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A Deep Learning-Based Discrete-Time Markov Chain Analysis of Cognitive Radio Network for Sustainable Internet of Things in 5G-Enabled Smart City

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Abstract

The integration of cognitive radio-based Internet of Things devices in 5G network environments for smart city applications necessitates effective spectrum management. The critical aspect of spectrum management lies in making appropriate spectrum decisions for selecting idle channels that meet the quality of service requirements of secondary users while avoiding harmful interference with primary users (PUs). This article addresses the challenges by proposing an 8-state-based discrete-time Markov chain model to analyze the busy and idle times of PUs in CRNs. By leveraging this model, expressions for the traffic state and channel state belief vector are derived under imperfect sensing conditions. Additionally, a deep neural network (DNN)-based spectrum decision algorithm is introduced to optimize spectral resource utilization, considering spatial and temporal availability and energy-saving aspects in cognitive packet transmission. Our analytical and numerical evaluations demonstrate the superiority of the DNN-based algorithm over traditional methods, showcasing improved spectral resource utilization.

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References

  • Abadía JJ, Walther C, Osman A, Smarsly K (2022) A systematic survey of internet of things frameworks for smart city applications. Sustain Cities Soc 19:103949

    Article  Google Scholar 

  • Afzal H, Awan I, Mufti MR, Sheriff RE (2014 Dec 1) Modeling and analysis of customer premise equipments registration process in IEEE 802.22 WRAN cell. J Syst Soft 98:107–116

  • Ahmed R, Chen Y, Hassan B (2021) Deep learning-driven opportunistic spectrum access (OSA) framework for cognitive 5G and beyond 5G (B5G) networks. Ad Hoc Netw 1(123):102632

    Article  Google Scholar 

  • Akyildiz IF, Lo BF, Balakrishnan R (2011) Cooperative spectrum sensing in cognitive radio networks: a survey. Phys Commun 4(1):40–62

    Article  Google Scholar 

  • Alfa AS, Pla V, Martinez-Bauset J, Casares-Giner V (2016) Discrete time analysis of cognitive radio networks with imperfect sensing and saturated source of secondary users. Comput Commun 79:53–65

    Article  Google Scholar 

  • Alhusein D, Idrees AK (2023) A comprehensive review of wireless medical biosensor networks in connected healthcare applications. Enab Technol Effect Plan Manage Sustain Smart Cities 26:229–44

    Google Scholar 

  • Ali S, Aslam M.I., Ahmed I et al (2023) Uplink performance of narrowband internet-of-things devices in downlink-uplink decoupled-based heterogeneous networks. Iran J Sci Technol Trans Electr Eng 47:385–399

  • Bala I, Sharma A, Tselykh A, Kim BG (2022 May 31) Throughput optimization of interference limited cognitive radio-based internet of things (CR-IoT) network. J King Saud Univ Comput Inf Sci

  • Bermudez-Edo M, Elsaleh T, Barnaghi P, Taylor K (2016 Jul 18) IoT-Lite: a lightweight semantic model for the Internet of Things. In: 2016 INTL IEEE conferences on ubiquitous intelligence and computing, advanced and trusted computing, scalable computing and communications, cloud and big data computing, internet of people, and smart world congress, pp 90–97. IEEE

  • Bjornson E, Jorswieck EA, Debbah M, Ottersten B (2014) Multiobjective signal processing optimization: the way to balance conflicting metrics in 5G systems. IEEE Signal Process Mag 31(6):14–23

    Article  ADS  Google Scholar 

  • Cabric D, Mishra SM, Brodersen RW (2004 Nov 7) Implementation issues in spectrum sensing for cognitive radios. In: Conference record of the thirty-eighth asilomar conference on signals, systems and computers, 2004. (vol 1, pp 772–776). IEEE

  • Chakravarthy V, Li X, Zhou R, Wu Z, Temple M (2010) Novel overlay/underlay cognitive radio waveforms using SD-SMSE framework to enhance spectrum efficiency-part II: analysis in fading channels. IEEE Trans Commun 58(6):1868–76

