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Open Access

Intelligent Computational Model for Energy Efficiency and AI Automation of Network Devices in 5G Communication Environment

Department of Electronics and Communication Engineering, Uttarakhand Technical University, Dehradun 248007, India
Department of Electronics and Communication Engineering, Sri Eshwar College of Engineering, Coimbatore 641202, India
Department of Computer Science and Engineering, CMR Institute of Technology, Karnataka 560037, India
Department of Information Science and Engineering, CMR Institute of Technology, Karnataka 560037, India
Advanced and Innovative Research Laboratory, Dehradun 248001, India
Department of Electronics and Communication Engineering, Graphic Era (Deemed to be University), Dehradun 248007, India
Department of Physics, College of Science and Humanities, Prince Sattam Bin Abdulaziz University, Al-Kharj 16273, Saudi Arabia
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Abstract

Currently, the 4G network service has caused massive digital growth in high use. Video calling has become the go-to Internet application for many people, downloading even huge files in minutes. Everyone is looking for and buying only 4G Subscriber Identity Module (SIM)-capable mobiles. In this case, the expectation of 5G has increased in line with 2G, 3G, and 4G, where the G stands for generation, and it does not indicate Internet or Internet speed. 5G includes next-generation features that are more advanced than those available in 4G network services. The main objective of 5G is uninterrupted telecommunication service in hybrid energy storage system. This paper proposes an intelligent networking model to obtain the maximum energy efficiency and Artificial Intelligence (AI) automation of 5G networks. There is currently an issue where the signal cuts out when crossing an area with one tower and traveling to an area with another tower. The problem of “call drop”, where the call is disconnected while talking, is not present in 5G. The proposed Intelligent Computational Model (ICM) model obtained 96.31% network speed management, 90.63% battery capacity management, 92.27% network device management, 93.57% energy efficiency, and 88.41% AI automation.

Keywords

energy efficiency5Gnetworkhybrid energystorage system4Gdigital growthdownloadingSubscriber Identity Module (SIM)generationArtificial Intelligence (AI) automation

References

[1]
M. Panek, A. Pomykała, I. Jabłoński, and M. Woźniak, 5G/5G+ network management employing AI-based continuous deployment, Appl. Soft Comput., vol. 134, p. 109984, 2023.
Crossref
[2]

B. Gopi and J. Logeshwaran, The fuzzy logical controller based energy storage and conservation model to achieve maximum energy efficiency in modern 5G communication, ICTACT J. Commun. Technol., vol. 13, no. 3, pp. 2774–2779, 2022.
Crossref Google Scholar
[3]

M. A. Matin, S. K. Goudos, S. Wan, P. Sarigiannidis, and E. M. Tentzeris, Artificial intelligence (AI) and machine learning (ML) for beyond 5G/6G communications, EURASIP J. Wirel. Commun. Netw., vol. 2023, no. 1, pp. 1–3, 2023.
Crossref Google Scholar
[4]

M. K. Hasan, A. F. Ismail, S. Islam, W. Hashim, and B. Pandey, Dynamic spectrum allocation scheme for heterogeneous network, Wirel. Pers. Commun., vol. 95, no. 2, pp. 299–315, 2017.
Crossref Google Scholar
[5]

S. Malta, P. Pinto, and M. Fernandez-Veiga, Using reinforcement learning to reduce energy consumption of ultra-dense networks with 5G use cases requirements, IEEE Access, vol. 11, pp. 5417–5428, 2023.
Crossref Google Scholar
[6]

Z. E. Ahmed, M. K. Hasan, R. A. Saeed, R. Hassan, S. Islam, R. A. Mokhtar, S. Khan, and M. Akhtaruzzaman, Optimizing energy consumption for cloud Internet of Things, Front. Phys., vol. 8, p. 358, 2020.
Crossref Google Scholar
[7]
N. Chukhno, A. Orsino, J. Torsner, A. Iera, and G. Araniti, 5G NR sidelink multi-hop transmission in public safety and factory automation scenarios, IEEE Netw., vol. 37, no. 5, pp. 129–136, 2023.
Crossref
[8]
A. Bagwari and B. Singh, Comparative performance evaluation of spectrum sensing techniques for cognitive radio networks, in Proc. 4th Int. Conf. Computational Intelligence and Communication Networks, Mathura, India, 2012, pp. 98–105.
Crossref
[9]
Y. Jiang, L. Li, J. Zhu, Y. Xue, and H. Ma, DEANet: Decomposition enhancement and adjustment network for low-light image enhancement, Tsinghua Science and Technology, vol. 28, no. 4, pp. 743–753, 2023.
Crossref
[10]

