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An enhanced energy-efficient fuzzy-based cognitive radio scheme for IoT

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Abstract

Energy is a critical factor to be considered in electrical and electronic systems. With the advent of technology, numerous techniques have been developed in communication systems to make the systems reliable, durable, and economic. In modern communication systems, the major requirements of an efficient radio model are to improve the delay, and throughput, reduce the energy consumption, and extend the network lifetime. So, there is a need to design a radio model to improve the quality of service (QoS) parameters. From the limitations identified in the wireless communication networks, the authors proposed an Enhanced Energy-Efficient Fuzzy-based Cognitive Radio scheme for Internet of things (IoT) networks. The proposed protocol is compared with the conventional method, Cognitive Radio-based Heterogeneous Wireless Sensor Area Network. The test-bed results show that the EEFCR protocol has achieved a significant gain on sum goodput versus a number of secondary radio users, average probability of bit error, computational time vs. sensor nodes, delay vs. sensing time. The computational time of the EEFCR protocol is shown to be 5% to 7% and 15% to 21% faster while comparing to CoRHAN and conventional methods. The EEFCR sensing time is reduced up to 80%. The average computational time for 500 nodes is reduced up to 40%. Also, 53% increment is achieved in spectrum utilization. The average bit error is reduced up to 5%.

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References

  1. Chithaluru P, Kumar S, Singh A, Benslimane A, Jangir SK (2021) An energy-efficient routing scheduling based on fuzzy ranking scheme for internet of things. IEEE Internet Things J 9(10):7251–7260

    Article  Google Scholar 

  2. Chithaluru P, Al-Turjman F, Kumar M, Stephan T (2021) MTCEE-LLN: multilayer threshold cluster-based energy efficient low power and lossy networks for industrial internet of things. IEEE Internet Things J 9(7):4940–4948

    Article  Google Scholar 

  3. Chithaluru P, Al-Turjman F, Stephan T, Kumar M, Mostarda L (2021) Energy-efficient blockchain implementation for cognitive wireless communication networks (CWCNs). Energy Rep 7:8277–8286

    Article  Google Scholar 

  4. Chithaluru PK, Khan MS, Kumar M, Stephan T (2021) ETH-LEACH: an energy enhanced threshold routing protocol for WSNs. Int J Commun Syst 34(12):e4881

    Article  Google Scholar 

  5. Chithaluru P, Tiwari R, Kumar K (2021) ARIOR: adaptive ranking-based improved opportunistic routing in wireless sensor networks. Wirel Personal Commun 116(1):153–176

    Article  Google Scholar 

  6. Haykin S (2005) Cognitive radio: brain-empowered wireless communications. IEEE J Sel Areas Commun 23(2):201–220

    Article  Google Scholar 

  7. Chithaluru P, Al-Turjman F, Kumar M, Stephan T (2020) I-AREOR: an energy-balanced clustering protocol for implementing green IoT in smart cities. Sustain Cities Soc 61:102254

    Article  Google Scholar 

  8. Chithaluru P, Tiwari R, Kumar K (2019) AREOR-an adaptive ranking-based energy-efficient opportunistic routing scheme in Wireless Sensor Network. Comput Netw 162:106863

    Article  Google Scholar 

  9. Akan OB, Karli OB, Ergul O (2009) Cognitive radio sensor networks. IEEE Netw 23(4):34–40

    Article  Google Scholar 

  10. Mitola J, Maguire GQ (1999) Cognitive radio: making software radios more personal. IEEE Personal Commun 6(4):13–18

    Article  Google Scholar 

  11. Carie A, Li M, Marapelli B, Reddy P, Dino H, Gohar M (2019) Cognitive radio assisted WSN with interference-aware AODV routing protocol. J Ambient Intell Humaniz Comput 10(10):4033–4042

    Article  Google Scholar 

  12. 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):17–25

    Article  Google Scholar 

  13. Zhang ZY, Jin CH, Liang XL, Chen Q, Peng LM (2006) Current-voltage characteristics and parameter retrieval of semiconducting nanowires. Appl Phys Lett 88(7):073102

