Firefly-inspired stochastic resonance for spectrum sensing in CR-based IoT communications

  • Haftu Tasew RedaEmail author
  • Abdun Mahmood
  • Abebe Diro
  • Naveen Chilamkurti
  • Suresh Kallam
Applying Artificial Intelligence to the Internet of Things


The exponential increase in the number of the Internet of Things (IoT) necessitates dynamic and shared spectrum access at the edge of network. In cognitive radio (CR)-based IoT communications, spectrum sensing (SS) plays a pivotal role, if designed carefully, to enable a coexistence between licensed users (LUs) and unlicensed IoT devices for efficient and dynamic spectrum utilization. Though several SS techniques have been proposed in the literature, energy detection (ED) is renowned for its time and resource efficiency. Despite its suitability for IoT devices owing to its low hardware complexity and absence of a priori LU information, the detection performance of ED is poor at very low signal-to-noise ratio (SNR) channel conditions. While cooperative sensing can alleviate the performance problem of ED sensing in IoT network, significant detection cannot be achieved under adverse channel environments using non-cooperative IoT applications. Recently, stochastic resonance (SR) has been employed in CRs to boost the performance of SS in weak signal detection. In this paper, we propose a metaheuristic firefly algorithm (FFA) to determine the SR parameters through an objective function defined by the output SNR of a dynamic IoT system. In particular, we use an optimization scheme to optimally compute a noise level to achieve the best SR effect. Hence, the proposed FFA-based optimization problem significantly improves the sensing time and utilization of IoT communication channels in the weak heterogeneous IoT application introductions into the market. Our proposed system achieves a better detection probability of 80% compared to the 50% obtained through previous SR-based ED research works taking into account of SNR value of − 20 dB and a 10% false alarm probability (\(Q_{\rm FA}\)). Moreover, for SNR value of − 20 dB, the sensing error probability of our proposed technique (20%) is 30% less than the previous SR-based ED considering \(Q_{\rm FA}\) = 5%.


Internet of things Spectrum sensing Energy detection Stochastic resonance Firefly algorithm Licensed user Secondary user 


Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest regarding the publication of this paper.