M. K. Hasan, T. M. Ghazal, A. Alkhalifah, K. A. Abu Bakar, A. Omidvar, N. S. Nafi, and J. I. Agbinya, Fischer linear discrimination and quadratic discrimination analysis-based data mining technique for Internet of Things framework for healthcare, Front. Public Health, vol. 9, p. 737149, 2021.
Crossref Google Scholar
[11]

N. Li, X. Xu, Q. Sun, J. Wu, Q. Zhang, G. Chi, I. Chih-Lin, and N. Sprecher, Transforming the 5G RAN with innovation: The confluence of cloud native and intelligence, IEEE Access, vol. 11, pp. 4443–4454, 2023.
Crossref Google Scholar
[12]
J. Logeshwaran and R. N. Shanmugasundaram, Enhancements of resource management for device to device (D2D) communication: A review, in Proc. 3rd Int. Conf. I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC), Palladam, India, 2019, pp. 51–55.
Crossref
[13]

H. Alqahtani, L. Niranjan, P. Parthasarathy, and A. Mubarakali, Modified power line system-based energy efficient routing protocol to improve network life time in 5G networks, Comput. Electr. Eng., vol. 106, p. 108564, 2023.
Crossref Google Scholar
[14]

S. Y. Siddiqui, A. Haider, T. M. Ghazal, M. A. Khan, I. Naseer, S. Abbas, M. Rahman, J. Ahmad Khan, M. Ahmad, M. K. Hasan, et al., IoMT cloud-based intelligent prediction of breast cancer stages empowered with deep learning, IEEE Access, vol. 9, pp. 146478–146491, 2021.
Crossref Google Scholar
[15]

Y. Shi, F. Chang, Y. Sun, G. Yang, R. Wang, and Y. Yao, Node search contributions based long-term follow-up specific individual searching model, Tsinghua Science and Technology, vol. 28, no. 4, pp. 729–742, 2023.
Crossref Google Scholar
[16]
S. Raja, J. Logeshwaran, S. Venkatasubramanian, M. Jayalakshmi, N. Rajeswari, N. G. Olaiya, and W. D. Mammo, OCHSA: Designing energy-efficient lifetime-aware leisure degree adaptive routing protocol with optimal cluster head selection for 5G communication network disaster management, Sci. Program., vol. 2022, p. 5424356, 2022.
Crossref
[17]

S. Zhao, Energy efficient resource allocation method for 5G access network based on reinforcement learning algorithm, Sustain. Energy Technol. Assess., vol. 56, p. 103020, 2023.
Crossref Google Scholar
[18]
A. Mehmood, Z. Lv, J. Lloret, and M. M. Umar, ELDC: An artificial neural network based energy-efficient and robust routing scheme for pollution monitoring in WSNs, IEEE Trans. Emerg. Topics Comput., vol. 8, no. 1, pp. 106–114, 2020.
Crossref
[19]
P. Ajmani and P. Saigal, 5G and IoT for smart farming, in Applying Drone Technologies and Robotics for Agricultural Sustainability, P. Raj, K. Saini, and V. Pacheco, eds. Hershey, PA, USA: IGI Global, 2023, pp. 124–139.
Crossref
[20]

S. Amanlou, M. K. Hasan, and K. A. Abu Bakar, Lightweight and secure authentication scheme for IoT network based on publish–subscribe fog computing model, Comput. Netw., vol. 199, p. 108465, 2021.
Crossref Google Scholar
[21]
M. M. Kamruzzaman, M. A. Hossin, and I. Alrashdi, Practical approaches to machine learning for 5G and beyond wireless network, in Handbook of Research on Advanced Practical Approaches to Deep Fake Detection and Applications, A. J. Obaid, G. H. Abdul-Majeed, A. Burlea-Schiopoiu, and P. Aggarwal, eds. Hershey, PA, USA: IGI Global, 2023, pp. 333–344.
Crossref
[22]

