Research article

Intelligence-based optimized cognitive radio routing for medical data transmission using IoT


  • Received: 11 May 2022 Revised: 28 June 2022 Accepted: 06 July 2022 Published: 25 July 2022
  • The Internet of Things (IoT) is considered an effective wireless communication, where the main challenge is to manage energy efficiency, especially in cognitive networks. The data communication protocol is a broadly used approach in a wireless network based IoT. Cognitive Radio (CR) networks are mainly concentrated on battery-powered devices for highly utilizing the data regarding the spectrum and routing allocation, dynamic spectrum access, and spectrum sharing. Data aggregation and clustering are the best solutions for enhancing the energy efficiency of the network. Most researchers have focused on solving the problems related to Cognitive Radio Sensor Networks (CRSNs) in terms of Spectrum allocation, Quality of Service (QoS) optimization, delay reduction, and so on. However, a very small amount of research work has focused on energy restriction problems by using the switching and channel sensing mechanism. As this energy validation is highly challenging due to dependencies on various factors like scheduling priority to the registered users, the data loss rate of unlicensed channels, and the possibilities of accessing licensed channels. Many IoT-based models involve energy-constrained devices and data aggregation along with certain optimization approaches for improving utilization. In this paper, the cognitive radio framework is developed for medical data transmission over the Internet of Medical Things (IoMT) network. The energy-efficient cluster-based data transmission is done through cluster head selection using the hybrid optimization algorithm named Spreading Rate-based Coronavirus Herding-Grey Wolf Optimization (SR-CHGWO). The network lifetime is improved with a cognitive- routing based on IoT framework to enhance the efficiency of the data transmission through the multi-objective function. This multi-objective function is derived using constraints like energy, throughput, data rate, node power, and outage probability delay of the proposed framework. The simulation experiments show that the developed framework enhances the energy efficiency using the proposed algorithm when compared to the conventional techniques.

    Citation: B Naresh Kumar, Jai Sukh Paul Singh. Intelligence-based optimized cognitive radio routing for medical data transmission using IoT[J]. AIMS Electronics and Electrical Engineering, 2022, 6(3): 223-246. doi: 10.3934/electreng.2022014

    Related Papers:

  • The Internet of Things (IoT) is considered an effective wireless communication, where the main challenge is to manage energy efficiency, especially in cognitive networks. The data communication protocol is a broadly used approach in a wireless network based IoT. Cognitive Radio (CR) networks are mainly concentrated on battery-powered devices for highly utilizing the data regarding the spectrum and routing allocation, dynamic spectrum access, and spectrum sharing. Data aggregation and clustering are the best solutions for enhancing the energy efficiency of the network. Most researchers have focused on solving the problems related to Cognitive Radio Sensor Networks (CRSNs) in terms of Spectrum allocation, Quality of Service (QoS) optimization, delay reduction, and so on. However, a very small amount of research work has focused on energy restriction problems by using the switching and channel sensing mechanism. As this energy validation is highly challenging due to dependencies on various factors like scheduling priority to the registered users, the data loss rate of unlicensed channels, and the possibilities of accessing licensed channels. Many IoT-based models involve energy-constrained devices and data aggregation along with certain optimization approaches for improving utilization. In this paper, the cognitive radio framework is developed for medical data transmission over the Internet of Medical Things (IoMT) network. The energy-efficient cluster-based data transmission is done through cluster head selection using the hybrid optimization algorithm named Spreading Rate-based Coronavirus Herding-Grey Wolf Optimization (SR-CHGWO). The network lifetime is improved with a cognitive- routing based on IoT framework to enhance the efficiency of the data transmission through the multi-objective function. This multi-objective function is derived using constraints like energy, throughput, data rate, node power, and outage probability delay of the proposed framework. The simulation experiments show that the developed framework enhances the energy efficiency using the proposed algorithm when compared to the conventional techniques.