    Article  Google Scholar 

  • Chhabra S, Aiden MK, Sabharwal SM, Al-Asadi M (2023) 5G and 6G technologies for smart city. inenabling technologies for effective planning and management in sustainable smart cities. Springer International Publishing, Cham, pp 335–365

  • Condoluci M, Sardis F, Mahmoodi T (2015 Oct 27) Softwarization and virtualization in 5G networks for smart cities. In: International internet of things summit. Springer, Cham, pp 179–186

  • Elhachmi J (2022) Distributed reinforcement learning for dynamic spectrum allocation in cognitive radio? Based internet of things. IET Netw 11(6):207–20

    Article  Google Scholar 

  • Garvanov I, Garvanova M, Borissova D, Vasovic B, Kanev D (2021 Jul 5) Towards IoT-based transport development in smart cities: safety and security aspects. In: International symposium on business modeling and software design. Springer, Cham, pp 392–398

  • Gelabert X, Sallent O, Pérez-Romero J, Agustí R (2010) Spectrum sharing in cognitive radio networks with imperfect sensing: a discrete-time Markov model. Comput Netw 54(14):2519–36

    Article  Google Scholar 

  • Ghaznavi M, Jamshidi A (2014) A reliable spectrum sensing method in the presence of malicious sensors in distributed cognitive radio network. IEEE Sens J 15(3):1810–6

    Google Scholar 

  • Ghaznavi M, Jamshidi A (2017) Defence against primary user emulation attack using statistical properties of the cognitive radio received power. IET Commun 11(9):1535–42

    Article  Google Scholar 

  • Ghofrani F, Jamshidi A, Keshavarz-Haddad A (2015 May 10) Internet traffic classification using Hidden Naive Bayes model. In: 2015 23rd Iranian conference on electrical engineering (pp 235–240). IEEE

  • Giral D, Hernández C, Rodríguez-Colina E (2020) Spectrum decision-making in collaborative cognitive radio networks. Appl Sci 10(19):6786

    Article  CAS  Google Scholar 

  • Global mobile data traffic forecast update. Cisco Visual Networking Index, White Pape (Feb 2019)

  • Gohar A, Nencioni G (2021) The role of 5G technologies in a smart city: the case for intelligent transportation system. Sustainability 13(9):5188

    Article  Google Scholar 

  • Habibzadeh H, Soyata T, Kantarci B, Boukerche A, Kaptan C (2018) Sensing, communication and security planes: a new challenge for a smart city system design. Comput Netw 24(144):163–200

    Article  Google Scholar 

  • He S, Shi K, Liu C, Guo B, Chen J, Shi Z (2022 Jun 29) Collaborative sensing in internet of things: a comprehensive survey. IEEE Commun Surv Tutorials

  • Hooshiary A, Azmi P, Mokari N, Maleki S (2018) Optimal channel selection for simultaneous RF energy harvesting and data transmission in cognitive radio networks. Trans Emerg Telecommun Technol 29(3):e3291

    Article  Google Scholar 

  • Hossain MA, Schukat M, Barrett E (2021 Jan 27) A reliable energy and spectral efficient spectrum sensing approach for cognitive radio based IoT networks. In: 2021 IEEE 11th annual computing and communication workshop and conference (CCWC) (pp 1569–1576). IEEE

  • Hu F, Chen B, Zhai X, Zhu C (2016) Channel selection policy in multi-su and multi-pu cognitive radio networks with energy harvesting for internet of everything. Mobile Inf Syst 1:2016

    Google Scholar 

  • Huang S, Liu X, Ding Z (2009) Optimal transmission strategies for dynamic spectrum access in cognitive radio networks. IEEE Trans Mobile Comput 8(12):1636–48

    Article  Google Scholar 

  • Hudson H (2019 Jul 27) 5G mobile broadband: spectrum challenges for rural regions. In: TPRC47: the 47th research conference on communication, information and internet policy