Q. Zhang, H. Zhang, K. Zhou, and L. Zhang, Developing a physiological signal-based, mean threshold and decision-level fusion algorithm (PMD) for emotion recognition, Tsinghua Science and Technology, vol. 28, no. 4, pp. 673–685, 2023.
Crossref Google Scholar
[23]
K. Samdanis, A. N. Abbou, J. Song, and T. Taleb, AI/ML service enablers & model maintenance for beyond 5G networks, IEEE Netw., vol. 37, no. 5, pp. 162–172, 2023.
Crossref
[24]

M. K. Hasan, R. A. Saeed, A. H. A. Hashim, S. Islam, R. Alsaqor, and T. A. Alahdal, Femtocell network time synchronization protocols and schemes, Research Journal of Applied Sciences, Engineering and Technology, vol. 4, no. 23, pp. 5136–5143, 2012.
Google Scholar
[25]

M. Tomala and K. Staniec, Modelling of ML-enablers in 5G radio access network-conceptual proposal of computational framework, Electronics, vol. 12, no. 3, p. 481, 2023.
Crossref Google Scholar
[26]

A. Bagwari and G. S. Tomar, Two-stage detectors with multiple energy detectors and adaptive double threshold in cognitive radio networks, Int. J. Distrib. Sens. Netw., vol. 9, no. 8, p. 656495, 2013.
Crossref Google Scholar
[27]

A. Bagwari and G. S. Tomar, Adaptive double-threshold based energy detector for spectrum sensing in cognitive radio networks, Int. J. Electron. Lett., vol. 1, no. 1, pp. 24–32, 2013.
Crossref Google Scholar
[28]
H. Godhrawala and R. Sridaran, Improving architectural reusability for resource allocation framework in futuristic cloud computing using decision tree based multi-objective automated approach, in Proc. Advancements in Smart Computing and Information Security, Rajkot, India, 2022, pp. 397–415.
Crossref
[29]
D. M. Monteiro, J. J. P. C. Rodrigues, and J. Lloret, A secure NFC application for credit transfer among mobile phones, in Proc. Int. Conf. Computer, Information and Telecommunication Systems (CITS), Amman, Jordan, 2012, pp. 1–5.
Crossref
[30]
R. Dangi and P. Lalwani, Harris Hawks optimization based hybrid deep learning model for efficient network slicing in 5G network, Clust. Comput., vol. 27, no. 1, pp. 395–409, 2024.
Crossref
[31]

G. Ramesh, J. Logeshwaran, V. Aravindarajan, and F. Thachil, Eliminate the interference in 5G ultrawide band communication antennas in cloud computing networks, ICTACT Journal on Microelectronics, vol. 8, no. 2, pp. 1338–1344, 2022.
Google Scholar
[32]
S. Kannadhasan, R. Nagarajan, and M. Shanmuganantham, Recent trends in 5G and machine learning, challenges, and opportunities, in Advancing Computational Intelligence Techniques for Security Systems Design, U. Sharma, P. Astya, A. Baliyan, S. Krit, V. Jain, and M. Z. Khan, eds. Boca Raton, FL, USA: CRC Press, 2022, pp. 73–86.
Crossref
[33]

N. A. Alrajeh, S. Khan, J. Lloret, and J. Loo, Secure routing protocol using cross-layer design and energy harvesting in wireless sensor networks, Int. J. Distrib. Sens. Netw., vol. 9, no. 1, p. 374796, 2013.
Crossref Google Scholar
[34]
A. El Amraoui, Metaheuristic moth flame optimization based energy efficient clustering protocol for 6G enabled unmanned aerial vehicle networks, in AI Enabled 6G Networks and Applications, D. Gupta, M. Ragab, R. F. Mansour, A. Khamparia, and A. Khanna, eds. New York, NY, USA: John Wiley & Sons, 2022, pp. 1–15.
Crossref
[35]
M. K. Hasan, A. F. Ismail, A. H. Abdalla, K. Abdullah, H. Ramli, S. Islam, and R. A. Saeed, Inter-cell interference coordination in LTE-a HetNets: A survey on self organizing approaches, in Proc. Int. Conf. Computing, Electrical and Electronic Engineering (ICCEEE), Khartoum, Sudan, 2013, pp. 196–201.
Crossref
[36]
N. Mangra, F. Behmann, A. Thakur, A. Popescu, G. Suciu Jr, G. Giannattasio, and W. Montlouis, White Paper-5G Enabled Agriculture Ecosystem : Food Supply Chain, Rural Development, and Climate Resiliency. Piscataway, NJ, USA: IEEE, 2023.
[37]

