International Journal of

ADVANCED AND APPLIED SCIENCES

EISSN: 2313-3724, Print ISSN: 2313-626X

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 Volume 8, Issue 2 (February 2021), Pages: 92-100

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 Original Research Paper

 Title: Fuzzy-based reliable and secure cooperative spectrum sensing for the smart grid

 Author(s): Laila Nassef *, Reemah Alhebshi

 Affiliation(s):

 Department of Computer Science, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia

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 * Corresponding Author. 

  Corresponding author's ORCID profile: https://orcid.org/0000-0001-9707-1259

 Digital Object Identifier: 

 https://doi.org/10.21833/ijaas.2021.02.013

 Abstract:

Cognitive radio is a promising technology to solve the spectrum scarcity problem caused by inefficient utilization of radio spectrum bands. It allows secondary users to opportunistically access the underutilized spectrum bands assigned to licensed primary users. The local individual spectrum detection is inefficient, and cooperative spectrum sensing is employed to enhance spectrum detection accuracy. However, cooperative spectrum sensing opens up opportunities for new types of security attacks related to the cognitive cycle. One of these attacks is the spectrum sensing data falsification attack, where malicious secondary users send falsified sensing reports about spectrum availability to mislead the fusion center. This internal attack cannot be prevented using traditional cryptography mechanisms. To the best of our knowledge, none of the previous work has considered both unreliable communication environments and the spectrum sensing data falsification attack for cognitive radio based smart grid applications. This paper proposes a fuzzy inference system based on four conflicting descriptors. An attack model is formulated to determine the probability of detection for both honest and malicious secondary users. It considers four independent malicious secondary users’ attacking strategies of always yes, always no, random, and opposite attacks. The performance of the proposed fuzzy fusion system is simulated and compared with the conventional fusion rules of AND, OR, Majority, and the reliable fuzzy fusion that does not consider the secondary user’s sensing reputation. The results indicate that incorporating sensing reputation in the fusion center has enhanced the accuracy of spectrum detection and have prevented malicious secondary users from participating in the spectrum detection fusion. 

 © 2020 The Authors. Published by IASE.

 This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

 Keywords: Spectrum sensing data falsification attack, Fuzzy fusion, Reliability, Sensing reputation

 Article History: Received 27 July 2020, Received in revised form 8 October 2020, Accepted 13 October 2020

 Acknowledgment:

No Acknowledgment.

 Compliance with ethical standards

 Conflict of interest: The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

 Citation:

  Nassef L and Alhebshi R (2021). Fuzzy-based reliable and secure cooperative spectrum sensing for the smart grid. International Journal of Advanced and Applied Sciences, 8(2): 92-100

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 Figures

 Fig. 1 Fig. 2 Fig. 3

 Tables

 Table 1

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 References (33)