  • Huseien GF, Shah KW (2022) A review on 5G technology for smart energy management and smart buildings in Singapore. Energy AI 1(7):100116

    Article  Google Scholar 

  • Hyoil K, Shin Kang G (2008) Efficient discovery of spectrum opportunities with MAC-layer sensing in cognitive radio networks. IEEE Trans Mobile Comput 7(5):533–545

    Article  Google Scholar 

  • Islam S, Budati AK, Mohammad KH, Goyal SB, Raju D (2023) A multi-sensory real-time data transmission method with sustainable and robust 5G energy signals for smart cities. Sustain Energy Technol Assess 1(57):103278

    Google Scholar 

  • Jamshidi A (2009) Performance analysis of low average reporting bits cognitive radio schemes in bandwidth constraint control channels. IET Commun 3(9):1544–56

    Article  Google Scholar 

  • Jamshidi A, Nasiri-Kenari M, Zeinalpour Z, Taherpour A (2007) Space?frequency coded cooperation in OFDM multiple-access wireless networks. IET Commun 1(6):1152–60

    Article  MathSciNet  Google Scholar 

  • Jin Z, Qiao Y, Liu A, Zhang L (2018) EESS: an energy-efficient spectrum sensing method by optimizing spectrum sensing node in cognitive radio sensor networks. Wireless Commun Mobile Comput 11:2018

    Google Scholar 

  • Kakalou I, Papadopoulou D, Xifilidis T, Psannis KE, Siakavara K, Ishibashi Y (2018 May 7) A survey on spectrum sensing algorithms for cognitive radio networks. In: 2018 7th International conference on modern circuits and systems technologies (MOCAST) (pp 1–4). IEEE

  • Kang X, Liang YC, Garg HK, Zhang L (2009) Sensing-based spectrum sharing in cognitive radio networks. IEEE Trans Veh Technol 58(8):4649–54

    Article  Google Scholar 

  • Kang JJ, Yang W, Dermody G, Ghasemian M, Adibi S, Haskell-Dowland P (2020) No soldiers left behind: an IoT-based low-power military mobile health system design. IEEE Access 4(8):201498–515

    Article  Google Scholar 

  • Khader AA, Ayoub ZA (2020) The cognitive radio and internet of things. Eur J Eng Technol Res 5(8):899–903

    Google Scholar 

  • Khambekar N, Spooner CM, Chaudhary V (2014 Apr 1) On improving serviceability with quantified dynamic spectrum access. In: 2014 IEEE international symposium on dynamic spectrum access networks (DYSPAN) (pp 553–564). IEEE

  • Khan AA, Rehmani MH, Rachedi A (2016 Sep 5) When cognitive radio meets the internet of things?. In: 2016 International wireless communications and mobile computing conference (IWCMC) (pp 469–474). IEEE

  • Khan AA, Rehmani MH, Rachedi A (2017) Cognitive-radio-based internet of things: applications, architectures, spectrum related functionalities, and future research directions. IEEE wireless Commun 24(3):17–25

    Article  Google Scholar 

  • Kim H, Shin KG (2008) Efficient discovery of spectrum opportunities with MAC-layer sensing in cognitive radio networks. IEEE Trans Mobile Comput 7(5):533–45

    Article  Google Scholar 

  • Li T, Yuan J, Torlak M (2018) Network throughput optimization for random access narrowband cognitive radio Internet of Things (NB-CR-IoT). IEEE Internet Things J 5(3):1436–48

    Article  Google Scholar 

  • Liu X, Li Y, Zhang X, Lu W, Xiong M (2020) Energy-efficient resource optimization in green cognitive internet of things. Mobile Netw Appl 25(6):2527–35

    Article  Google Scholar 

  • Liu X, Krishnamachari B, Liu H (2010 Apr 6) Channel selection in multi-channel opportunistic spectrum access networks with perfect sensing. In: 2010 IEEE symposium on new frontiers in dynamic spectrum (DySPAN) (pp 1–8). IEEE

