Skip to main content

Advertisement

SpringerLink
Log in

Energy and spectrum aware unequal clustering with deep learning based primary user classification in cognitive radio sensor networks

  • Original Article
  • Published:
International Journal of Machine Learning and Cybernetics Aims and scope Submit manuscript

Abstract

The problem of energy efficiency in cognitive radio sensor networks (CRSN) is mainly caused by the limited energy of sensor nodes and other channel-related operations for data transmission. The unequal clustering method should be considered for balancing the energy consumption among the cluster heads (CHs) for prolonging the network lifetime. The CH selection should consider the number of accessible free channels for efficient channel assignment. To improve fairness, the channel assignment problem should consider energy consumption among the cluster members. Furthermore, the relay metric for the selection of the best next-hop should consider the stability of the link for improving the transmission time. The CH rotation for cluster maintenance should be energy and spectrum aware. With regard to the above objectives, this paper proposes an energy and spectrum aware unequal clustering (ESAUC) protocol that jointly overcomes the limitations of energy and spectrum for maximizing the lifetime of CRSN. Our proposed ESAUC protocol improves fairness by achieving residual energy balance among the sensor nodes and enhances the network lifetime by reducing the overall energy consumption. Deep Belief Networks algorithm is exploited to predict the spectrum holes. ESAUC improves the stability of the cluster by optimally adjusting the number of common channels. ESAUC uses a CogAODV based routing mechanism to perform inter-cluster forwarding. Simulation results show that the proposed scheme outperforms the existing CRSN clustering algorithms in terms of residual energy, Network Lifetime, secondary user–primary user Interference Ratio, Route Discovery Frequency, throughput, Packet Delivery Ratio, and end-to-end delay.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19
Fig. 20
Fig. 21
Fig. 22
Fig. 23
Fig. 24
Fig. 25

Similar content being viewed by others

References

  1. Haykin S (2005) Cognitive radio: brain-empowered wireless communications. IEEE J Sel Areas Commun 23:201–220. https://doi.org/10.1109/JSAC.2004.839380

    Article  Google Scholar 

  2. Luo H, Huang Z, Zhu T (2015) A survey on spectrum utilization in wireless sensor networks. J Sens 2015:1–13. https://doi.org/10.1155/2015/624610

    Article  Google Scholar 

  3. Akan O, Karli O, Ergul O (2009) Cognitive radio sensor networks. IEEE Netw 23:34–40. https://doi.org/10.1109/MNET.2009.5191144

    Article  Google Scholar 

  4. Joshi G, Nam S, Kim S, Joshi GP, Nam SY, Kim SW (2013) Cognitive radio wireless sensor networks: applications, challenges and research trends. Sensors 13:11196–11228. https://doi.org/10.3390/s130911196

    Article  Google Scholar 

  5. Ahmad A, Ahmad S, Rehmani MH, Hassan NU (2015) A survey on radio resource allocation in cognitive radio sensor networks. IEEE Commun Surv Tutor 17:888–917. https://doi.org/10.1109/COMST.2015.2401597

    Article  Google Scholar 

  6. Yu J, Qi Y, Wang G, Guo Q, Gu X (2011) An energy-aware distributed unequal clustering protocol for wireless sensor networks. Int J Distrib Sens Netw 7:202145. https://doi.org/10.1155/2011/202145

    Article  Google Scholar 

  7. Stephan T, Joseph KS (2016) PSO assisted OLSR routing for cognitive radio vehicular sensor networks. In: Proceedings of the international conference on informatics and analytics—ICIA-16. https://doi.org/10.1145/2980258.2980457

  8. Kang X, Liang Y-C, Nallanathan A, Garg HK, Zhang R (2009) Optimal power allocation for fading channels in cognitive radio networks: ergodic capacity and outage capacity. IEEE Trans Wirel Commun 8:940–950. https://doi.org/10.1109/TWC.2009.071448

    Article  Google Scholar 

  9. Van Huynh V, Nguyen HS, Hoc LTT, Nguyen TS, Voznak M (2019) Optimization issues for data rate in energy harvesting relay-enabled cognitive sensor networks. Comput Netw. https://doi.org/10.1016/j.comnet.2019.04.012

