (1994). Self-sorting in place FFT algorithm with minimum working space. IEEE Transactions on Signal Processing, 42(10), 2835–2836.CrossRef

41.

Harvey, D., & Roche, D. S. (2010). In-place truncated fourier transform and applications to polynomial multiplication. In Proceedings of International Symposium on Symbolic and Algebraic Computation (pp. 325–329).

42.

Cooley, J. W. (1990). ”How the FFT gained acceptance”, a history of scientific computing. Reading, MA: ACM Publication.

43.

Cordeiro, C., Challapali, K., & Birru, D. (2006). IEEE 802.22: An introduction to the first wireless standard based on cognitive radios. Journal of Communications, 1(1), 38–47.CrossRef

Title: United Versus Cooperative Spectrum Sensing in Cognitive Wireless Sensor Networks (C-WSNs)
Authors: Morteza Shafiee
Vahid Tabataba Vakili
Publication date: 06-01-2017
Publisher: Springer US
Published in: Wireless Personal Communications / Issue 3/2017
Print ISSN: 0929-6212
Electronic ISSN: 1572-834X
DOI: https://doi.org/10.1007/s11277-016-3929-x

Springer Professional

Abstract

Please log in to get access to your license.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Technik"

Springer Professional "Wirtschaft+Technik"

Springer Professional "Wirtschaft"

Other articles of this Issue 3/2017

A Model-Based Reinforcement Learning Algorithm for Routing in Energy Harvesting Mobile Ad-Hoc Networks

RMS Delay Spread Assessment for Indoor UWB Propagation Channels

Global Optimization of Wireless Seismic Sensor Network Based on the Kriging Model and Improved Particle Swarm Optimization Algorithm

Power Adaptation for Energy Efficient Bi-directional Relaying with Quality-of-Service Requirement and Individual Peak-Power Limit

A Novel Low-Complex Antenna Selection Scheme for Beyond 4G (B4G) Systems

Performance Analysis of Combined Space Time Block Coding with Generalized Selection Combining in Underlay Cognitive Radio