  • Loganathan J, Latchoumi TP, Janakiraman S, parthiban L (2016 Aug 25) A novel multi-criteria channel decision in co-operative cognitive radio network using E-TOPSIS. In: Proceedings of the international conference on informatics and analytics (pp 1–6)

  • Lu W, Hu S, Liu X, He C, Gong Y (2019) Incentive mechanism based cooperative spectrum sharing for OFDM cognitive IoT network. IEEE Trans Netw Sci Eng 7(2):662–72

    Article  MathSciNet  Google Scholar 

  • Manesh MR, Apu MS, Kaabouch N, Hu WC (2016 Oct 20) Performance evaluation of spectrum sensing techniques for cognitive radio systems. In: 2016 IEEE 7th annual ubiquitous computing, electronics and mobile communication conference (UEMCON) (pp 1–7). IEEE

  • Nandakumar S, Velmurugan T, Thiagarajan U, Karuppiah M, Hassan MM, Alelaiwi A, Islam MM (2019) Efficient spectrum management techniques for cognitive radio networks for proximity service. IEEE Access 20(7):43795–805

    Article  Google Scholar 

  • Nguyen VD, Duong TQ, Vien QT (2020) Emerging techniques and applications for 5G networks and beyond. Mobile Netw Appl 25(5):1984–6

    Article  Google Scholar 

  • Papadias CB, Ratnarajah T, Slock DT (2020 Jun 2) editors. Spectrum sharing: the next frontier in wireless networks. John Wiley & Sons

  • Patil VM, Patil SR (2016 Mar 3) A survey on spectrum sensing algorithms for cognitive radio. In: 2016 International conference on advances in human machine interaction (HMI) (pp 1–5). IEEE

  • Pei Y, Liang YC, 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 Selected Areas Commun 29(8):1648–59

    Article  Google Scholar 

  • Piran MJ, Pham QV, Islam SR, Cho S, Bae B, Suh DY, Han Z (2020) Multimedia communication over cognitive radio networks from QoS/QoE perspective: a comprehensive survey. J Netw Comput Appl 15:102759

    Article  Google Scholar 

  • Pla V, Alfa AS, Martinez-Bauset J, Casares-Giner V (2019) Discrete-time analysis of cognitive radio networks with nonsaturated source of secondary users. Wireless Commun Mobile Comput 2:2019

    Google Scholar 

  • Prabhavathi S, Saminadan V (2022) Energy efficient allocation of resources in NOMA based (MU-HCRN) with perfect spectrum sensing. In: International conference on computing science, communication and security (pp 274–285). Springer, Cham

  • Rongfei F et al (2016) Adaptive channel selection and slot length conguration in cognitive radio. Wireless Commun Mobile Comput 16:2636–2648

    Article  Google Scholar 

  • Rusti B, Stefanescu H, Ghenta J, Patachia C (2018 Jun 14) Smart city as a 5G ready application. In: 2018 International conference on communications (COMM) (pp 207–212). IEEE

  • Sardar AA, Roy D, Mondal WU, Das G (2021 Mar 16) Queuing analysis of opportunistic cognitive radio iot network with imperfect sensing. arXiv preprint arXiv:2103.08875

  • Sethi P, Sarangi SR (2017) Internet of things: architectures, protocols, and applications. J Electrical Comput Eng 26:2017

    Google Scholar 

  • Shakeel PM, Baskar S, Fouad H, Manogaran G, Saravanan V, Xin Q (2021) Creating collision-free communication in IoT with 6G using multiple machine access learning collision avoidance protocol. Mobile Netw Appl 26(3):969–80

    Article  Google Scholar 

  • Sharma A, Sharma V, Jaiswal M, Wang HC, Jayakody DN, Basnayaka CM, Muthanna A (2022) Recent trends in AI-based intelligent sensing. Electronics 11(10):1661

    Article  CAS  Google Scholar 

  • Shin KG, Kim H, Min AW, Kumar A (2010) Cognitive radios for dynamic spectrum access: from concept to reality. IEEE Wireless Commun 17(6):64–74