  1. Akyildiz IF, Lee WY, Vuran MC, and Mohanty S (2006). NeXt generation/dynamic spectrum access/cognitive radio wireless networks: A survey. Computer Networks, 50(13): 2127-2159. https://doi.org/10.1016/j.comnet.2006.05.001   [Google Scholar]
  2. Akyildiz IF, Lo BF, and Balakrishnan R (2011). Cooperative spectrum sensing in cognitive radio networks: A survey. Physical Communication, 4(1): 40-62. https://doi.org/10.1016/j.phycom.2010.12.003   [Google Scholar]
  3. Ansere JA, Han G, Wang H, Choi C, and Wu C (2019). A reliable energy efficient dynamic spectrum sensing for cognitive radio IoT networks. IEEE Internet of Things Journal, 6(4): 6748-6759. https://doi.org/10.1109/JIOT.2019.2911109   [Google Scholar]
  4. Bae S, So J, and Kim H (2017). On optimal cooperative sensing with energy detection in cognitive radio. Sensors, 17(9): 2111. https://doi.org/10.3390/s17092111   [Google Scholar] PMid:28914753 PMCid:PMC5621099
  5. Bai P, Zhang X, and Ye F (2017). Reputation-based Beta reputation system against SSDF attack in cognitive radio networks. In the Progress in Electromagnetics Research Symposium-Fall, IEEE, Singapore, Singapore: 792-799. https://doi.org/10.1109/PIERS-FALL.2017.8293243   [Google Scholar]
  6. Bennaceur J, Idoudi H, and Azouz Saidane L (2018). Trust management in cognitive radio networks: A survey. International Journal of Network Management, 28(1): e1999. https://doi.org/10.1002/nem.1999   [Google Scholar]
  7. Bhattacharjee S, Chatterjee M, Kwiat K, and Kamhoua C (2015). Multinomial trust in presence of uncertainty and adversaries in DSA networks. In the MILCOM 2015-2015 IEEE Military Communications Conference, IEEE, Tampa, USA: 611-616. https://doi.org/10.1109/MILCOM.2015.7357511   [Google Scholar]
  8. Chen H, Zhou M, Xie L, and Li J (2017). Cooperative spectrum sensing with M-ary quantized data in cognitive radio networks under SSDF attacks. IEEE Transactions on Wireless Communications, 16(8): 5244-5257. https://doi.org/10.1109/TWC.2017.2707407   [Google Scholar]
  9. Dehalwar V, Kalam A, Kolhe ML, and Zayegh A (2016). Compliance of IEEE 802.22 WRAN for field area network in smart grid. In the IEEE International Conference on Power System Technology, IEEE, Wollongong, Australia: 1-6. https://doi.org/10.1109/POWERCON.2016.7754046   [Google Scholar]
  10. Fadel E, Faheem M, Gungor VC, Nassef L, Akkari N, Malik MGA, and Akyildiz IF (2017). Spectrum-aware bio-inspired routing in cognitive radio sensor networks for smart grid applications. Computer Communications, 101: 106-120. https://doi.org/10.1016/j.comcom.2016.12.020   [Google Scholar]
  11. Fadel E, Gungor VC, Nassef L, Akkari N, Malik MA, Almasri S, and Akyildiz IF (2015). A survey on wireless sensor networks for smart grid. Computer Communications, 71: 22-33. https://doi.org/10.1016/j.comcom.2015.09.006   [Google Scholar]
  12. Fragkiadakis A, Angelakis V, and Tragos EZ (2014). Securing cognitive wireless sensor networks: A survey. International Journal of Distributed Sensor Networks, 10(3): 393248. https://doi.org/10.1155/2014/393248   [Google Scholar]
  13. Fu Y, Yang F, and He Z (2018). A quantization-based multibit data fusion scheme for cooperative spectrum sensing in cognitive radio networks. Sensors, 18(2): 473. https://doi.org/10.3390/s18020473   [Google Scholar] PMid:29415448 PMCid:PMC5856117
  14. Hernandes AG and Abrao T (2020). Distributed average consensus optimization for cooperative spectrum sensing in cognitive radio ad hoc networks. Emerging Telecommunications Technologies, 31(7): e3965. https://doi.org/10.1002/ett.3965   [Google Scholar]
  15. Huang XL, Tang XW, and Hu F (2020). Dynamic spectrum access for multimedia transmission over multi-user, multi-channel cognitive radio networks. IEEE Transactions on Multimedia, 22(1): 201-214. https://doi.org/10.1109/TMM.2019.2925960   [Google Scholar]
  16. Joshi GP, Nam SY, and Kim SW (2013). Cognitive radio wireless sensor networks: Applications, challenges and research trends. Sensors, 13(9): 11196-11228. https://doi.org/10.3390/s130911196   [Google Scholar] PMid:23974152 PMCid:PMC3821336
  17. Kar S, Sethi S, and Sahoo RK (2017). A multi-factor trust management scheme for secure spectrum sensing in cognitive radio networks. Wireless Personal Communications, 97(2): 2523-2540. https://doi.org/10.1007/s11277-017-4621-5   [Google Scholar]