    Article  Google Scholar 

  10. Xu C, Song C, Zeng P, Yu H (2018) Secure resource allocation for energy harvesting cognitive radio sensor networks without and with cooperative jamming. Comput Netw. https://doi.org/10.1016/j.comnet.2018.05.026

    Article  Google Scholar 

  11. Lo BF (2011) A survey of common control channel design in cognitive radio networks. Phys Commun 4:26–39. https://doi.org/10.1016/J.PHYCOM.2010.12.004

    Article  Google Scholar 

  12. Stephan T, Joseph KS (2016) Cognitive radio assisted OLSR routing for vehicular sensor networks. Procedia Comput Sci 89:271–282. https://doi.org/10.1016/j.procs.2016.06.058

    Article  Google Scholar 

  13. Al-Turjman F (2019) Cognitive routing protocol for disaster-inspired Internet of Things. Future Gener Comput Syst 92:1103–1115. https://doi.org/10.1016/j.future.2017.03.014

    Article  Google Scholar 

  14. Al-Turjman F (2017) Cognitive-node architecture and a deployment strategy for the future WSNs. Mob Netw Appl 24(5):1663–1681. https://doi.org/10.1007/s11036-017-0891-0

    Article  Google Scholar 

  15. Ullah Z, Al-Turjman F, Mostarda L (2020) Cognition in UAV-aided 5G and beyond communications: a survey. IEEE Trans Cogn Commun Netw. https://doi.org/10.1109/tccn.2020.2968311

    Article  Google Scholar 

  16. Stephan T, Suresh Joseph K (2018) Particle swarm optimization-based energy efficient channel assignment technique for clustered cognitive radio sensor networks. Comput J. https://doi.org/10.1093/comjnl/bxx119

    Article  Google Scholar 

  17. Ahmed E, Gani A, Abolfazli S, Yao LJ, Khan SU (2016) Channel assignment algorithms in cognitive radio networks: taxonomy, open issues, and challenges. IEEE Commun Surv Tutor 18:795–823. https://doi.org/10.1109/COMST.2014.2363082

    Article  Google Scholar 

  18. Cui Y, Jing Xj, Sun S, Wang X, Cheng D, Huang H (2015) Deep learning based primary user classification in cognitive radios. In: 2015 15th International symposium on communications and information technologies (ISCIT). pp 165–168. https://doi.org/10.1109/iscit.2015.7458333

  19. Heinzelman WB, Chandrakasan AP, Balakrishnan H (2002) An application-specific protocol architecture for wireless microsensor networks. IEEE Trans Wirel Commun 1:660–670. https://doi.org/10.1109/TWC.2002.804190

    Article  Google Scholar 

  20. Younis O, Fahmy S (2004) HEED: a hybrid, energy-efficient, distributed clustering approach for ad hoc sensor networks. IEEE Trans Mob Comput 3:366–379. https://doi.org/10.1109/TMC.2004.41

    Article  Google Scholar 

  21. Gupta V, Pandey R (2016) An improved energy aware distributed unequal clustering protocol for heterogeneous wireless sensor networks. Eng Sci Technol Int J 19:1050–1058. https://doi.org/10.1016/J.JESTCH.2015.12.015

    Article  Google Scholar 

  22. Bradonjic M, Lazos L (2012) Graph-based criteria for spectrum-aware clustering in cognitive radio networks. Ad Hoc Netw 10:75–94. https://doi.org/10.1016/J.ADHOC.2011.05.009

    Article  Google Scholar 

  23. Eletreby R, Elsayed H, Khairy M (2014) CogLEACH: A spectrum aware clustering protocol for cognitive radio sensor networks. In: Proceedings of 9th international conference on cognitive radio oriented wireless networks, ICST, 2014. https://doi.org/10.4108/icst.crowncom.2014.255370

  24. Zhang H, Zhang Z, Dai H, Yin R, Chen X (2011) Distributed spectrum-aware clustering in cognitive radio sensor networks. In: 2011 IEEE global telecommunication conference—GLOBECOM 2011, IEEE. pp 1–6. https://doi.org/10.1109/glocom.2011.6134296