    Article  Google Scholar 

  14. Özgür Ü, Alivov Y, Morkoç H (2009) Microwave ferrites, part 1: fundamental properties. J Mater Sci Mater Electron 20(9):789–834

    Article  Google Scholar 

  15. Isik MT, Akan OB (2009) A three-dimensional localization algorithm for underwater acoustic sensor networks. IEEE Trans Wirel Commun 8(9):4457–4463

    Article  Google Scholar 

  16. Tachwali Y, Basma F, Refai HH (2010) Cognitive radio architecture for rapidly deployable heterogeneous wireless networks. IEEE Trans Consumer Electron 56(3):1426–1432

    Article  Google Scholar 

  17. Bambos N, Chen SC, Pottie GJ (2000) Channel access algorithms with active link protection for wireless communication networks with power control. IEEE/ACM Trans Netw 8(5):583–597

    Article  Google Scholar 

  18. Li S, Da Xu L, Wang X (2012) Compressed sensing signal and data acquisition in wireless sensor networks and internet of things. IEEE Trans Ind Inf 9(4):2177–2186

    Article  Google Scholar 

  19. Xie L, Jia X, Zhou K (2012) QoS multicast routing in cognitive radio ad hoc networks. Int J Commun Syst 25(1):30–46

    Article  Google Scholar 

  20. Yuan F, Zhan Y, Wang Y (2013) Data density correlation degree clustering method for data aggregation in WSN. IEEE Sens J 14(4):1089–1098

    Article  Google Scholar 

  21. Kumar S (2017) Compartmental modeling of opportunistic signals for energy-efficient optimal clustering in WSN. IEEE Commun Lett 22(1):173–176

    Article  Google Scholar 

  22. Hossain E, Bhargava VK (eds) (2007) Cognitive wireless communication networks. Springer Science & Business Media, London

    Google Scholar 

  23. Doyle L (2009) Essentials of cognitive radio. Cambridge University Press, Cambridge

    Book  Google Scholar 

  24. Cabric D, Tkachenko A, Brodersen RW (2006) Spectrum sensing measurements of a pilot, energy, and collaborative detection. In: Milcom 2006–2006 IEEE military communications conference, IEEE, pp 1–7

  25. Luo L, Roy S (2007) Analysis of search schemes in cognitive radio. In: 2007 2nd IEEE workshop on networking technologies for software define radio networks, IEEE, pp, 17–24

  26. Ahmad R, Wazirali R, Bsoul Q, Abu-Ain T, Abu-Ain W (2021) Feature-selection and mutual-clustering approaches to improve dos detection and maintain WSNs’ lifetime. Sensors 21(14):4821

    Article  Google Scholar 

  27. Selvakumar K, Karuppiah M, SaiRamesh L, Islam SH, Hassan MM, Fortino G, Choo KKR (2019) Intelligent temporal classification and fuzzy rough set-based feature selection algorithm for intrusion detection system in WSNs. Inf Sci 497:77–90

    Article  Google Scholar 

  28. Mitola J (1999) Cognitive radio for flexible mobile multimedia communications. In: 1999 IEEE international workshop on mobile multimedia communications (MoMuC’99)(Cat. No. 99EX384), IEEE, pp. 3–10

  29. Tandra R, Sahai A (2007) SNR walls for feature detectors. In: 2007 2nd IEEE international symposium on new frontiers in dynamic spectrum access networks, IEEE, pp 559–570

  30. Nisioti E, Thomos N (2019) Robust coordinated reinforcement learning for MAC design in sensor networks. IEEE J Sel Areas Commun 37(10):2211–2224

    Article  Google Scholar 

  31. Meng W, Li W, Xiang Y, Choo KKR (2017) A bayesian inference-based detection mechanism to defend medical smartphone networks against insider attacks. J Netw Comput Appl 78:162–169

    Article  Google Scholar 

  32. Akyildiz IF, Lee WY, Vuran MC, Mohanty S (2006) NeXt generation/dynamic spectrum access/cognitive radio wireless networks: a survey. Comput Netw 50(13):2127–2159

    Article  Google Scholar 

  33. Chowdhury KR, Akyildiz IF (2008) Cognitive wireless mesh networks with dynamic spectrum access. IEEE J Sel Areas Commun 26(1):168–181