  18. Khan AA, Rehmani MH, and Rachedi A (2017). Cognitive-radio-based internet of things: Applications, architectures, spectrum related functionalities, and future research directions. IEEE Wireless Communications, 24(3): 17-25. https://doi.org/10.1109/MWC.2017.1600404   [Google Scholar]
  19. Khan MS, Jibran M, Koo I, Kim SM, and Kim J (2019). A double adaptive approach to tackle malicious users in cognitive radio networks. Wireless Communications and Mobile Computing, 2019: 2350694. https://doi.org/10.1155/2019/2350694   [Google Scholar]
  20. Mustapha I, Ali BM, Rasid MFA, Sali A, and Mohamad H (2015). An energy-efficient spectrum-aware reinforcement learning-based clustering algorithm for cognitive radio sensor networks. Sensors, 15(8): 19783-19818. https://doi.org/10.3390/s150819783   [Google Scholar] PMid:26287191 PMCid:PMC4570397
  21. Nassef L and Alhebshi R (2016). Secure spectrum sensing in cognitive radio sensor networks: A survey. International Journal of Computational Engineering Research, 6(3): 1-7.   [Google Scholar]
  22. Nassef L, El-Habshi R, and Jose L (2018). Clustering-based routing for wireless sensor networks in smart grid environment. International Journal of Advanced Smart Sensor Network Systems, 8(1/2/3): 1-14. https://doi.org/10.5121/ijassn.2018.8301   [Google Scholar]
  23. Rasheed T, Rashdi A, and Akhtar AN (2018). Cooperative spectrum sensing using fuzzy logic for cognitive radio network. In the Advances in Science and Engineering Technology International Conferences, IEEE, Abu Dhabi, UAE: 1-6. https://doi.org/10.1109/ICASET.2018.8376914   [Google Scholar] PMCid:PMC5870312
  24. Saber MJ and Sadough SMS (2016). Multiband cooperative spectrum sensing for cognitive radio in the presence of malicious users. IEEE Communications Letters, 20(2): 404-407. https://doi.org/10.1109/LCOMM.2015.2505299   [Google Scholar]
  25. Sharifi AA (2019). Attack-aware defense strategy: A robust cooperative spectrum sensing in cognitive radio sensor networks. Iranian Journal of Science and Technology, Transactions of Electrical Engineering, 43(1): 133-140. https://doi.org/10.1007/s40998-018-0133-x   [Google Scholar]
  26. Toma OH, López-Benítez M, Patel DK, and Umebayashi K (2020). Estimation of primary channel activity statistics in cognitive radio based on imperfect spectrum sensing. IEEE Transactions on Communications, 68(4): 2016-2031. https://doi.org/10.1109/TCOMM.2020.2965944   [Google Scholar]
  27. Wan R, Ding L, Xiong N, and Zhou X (2019). Mitigation strategy against spectrum-sensing data falsification attack in cognitive radio sensor networks. International Journal of Distributed Sensor Networks, 15(9): 1550147719870645. https://doi.org/10.1177/1550147719870645   [Google Scholar]
  28. Wang J, Chen R, Tsai JJ, and Wang DC (2018). Trust-based mechanism design for cooperative spectrum sensing in cognitive radio networks. Computer Communications, 116: 90-100. https://doi.org/10.1016/j.comcom.2017.11.010   [Google Scholar]
  29. Wang P, Chen C, Zhu S, Lyu L, Zhang W, and Guan X (2017). An optimal reputation-based detection against SSDF attacks in industrial cognitive radio network. In the 13th IEEE International Conference on Control and Automation, IEEE, Ohrid, Macedonia: 729-734. https://doi.org/10.1109/ICCA.2017.8003150   [Google Scholar]
  30. Wu J, Song T, Yu Y, Wang C, and Hu J (2018). Sequential cooperative spectrum sensing in the presence of dynamic Byzantine attack for mobile networks. PloS One, 13(7): e0199546. https://doi.org/10.1371/journal.pone.0199546   [Google Scholar] PMid:29975727 PMCid:PMC6033420
  31. Ye F, Zhang X, and Li Y (2016). Comprehensive reputation-based security mechanism against dynamic SSDF attack in cognitive radio networks. Symmetry, 8(12): 147. https://doi.org/10.3390/sym8120147   [Google Scholar]
  32. Yigit M, Gungor VC, Fadel E, Nassef L, Akkari N, and Akyildiz IF (2016). Channel-aware routing and priority-aware multi-channel scheduling for WSN-based smart grid applications. Journal of Network and Computer Applications, 71: 50-58. https://doi.org/10.1016/j.jnca.2016.05.015   [Google Scholar]
  33. Zheng M, Liang W, Yu H, and Song M (2016). SMCSS: A quick and reliable cooperative spectrum sensing scheme for cognitive industrial wireless networks. IEEE Access, 4: 9308-9319. https://doi.org/10.1109/ACCESS.2016.2641471   [Google Scholar]