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    [1] Yuan B, Lin C, Zhao H, et al. (2020) Secure Data Transportation with Software-Defined Networking and k-n Secret Sharing for High-Confidence IoT Services. IEEE Internet Things 7: 7967–7981. https://doi.org/10.1109/JIOT.2020.2993587 doi: 10.1109/JIOT.2020.2993587
    [2] Awin FA, Alginahi YM, Abdel-Raheem E, et al. (2019) Technical Issues on Cognitive Radio-Based Internet of Things Systems: A Survey. IEEE Access 7: 97887–97908. https://doi.org/10.1109/ACCESS.2019.2929915 doi: 10.1109/ACCESS.2019.2929915
    [3] Fang D, Qian Y, Hu RQ (2020) A Flexible and Efficient Authentication and Secure Data Transmission Scheme for IoT Applications. IEEE Internet Things 7: 3474–3484. https://doi.org/10.1109/JIOT.2020.2970974 doi: 10.1109/JIOT.2020.2970974
    [4] Zhong X, Li L, Zhang Y, et al. (2020) OODT: Obstacle Aware Opportunistic Data Transmission for Cognitive Radio Ad Hoc Networks. IEEE T Commun 68: 3654–3666. https://doi.org/10.1109/TCOMM.2020.2979976 doi: 10.1109/TCOMM.2020.2979976
    [5] Inagaki Y, Shinkuma R, Sato T, et al. (2019) Prioritization of Mobile IoT Data Transmission Based on Data Importance Extracted From Machine Learning Model. IEEE Access 7: 93611–93620. https://doi.org/10.1109/ACCESS.2019.2928216 doi: 10.1109/ACCESS.2019.2928216
    [6] Zhang K, Leng S, Peng X, et al. (2019) Artificial Intelligence Inspired Transmission Scheduling in Cognitive Vehicular Communications and Networks. IEEE Internet Things 6: 1987–1997. https://doi.org/10.1109/JIOT.2018.2872013 doi: 10.1109/JIOT.2018.2872013
    [7] Manman L, Xin Q, Goswami P, et al. (2020) Energy-Efficient Dynamic Clustering for IoT Applications: A Neural Network Approach. 2020 IEEE Eighth International Conference on Communications and Networking (ComNet), 1‒7. https://doi.org/10.1109/ComNet47917.2020.9306092
    [8] Wang X, Zhong X, Li L, et al. (2020) TOT: Trust aware opportunistic transmission in cognitive radio Social Internet of Things. Comput Commun 162: 1–11. https://doi.org/10.1016/j.comcom.2020.08.007 doi: 10.1016/j.comcom.2020.08.007
    [9] Dhiman G, Sharma R (2021) SHANN: an IoT and machine-learning-assisted edge cross-layered routing protocol using spotted hyena optimizer. Complex Intell Syst, 1‒9. https://doi.org/10.1007/s40747-021-00578-5
    [10] Mukherjee A, Jain DK, Yang L (2021) On-Demand Efficient Clustering for Next Generation IoT Applications: A Hybrid NN Approach. IEEE Sens J 21: 25457–25464. https://doi.org/10.1109/JSEN.2020.3026647 doi: 10.1109/JSEN.2020.3026647
    [11] Kuila P, Jana PK (2020) Energy efficient clustering and routing algorithms for wireless sensor networks: Particle swarm optimization approach. Eng Appl Artif Intel 33: 127–140. https://doi.org/10.1016/j.engappai.2014.04.009 doi: 10.1016/j.engappai.2014.04.009
    [12] Mukherjee A, Goswami P, Yan Z, et al. (2020) Distributed gradient descent based cluster head identification in MIMO sensor networks. Optik 204: 164185. https://doi.org/10.1016/j.ijleo.2020.164185 doi: 10.1016/j.ijleo.2020.164185
    [13] Mukherjee A, Jain DK, Goswami P, et al. (2020) Back Propagation Neural Network Based Cluster Head Identification in MIMO Sensor Networks for Intelligent Transportation Systems. IEEE Access 8: 28524–28532. https://doi.org/10.1109/ACCESS.2020.2971969 doi: 10.1109/ACCESS.2020.2971969
    [14] Gopikrishnan S, Priakanth P, Srivastava G (2021) DEDC: Sustainable data communication for cognitive radio sensors in the Internet of Things. Sustainable Computing: Informatics and Systems 29: 100471. https://doi.org/10.1016/j.suscom.2020.100471 doi: 10.1016/j.suscom.2020.100471
    [15] Vimal S, Khari M, Crespo RG, et al. (2020) Energy enhancement using Multiobjective Ant colony optimization with Double Q learning algorithm for IoT based cognitive radio networks. Comput Commun 154: 481–490. https://doi.org/10.1016/j.comcom.2020.03.004 doi: 10.1016/j.comcom.2020.03.004
    [16] Ghose D, Frøytlog A, Li FY (2019) Enabling early sleeping and early data transmission in wake-up radio-enabled IoT networks. Comput Networks 153: 132–144. https://doi.org/10.1016/j.comnet.2019.03.002 doi: 10.1016/j.comnet.2019.03.002
    [17] Anamalamudi S, Sangi AR, Alkatheiri M, et al. (2018) AODV routing protocol for Cognitive radio access based Internet of Things (IoT). Futur Gener Comput Syst 83: 228–238. https://doi.org/10.1016/j.future.2017.12.060 doi: 10.1016/j.future.2017.12.060
    [18] Qureshi FF, Iqbal R, Asghar MN (2017) Energy-efficient wireless communication technique based on Cognitive Radio for Internet of Things. J Netw Comput Appl 89: 14–25. https://doi.org/10.1016/j.jnca.2017.01.003 doi: 10.1016/j.jnca.2017.01.003
    [19] Kumar MA, Vimala R, Britto KRA (2019) A cognitive technology-based healthcare monitoring system and medical data transmission. Meas J Int Meas Confed 146: 322–332. https://doi.org/10.1016/j.measurement.2019.03.017 doi: 10.1016/j.measurement.2019.03.017
    [20] Mukherjee A, Goswami P, Datta A (2016) HML-Based Smart Positioning of Fusion Center for Cooperative Communication in Cognitive Radio Networks. IEEE Commun Lett 20: 2261–2263. https://doi.org/10.1109/LCOMM.2016.2602266 doi: 10.1109/LCOMM.2016.2602266
    [21] Al-Betar MA, Alyasseri ZAA, Awadallah MA, et al. (2021) Coronavirus herd immunity optimizer (CHIO). Neural Comput Appl 33: 5011–5042. https://doi.org/10.1007/s00521-020-05296-6 doi: 10.1007/s00521-020-05296-6
    [22] Mirjalili S, Mirjalili SM, Lewis A (2014) Grey Wolf Optimizer. Adv Eng Softw 69: 46–61. https://doi.org/10.1016/j.advengsoft.2013.12.007 doi: 10.1016/j.advengsoft.2013.12.007
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