    Article  Google Scholar 

  34. Digham FF, Alouini MS (2003) Variable rate variable-power hybrid M-FSK M-QAM for fading channels. In: 2003 IEEE 58th Vehicular technology conference. VTC 2003-Fall (IEEE Cat. No. 03CH37484), Vol. 3 IEEE. pp. 1512-1516

  35. Bharadia D, Bansal G, Kaligineedi P, Bhargava VK (2011) Relay and power allocation schemes for OFDM-based cognitive radio systems. IEEE Trans Wirel Commun 10(9):2812–2817

    Article  Google Scholar 

  36. He W, Cao J (2008) Robust stability of genetic regulatory networks with distributed delay. Cognit Neurodyn 2(4):355

    Article  Google Scholar 

  37. Chithaluru P, Tiwari R, Kumar K (2021) Performance analysis of energy efficient opportunistic routing protocols in wireless sensor network. Int J Sens Wirel Commun Control 11(1):24–41

    Google Scholar 

  38. Ramakuri SK, Chithaluru P, Kumar S (2019) Eyeblink robot control using brain-computer interface for healthcare applications. Int J Mobile Devices Wearable Technol Flex Electron (IJMDWTFE) 10(2):38–50

    Article  Google Scholar 

  39. Chithaluru P, Prakash R (2020) Organization security policies and their after effects. Information security and optimization. Chapman and Hall/CRC, New York, pp 43–60

    Chapter  Google Scholar 

  40. Chithaluru P, Tanwar R, Kumar S (2020) Cyber-attacks and their impact on real life: what are real-life cyber-attacks, how do they affect real life and what should we do about them? Information security and optimization. Chapman and Hall/CRC, New York, pp 61–77

    Chapter  Google Scholar 

  41. Chithaluru P, Singh K, Sharma MK (2020) Cryptocurrency and Blockchain. Information security and optimization. Chapman and Hall/CRC, New York, pp 143–158

    Chapter  Google Scholar 

  42. Prakash R, Chithaluru P (2021) Active security by implementing intrusion detection and facial recognition. Nanoelectronics, circuits and communication systems. Springer, Singapore, pp 1–7

    Google Scholar 

  43. Prakash R, Chithaluru P, Sharma D, Srikanth P (2019) Implementation of trapdoor functionality to two-layer encryption and decryption by using RSA-AES cryptography algorithms. Nanoelectronics, circuits and communication systems. Springer, Singapore, pp 89–95

    Chapter  Google Scholar 

  44. Chithaluru P, Prakash R, Srivastava S (2018) WSN structure based on SDN. Innovations in software-defined networking and network functions virtualization. IGI Global, Pennsylvania, pp 240–253

    Chapter  Google Scholar 

  45. Chithaluru P, Prakash R (2018) Simulation on SDN and NFV models through mininet. Innovations in software-defined networking and network functions virtualization. IGI Global, Pennsylvania, pp 149–174

    Chapter  Google Scholar 

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation (KLEF), Vaddeswaram, Andhra Pradesh, 522302, India

    Premkumar Chithaluru

  2. Department of Computer Science and Engineering, Faculty of Engineering and Technology, M. S. Ramaiah University of Applied Sciences, Bangalore, Karnataka, India

    Thompson Stephan

  3. School of Computer Science, University of Petroleum and Energy Studies, Bidoli, Dehradun, India

    Manoj Kumar

  4. Graduate School, Faculty of Information Technology, Duy Tan University, Da Nang, 550000, Vietnam

    Anand Nayyar

  5. Faculty of Engineering & Information Sciences, University of Wollongong in Dubai (UOWD), Dubai, UAE

    Manoj Kumar

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  1. Premkumar Chithaluru
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  2. Thompson Stephan
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  3. Manoj Kumar
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  4. Anand Nayyar
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Correspondence to Anand Nayyar.

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Chithaluru, P., Stephan, T., Kumar, M. et al. An enhanced energy-efficient fuzzy-based cognitive radio scheme for IoT. Neural Comput & Applic 34, 19193–19215 (2022). https://doi.org/10.1007/s00521-022-07515-8

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  • DOI: https://doi.org/10.1007/s00521-022-07515-8

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