Cognitive radio-enabled Internet of Vehicles: a cooperative spectrum sensing and allocation for vehicular communication

Cognitive radio-enabled Internet of Vehicles: a cooperative spectrum sensing and allocation for vehicular communication

For access to this article, please select a purchase option:

Buy article PDF
(plus tax if applicable)
Buy Knowledge Pack
10 articles for $120.00
(plus taxes if applicable)

IET members benefit from discounts to all IET publications and free access to E&T Magazine. If you are an IET member, log in to your account and the discounts will automatically be applied.

Learn more about IET membership 

Recommend Title Publication to library

You must fill out fields marked with: *

Librarian details
Your details
Why are you recommending this title?
Select reason:
IET Networks — Recommend this title to your library

Thank you

Your recommendation has been sent to your librarian.

Internet of Things (IoTs) era is expected to empower all aspects of Intelligent Transportation System (ITS) to improve transport safety and reduce road accidents. US Federal Communication Commission (FCC) officially allocated 75 MHz spectrum in the 5.9 GHz band to support vehicular communication. The authors studied the application of cognitive radio (CR) technology to IoVs to increase the spectra opportunities for vehicular communication, especially when the allocated 75 MHz is not enough due to high demands because of the increasing number of connected vehicles as already foreseen in the near era of IoVs. They proposed a novel CR Assisted Vehicular NETwork (CRAVNET) framework which empowers CR enabled vehicles to make opportunistic usage of licensed spectrum on the highways. They also developed a novel cooperative three-state spectrum sensing and allocation model which makes CR vehicular secondary units aware of additional spectrum resources opportunities on their current and future positions, and applies optimal sensing node allocation algorithm to guarantee timely acquisition of the available channels within a limited sensing time. The results of the theoretical analyses and simulation experiments have demonstrated that the proposed model can significantly improve the performance of a cooperative spectrum sensing, and provide vehicles with additional spectrum opportunities.


    1. 1)
      • 1. Eze, E.C., Zhang, S., Liu, E., et al: ‘Advances in vehicular ad-hoc networks (VANETs): challenges and road-map for future development’, Int. J. Autom. Comput., 2016, 13, (1), pp. 118.
    2. 2)
      • 2. Hussain, R., Son, J., Eun, H., et al: ‘Rethinking vehicular communications: merging VANET with cloud computing’. 2012 IEEE 4th Int. Conf. Cloud Computing Technology and Science (CloudCom), 2012, pp. 606609.
    3. 3)
      • 3. Rawat, D.B., Popescu, D.C., Gongjun, Y., et al: ‘Enhancing VANET performance by joint adaptation of transmission power and contention window size’, IEEE Trans. Parallel Distrib. Syst., 2011, 22, (9), pp. 15281535.
    4. 4)
      • 4. ‘IEEE Std 1609 family, IEEE Trial-Use Standard for Wireless Access in Vehicular Environments (WAVE)’, November 2006.
    5. 5)
      • 5. Eze, E.C., Zhang, S., Liu, E.: ‘Vehicular ad hoc networks (VANETs): current state, challenges, potentials and way forward’. 20th Int. Conf. Automation and Computing (ICAC) 2014, 12–13 September 2014, pp. 176181.
    6. 6)
      • 6. Wang, C.-H., Chou, C.-T., Lin, P., et al: ‘Performance evaluation of IEEE 802.15.4 non beacon-enabled mode for internet of vehicles’, IEEE Trans. Intell. Transp. Syst., 2015, PP, (99), pp. 110.
    7. 7)
      • 7. Ansari, K., Feng, Y., Tang, M.: ‘A runtime integrity monitoring framework for real-time relative positioning systems based on GPS and DSRC’, IEEE Trans. Intell. Transp. Syst., 2015, 16, (2), pp. 980992.
    8. 8)
      • 8. Marfia, G., Roccetti, M., Amoroso, A., et al: ‘Cognitive cars: constructing a cognitive playground for VANET research testbeds’. 4th Int. Conf. Cognitive Radio and Advanced Spectrum Management (CogART 2011), Barcelona, Spain, October 2011.
    9. 9)
      • 9. Wang, Z.Y., Ho, P-H.: ‘A novel sensing coordination framework for CR-VANETs’, IEEE Trans. Veh. Technol., 2010, 59, (4), pp. 19361948.
    10. 10)
      • 10. Eze, J.C., Zhang, S., Liu, E., et al: ‘Cognitive radio aided internet of vehicles (IoVs) for improved spectrum resource allocation’. 2015 IEEE Int. Conf. Computer and Information Technology; Ubiquitous Computing and Communications; Dependable, Autonomic and Secure Computing; Pervasive Intelligence and Computing (CIT/IUCC/DASC/PICOM), 26–28 October 2015, pp. 23462352.
    11. 11)
      • 11. Eze, J.C., Zhang, S., Liu, E., et al: ‘Cognitive radio aided vehicular ad-hoc networks with efficient spectrum allocation and QoS guarantee’. 2016 22nd Int. Conf. Automation and Computing (ICAC), Colchester, UK, 2016, pp. 156161.
    12. 12)
      • 12. Zhao, Y., Song, M., Xin, C., et al: ‘Spectrum sensing based on three-state model to accomplish all-level fairness for co-existing multiple cognitive radio networks’. 2012 Proc. IEEE INFOCOM, Orlando, FL, 2012, pp. 17821790.
    13. 13)
      • 13. Zhao, Y., Song, M., Xin, C.: ‘FMAC: A fair MAC protocol for coexisting cognitive radio networks’. 2013 Proc. IEEE INFOCOM, Turin, 2013, pp. 14741482.
    14. 14)
      • 14. Gao, Y., Li, N., Zhang, J., et al: ‘Effective capacity of cognitive radio systems’. 2016 IEEE 13th Int. Conf. Signal Processing (ICSP), Chengdu, 2016, pp. 17571761.
    15. 15)
      • 15. Rawat, D.B., Zhao, Y., Yan, G., et al: ‘CRAVE: cognitive radio enabled vehicular communications in heterogeneous networks’. 2013 IEEE Radio and Wireless Symp., Austin, TX, 2013, pp. 190192.
    16. 16)
      • 16. Huang, D., Meyn, S.: ‘Generalized error exponents for small sample universal hypothesis testing’, IEEE Trans. Inf. Theory, 2013, 59, (12), pp. 81578181.
    17. 17)
      • 17. Digham, F.F., Alouini, M.-S., Simon, M.K.: ‘On the energy detection of unknown signals over fading channels’, IEEE Trans. Commun., 2007, 55, (1), pp. 2124.
    18. 18)
      • 18. Quan, Z., Shuguang, C., Poor, H.V., et al: ‘Collaborative wideband sensing for cognitive radios’, IEEE Signal Process. Mag., 2008, 25, (6), pp. 6073.
    19. 19)
      • 19. Liang, Y.-C., Zeng, Y., Peh, E.C.Y., et al: ‘Sensing-throughput tradeoff for cognitive radio networks’, IEEE Trans. Wirel. Commun., 2008, 7, (4), pp. 13261337.
    20. 20)
      • 20. Bellman, R.: ‘Applied dynamic programming’ (Princeton University Press, Princeton, NJ, 1962).

Related content

This is a required field
Please enter a valid email address