  1. 1.
    Diro A, Chilamkurti N (2018) Distributed attack detection scheme using deep learning approach for internet of things. J Future Gener Comput Syst 82:1–11CrossRefGoogle Scholar
  2. 2.
    Diro A, Reda H, Chilamkurti N (2018) Differential flow space allocation scheme in SDN based fog computing for IoT applications. J Ambient Intell Human Comput 22:1–11Google Scholar
  3. 3.
    Zeng Y, Liang YC, Hoang AT, Zhang R (2010) A review on spectrum sensing techniques for cognitive radio: challenges and solutions. EURASIP J Adv Signal Process 1:1–15Google Scholar
  4. 4.
    Liang YC, Chen K, Li GY, Mahonen P (2011) Cognitive radio networking and communications: an overview. IEEE Trans Veh Technol 60(7):3386–3407CrossRefGoogle Scholar
  5. 5.
    Hossain E, Niyato D, Han Z (2009) Dynamic spectrum access and management in cognitive radio networks. Cambridge University Press, CambridgeCrossRefGoogle Scholar
  6. 6.
    Wang N, Gao Y, Zhang X (2013) Adaptive spectrum sensing algorithm under different primary user utilizations. IEEE Commun Lett 17(9):1838–1841CrossRefGoogle Scholar
  7. 7.
    Tandra R, Sahai A (2005) Fundamental limits on detection in low SNR under noise uncertainty. Int Conf Wirel Netw Commun Mobile Comput 1:464–469Google Scholar
  8. 8.
    Rauniyar A, Shin SY (2015) Multiple antenna-aided cascaded energy and matched filter detector for cognitive radio networks. Int J Distrib Sens Netw 11(9):1–9CrossRefGoogle Scholar
  9. 9.
    Khambekar N, Dong L, Chaudhary V (2007 Mar) Utilizing OFDM guard interval for spectrum sensing. In: Proceedings of the IEEE wireless communication and networking conference, Hong Kong, pp 38-42Google Scholar
  10. 10.
    IEEE (2011) IEEE standard for information technology-local and metropolitan area networks-specific requirements-part 22: cognitive wireless ran medium access control (MAC) and physical layer (PHY) specifications: policies and procedures for operation in the TV. IEEE Std 802.22, pp 1–680, July 1Google Scholar
  11. 11.
    Wang H, Noh G, Kim D, Kim S, Hong D (2010) Advanced sensing techniques of energy detection in cognitive radios. J Commun Netw 12(1):19–29CrossRefGoogle Scholar
  12. 12.
    Tandra R, Sahai A (2008) SNR walls for signal detection. IEEE J Sel Top Signal Process 2(1):4–17CrossRefGoogle Scholar
  13. 13.
    Ganesan G, Li Y (2007) Cooperative spectrum sensing in cognitive radio, part I: two user networks. IEEE Trans Wirel Commun 6(6):2204–2213CrossRefGoogle Scholar
  14. 14.
    Rauniyar A, Shin SY (2015) A novel energy-efficient clustering based cooperative spectrum sensing for cognitive radio sensor networks. Int J Distrib Sen Netw 2015:1–8Google Scholar
  15. 15.
    Wang H, Su X, Xu Y, Chen X, Wang J (2010) Snr wall and cooperative spectrum sensing in cognitive radio under noise uncertainty. J Electron 27:611–617Google Scholar
  16. 16.
    Zeng Y, Liang YC (2009) Eigenvalue-based spectrum sensing algorithms for cognitive radio. IEEE Trans Commun 57(6):1784–1793CrossRefGoogle Scholar
  17. 17.
    Charan Chhagan, Pandey Rajoo (2016) Eigenvalue based double threshold spectrum sensing under noise uncertainty for cognitive radio. Optik Int J Light Electron Opt 127(15):5968–5975CrossRefGoogle Scholar
  18. 18.
    Reda HT, Daely PT, Kharel J, Shin SY (2017) On the application of IoT: meteorological information display system based on LoRa wireless communication. IETE Tech Rev 0:1–10Google Scholar
  19. 19.
    Daely PT, Reda HT, Satrya GB, Kim JW, Shin SY (2017) Design of smart LED streetlight system for smart city with web-based management system. IEEE Sens J 17(18):6100–6110CrossRefGoogle Scholar
  20. 20.
    Gammaiton L, Hanggi P, Jung P, Marchesoni F (1998) Stochastic resonance. Rev Mod Phys 70(1):223–287CrossRefGoogle Scholar
  21. 21.
    McNamara B, Wiesenfeld K (1989) Theory of stochastic resonance. Phys Rev A 39(9):4854–4869CrossRefGoogle Scholar
  22. 22.
    Mitaim S, Kosko B (1998) Adaptive stochastic resonance. Proc IEEE 86(11):2152–2183CrossRefGoogle Scholar
  23. 23.
    Zhang S, Wang J, Zhong M, Li S (2013 Nov) Adaptive stochastic resonance aided energy detection with modified periodogram. In: International conference on communications, circuits and systems (ICCCAS), pp 72–76Google Scholar
  24. 24.
    Wang J, Ren X, Zhang S, Zhang D, Li H, Li S (2014) Adaptive bistable stochastic resonance aided spectrum sensing. IEEE Trans Wirel Commun 13(7):4014–4024CrossRefGoogle Scholar
  25. 25.
    He D (2010) Breaking the SNR wall of spectrum sensing in cognitive radio by using the chaotic stochastic resonance. In: Proceedings of 2010 IEEE international symposium on circuits and systems, pp 61–64Google Scholar
  26. 26.
    Liu J, Li Z (2015) Lowering the signal-to-noise ratio wall for energy detection using parameter-induced stochastic resonator. IET Commun 9(1):101–107CrossRefGoogle Scholar
  27. 27.
    Yang XS (2010) Nature-inspired metaheuristic algorithms. Luniver Press, Bristol, pp 81–89Google Scholar
  28. 28.
    Yang XS (2009) Firefly algorithms for multimodal optimization. In: Watanabe O, Zeugmann T (eds) Stochastic algorithms: foundations and appplications, SAGA 2009, lecture notes in computer science, vol 5792. Springer, Berlin, pp 169–178Google Scholar
  29. 29.
    Chen W, Wang J, Li H, Li S (2010) Stochastic resonance noise enhanced spectrum sensing in cognitive radio networks. In: 2010 IEEE global telecommunications conference GLOBECOM, pp 1–6Google Scholar
  30. 30.
    Zheng K, Li H, Djouadi SM, Wang J (2010) Spectrum sensing in low SNR regime via stochastic resonance. In: 2010 44th annual conference on information sciences and systems (CISS)Google Scholar
  31. 31.
    Guo G, Mandal M (2010) Design of stochastic-resonator-based detector using bistable system. In: 2010 International conference on signal processing and communications (SPCOM), Bangalore, pp 1–5Google Scholar
  32. 32.
    He D, Lin Y, He C, Jiang L (2010) A novel spectrum-sensing technique in cognitive radio based on stochastic resonance. IEEE Trans Veh Technol 59(4):1680–1688CrossRefGoogle Scholar
  33. 33.
    Tasew RH, Shin SY (2016) Cascaded learning system for SR noise induced spectrum sensing in cognitive radio network. In: 2016 Eighth international conference on ubiquitous and future networks (ICUFN), Vienna, pp 436–440Google Scholar
  34. 34.
    Khan AA, Rehmani MH, Rachedi A (2017) Cognitive-radio-based internet of things: applications, architectures, spectrum related functionalities, and future research directions. IEEE Wirel Commun 24(3):17–25CrossRefGoogle Scholar
  35. 35.
    Digham FF, Alouini MS, Simon MK (2003) On the energy detection of unknown signals over fading channels. In: IEEE international conference on communications, pp 3575–3579Google Scholar
  36. 36.
    Atapattu S, Tellambura C, Jiang H (2014) Conventional energy detector. Energy detection for spectrum sensing in cognitive radio, springer briefs in computer science, ch. 2. Springer, Berlin, pp 18–22CrossRefGoogle Scholar
  37. 37.
    Anishchenko VS, Astakhov V, Neiman A, Vadivasova T (2007) Stochastic dynamics. Nonlinear dynamics of chaotic and stochastic systems, ch. 3, section 3.1. Springer, Berlin, pp 18–22Google Scholar
  38. 38.
    Rautenberg W (2004) Ordered sets and linear orderings. Logical reasoning: a first course and texts in computing, Ch. 20, 3rd edn. King’s College Publications, London, pp 330–334Google Scholar
  39. 39.
    Lee W y, Akyildiz IF (2008) Optimal spectrum sensing framework for cognitive radio networks. IEEE Trans Wireless Commun 7(10):3845–3857CrossRefGoogle Scholar

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© Springer-Verlag London Ltd., part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Computer Science and ITLa Trobe UniversityMelbourneAustralia
  2. 2.Sreevidyanikethan Engineering College, Autonomous InstituteHyderabadIndia

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