  25. Shah GA, Alagoz F, Fadel EA, Akan OB (2014) A spectrum-aware clustering for efficient multimedia routing in cognitive radio sensor networks. IEEE Trans Veh Technol 63:3369–3380. https://doi.org/10.1109/TVT.2014.2300141

    Article  Google Scholar 

  26. Li C, Ye M, Chen G, Wu J (n.d.) An energy-efficient unequal clustering mechanism for wireless sensor networks. In: IEEE international conference on mobile adhoc sensor system 2005., IEEE. pp 597–604. https://doi.org/10.1109/mahss.2005.1542849

  27. Pei E, Han H, Sun Z, Shen B, Zhang T (2015) LEAUCH: low-energy adaptive uneven clustering hierarchy for cognitive radio sensor network. EURASIP J Wirel Commun Netw 2015:122. https://doi.org/10.1186/s13638-015-0354-x

    Article  Google Scholar 

  28. Motamedi A, Bahai A (2007) MAC protocol design for spectrum-agile wireless networks: stochastic control approach. In: 2007 2nd IEEE international symposium new frontiers in dynamic spectrum access networks, IEEE. pp 448–451. https://doi.org/10.1109/dyspan.2007.65

  29. Chen Yunxia, Zhao Qing (2005) On the lifetime of wireless sensor networks. IEEE Commun Lett 9:976–978. https://doi.org/10.1109/LCOMM.2005.11010

    Article  Google Scholar 

  30. Li X, Wang D, McNair J, Chen J (2011) Residual energy aware channel assignment in cognitive radio sensor networks. In: 2011 IEEE wireless communication network conference, IEEE. pp 398–403. https://doi.org/10.1109/wcnc.2011.5779196

  31. Liang Z, Feng S, Zhao D, Shen XS (2011) Delay performance analysis for supporting real-time traffic in a cognitive radio sensor network. IEEE Trans Wirel Commun 10:325–335. https://doi.org/10.1109/TWC.2010.111910.100804

    Article  Google Scholar 

  32. Bicen AO, Gungor VC, Akan OB (2012) Delay-sensitive and multimedia communication in cognitive radio sensor networks. Ad Hoc Netw 10:816–830. https://doi.org/10.1016/J.ADHOC.2011.01.021

    Article  Google Scholar 

  33. Lin S-C, Chen K-C (2014) Improving spectrum efficiency via in-network computations in cognitive radio sensor networks. IEEE Trans Wirel Commun 13:1222–1234. https://doi.org/10.1109/TWC.2014.011514.121905

    Article  Google Scholar 

  34. Quang PTA, Kim D-S (2014) Throughput-aware routing for industrial sensor networks: application to ISA100.11a. IEEE Trans Ind Inform 10:351–363. https://doi.org/10.1109/tii.2013.2255617

    Article  Google Scholar 

  35. Spachos P, Hantzinakos D (2014) Scalable dynamic routing protocol for cognitive radio sensor networks. IEEE Sens J 14:2257–2266. https://doi.org/10.1109/JSEN.2014.2309138

    Article  Google Scholar 

  36. Pei Y, Liang Y-C, Teh KC, Li KH (2011) Energy-efficient design of sequential channel sensing in cognitive radio networks: optimal sensing strategy, power allocation, and sensing order. IEEE J Sel Areas Commun 29:1648–1659. https://doi.org/10.1109/JSAC.2011.110914

    Article  Google Scholar 

  37. Han JeongAe (2011) WhaSookJeon, dong geunjeong, energy-efficient channel management scheme for cognitive radio sensor networks. IEEE Trans Veh Technol 60:1905–1910. https://doi.org/10.1109/TVT.2011.2128355

    Article  Google Scholar 

  38. Bhuvaneswari PTV, Vaidehi V, Saranya MA (2010) Distance based transmission power control scheme for indoor wireless sensor network. Springer, Berlin, pp 207–222. https://doi.org/10.1007/978-3-642-17697-5_11

    Book  Google Scholar 

  39. Jovanovic MD, Djordjevic GL (2007) TFMAC: Multi-channel MAC protocol for wireless sensor networks. In: 2007 8th international conference telecommunication modern satellite cable broadcasting service, IEEE. pp 23–26. https://doi.org/10.1109/telsks.2007.4375929