    Article  Google Scholar 

  • Taherpour A, Nasiri-Kenari M, Jamshidi A (2007 Sep 3) Efficient cooperative spectrum sensing in cognitive radio networks. In: 2007 IEEE 18th international symposium on personal, indoor and mobile radio communications (pp 1–6). IEEE

  • Taiwo JF, Prisca OI, Matthew UO, Onyebuchi A, Nwamouh UC, Robert UI, Matthew AO (2022) IoT drone technology integration in medical logistics delivery. Science 10(3):124–33

    Google Scholar 

  • Tan SS, Zeidler J, Rao B (2013) Opportunistic channel-aware spectrum access for cognitive radio networks with interleaved transmission and sensing. IEEE Trans Wireless Commun 12(5):2376–88

    Article  Google Scholar 

  • Teekaraman Y, Manoharan H, Basha AR, Manoharan A (2020) Hybrid optimization algorithms for resource allocation in heterogeneous cognitive radio networks. Neural Process Lett 29:1–4

    Google Scholar 

  • Tlouyamma J, Velempini M (2021) Investigative analysis of channel selection algorithms in cooperative spectrum sensing in cognitive radio networks. SAIEE Afr Res J 112(1):4–14

    Article  Google Scholar 

  • Towhidlou V, Shikh-Bahaei M (2018) Adaptive full-duplex communications in cognitive radio networks. IEEE Trans Veh Technol 67(9):8386–95

    Article  Google Scholar 

  • Tufail A, Namoun A, Alrehaili A, Ali A (2021) A survey on 5G enabled multi-access edge computing for smart cities: issues and future prospects. Int J Comput Sci Netw Security 21(6):107–18

    Google Scholar 

  • Vimal S, Kalaivani L, Kaliappan M, Suresh A, Gao XZ, Varatharajan R (2020) Development of secured data transmission using machine learning-based discrete-time partially observed Markov model and energy optimization in cognitive radio networks. Neural Comput Appl 32(1):151–61

    Article  Google Scholar 

  • Vimal S, Jeyabalaraja V, Subbulakshmi P, Suresh A, Kaliappan M, Koteeswaran S (2020 Jun 2) Deep learning-based decision-making with WoT for smart city development. In: Smart innovation of web of things (pp 51–62). CRC Press

  • Wei Z, Jiang H (2019) Optimal slot length configuration in cognitive radio networks. IEEE Access 7(7):78037–49

    Article  Google Scholar 

  • Wellens M, Riihijärvi J, Mähönen P (2009) Empirical time and frequency domain models of spectrum use. Phys Commun 2(1–2):10–32

    Article  Google Scholar 

  • Wu Q, Ding G, Xu Y, Feng S, Du Z, Wang J, Long K (2014) Cognitive internet of things: a new paradigm beyond connection. IEEE Internet Things J 1(2):129–43

    Article  Google Scholar 

  • Wu M, Lu TJ, Ling FY, Sun J, Du HY (2010 Aug 20) Research on the architecture of internet of things. In: 2010 3rd International conference on advanced computer theory and engineering (ICACTE) (vol 5, pp V5-484). IEEE

  • Yu H, Zikria YB (2020) Cognitive radio networks for internet of things and wireless sensor networks. Sensors 20(18):1-6

  • Zhu X, Shen L, Yum TS (2007) Analysis of cognitive radio spectrum access with optimal channel reservation. IEEE Commun Lett 11(4):304–6

    Article  Google Scholar 

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  1. Veer Surendra Sai Unuversity of Technology, Burla, India

    Subrat Kumar Sethi & Arunanshu Mahapatro

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Sethi, S.K., Mahapatro, A. A Deep Learning-Based Discrete-Time Markov Chain Analysis of Cognitive Radio Network for Sustainable Internet of Things in 5G-Enabled Smart City. Iran J Sci Technol Trans Electr Eng 48, 37–64 (2024). https://doi.org/10.1007/s40998-023-00665-y

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