  40. Liu Y, Liang J, Xiao N, Yuan X, Zhang Z, Hu M, Hu Y (2017) Adaptive double threshold energy detection based on Markov model for cognitive radio. PLoS ONE 12:e0177625. https://doi.org/10.1371/journal.pone.0177625

    Article  Google Scholar 

  41. Heinzelman WR, Chandrakasan A, Balakrishnan H (n.d.) Energy-efficient communication protocol for wireless microsensor networks. In: Proceedings of 33rd annual hawaii international conference on system science, IEEE Computer Society. p 10. https://doi.org/10.1109/hicss.2000.926982

  42. Agarwal S, De S (2015) Impact of channel switching in energy constrained cognitive radio networks. IEEE Commun Lett 19:977–980. https://doi.org/10.1109/LCOMM.2015.2416334

    Article  Google Scholar 

  43. Huang X, Lu D, Li P, Fang Y (2011) Coolest path: spectrum mobility aware routing metrics in cognitive ad hoc networks. In: 2011 31st international conference distributor computer systems, IEEE. pp 182–191. https://doi.org/10.1109/icdcs.2011.34

  44. Osiadacz AJ (1989) Multiple criteria optimization; theory, computation, and application, Ralph E. Steuer, Wiley Series in Probability and Mathematical Statistics—Applied, Wiley, 1986, No. of pages 546, Price f5 1.40, $77.10. Optim Control Appl Methods 10:89–90. https://doi.org/10.1002/oca.4660100109

    Article  Google Scholar 

  45. Kim W, Oh SY, Gerla M, Lee KC (2011) CoRoute: A new cognitive anypath vehicular routing protocol. In: 2011 7th international wireless communication mobile computing conference, IEEE. pp 766–771. https://doi.org/10.1109/iwcmc.2011.5982643

  46. Saleem Y, Yau K-LA, Mohamad H, Ramli N, Rehmani MH (2015) SMART: A SpectruM-Aware ClusteR-based rouTing scheme for distributed cognitive radio networks. Comput Netw 91:196–224. https://doi.org/10.1016/J.COMNET.2015.08.019

    Article  Google Scholar 

  47. Hoyhtya M, Pollin S, Mammela A (2010) Classification-based predictive channel selection for cognitive radios. In: 2010 IEEE international conference communication, IEEE. pp 1–6. https://doi.org/10.1109/ICC.2010.5501787

  48. Zhang X, Yang S, Srivastava G, Chen M-Y, Cheng X (2020) Hybridization of cognitive computing for food services. Appl Soft Comput 89:106051. https://doi.org/10.1016/j.asoc.2019.106051

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science & Engineering, Amity School of Engineering and Technology, Amity University Uttar Pradesh, Noida, India

    Thompson Stephan

  2. Artificial Intelligence Department, Research Center for AI and IoT, Near East University, Nicosia, Mersin 10, Turkey

    Fadi Al-Turjman

  3. Department of Computer Science, Pondicherry University, Puducherry, India

    Suresh Joseph K

  4. School of Computer Science and Engineering, Galgotias University, Greater Noida, India

    Balamurugan Balusamy

Authors
  1. Thompson Stephan
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Fadi Al-Turjman
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Suresh Joseph K
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Balamurugan Balusamy
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Thompson Stephan.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Appendix

Appendix

Illustrations

Illustration for WCSE metric described in Sect. 4.1.

Figure 26 depicts the PU occupancy over six different channels, (n1, n2, n3, n4, n5 and n6) which is measured over forty four sampling intervals.

Fig. 26
figure 26

PU occupancy over six different channels

Full size image

The estimated CS values are shown in Table 12.

Table 12 Estimated CS values with weights and without weights
Full size table

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Stephan, T., Al-Turjman, F., K, S. et al. Energy and spectrum aware unequal clustering with deep learning based primary user classification in cognitive radio sensor networks. Int. J. Mach. Learn. & Cyber. 12, 3261–3294 (2021). https://doi.org/10.1007/s13042-020-01154-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13042-020-01154-y

Keywords

Search

Navigation