A. Bagwari and G. S. Tomar, Cooperative spectrum sensing with adaptive double-threshold based energy detector in cognitive radio networks, Wirel. Pers. Commun., vol. 73, no. 3, pp. 1005–1019, 2013.
Crossref Google Scholar
[38]

T. Mazhar, H. M. Irfan, I. Haq, I. Ullah, M. Ashraf, T. Al Shloul, Y. Y. Ghadi, Imran, and D. H. Elkamchouchi, Analysis of challenges and solutions of IoT in smart grids using AI and machine learning techniques: A review, Electronics, vol. 12, no. 1, p. 242, 2023.
Crossref Google Scholar
[39]

J. Logeshwaran and S. Karthick, A smart design of a multi-dimensional antenna to enhance the maximum signal clutch to the allowable standards in 5G communication networks, ICTACT Journal on Microelectronics, vol. 8, no. 1, pp. 1269–1274, 2022.
Google Scholar
[40]
Y. L. Lee, A. G. H. Sim, L. C. Wang, and T. C. Chuah, Blockchain and artificial intelligence-based radio access network slicing for 6G networks, in Blockchain for 6G-Enabled Network-Based Applications, V. Rishiwal, S. Tanwar, and R. Chaudhry, eds. Boca Raton, FL, USA: CRC Press, 2022, pp. 77–105.
Crossref
[41]

M. K. Hasan, M. Akhtaruzzaman, S. R. Kabir, T. R. Gadekallu, S. Islam, P. Magalingam, R. Hassan, M. Alazab, and M. A. Alazab, Evolution of industry and blockchain era: Monitoring price hike and corruption using BIoT for smart government and industry 4.0, IEEE Trans. Ind. Inf., vol. 18, no. 12, pp. 9153–9161, 2022.
Crossref Google Scholar
[42]

A. S. Alqahtani, S. B. Changalasetty, P. Parthasarathy, L. S. Thota, and A. Mubarakali, Effective spectrum sensing using cognitive radios in 5G and wireless body area networks, Comput. Electr. Eng., vol. 105, p. 108493, 2023.
Crossref Google Scholar
[43]

A. Bagwari, G. S. Tomar, and S. Verma, Cooperative spectrum sensing based on two-stage detectors with multiple energy detectors and adaptive double threshold in cognitive radio networks, Can. J. Electr. Comput. Eng., vol. 36, no. 4, pp. 172–180, 2013.
Crossref Google Scholar
[44]

L. Tomaszewski and R. Kołakowski, Mobile services for smart agriculture and forestry, biodiversity monitoring, and water management: Challenges for 5G/6G networks, Telecom, vol. 4, no. 1, pp. 67–99, 2023.
Crossref Google Scholar
[45]

A. Paul and S. Rho, Probabilistic model for M2M in IoT networking and communication, Telecommun. Syst., vol. 62, no. 1, pp. 59–66, 2016.
Crossref Google Scholar
[46]

I. P. Chochliouros, M. A. Kourtis, A. S. Spiliopoulou, P. Lazaridis, Z. Zaharis, C. Zarakovitis, and A. Kourtis, Energy efficiency concerns and trends in future 5G network infrastructures, Energies, vol. 14, no. 17, p. 5392, 2021.
Crossref Google Scholar
[47]
S. Din, A. Ahmad, A. Paul, and S. Rho, MGR: Multi-parameter Green Reliable communication for Internet of Things in 5G network, J. Parallel Distrib. Comput., vol. 118, pp. 34–45, 2018.
Crossref
[48]
G. Carrozzo, M. S. Siddiqui, A. Betzler, J. Bonnet, G. M. Perez, A. Ramos, and T. Subramanya, AI-driven zero-touch operations, security and trust in multi-operator 5G networks: A conceptual architecture, in Proc. European Conf. Networks and Communications (EuCNC), Dubrovnik, Croatia, 2020, pp. 254–258.
Crossref
[49]
D. Jiang, Z. Wang, W. Wang, Z. Lv, and K. K. R. Choo, AI-assisted energy-efficient and intelligent routing for reconfigurable wireless networks, IEEE Trans. Netw. Sci. Eng., vol. 9, no. 1, pp. 78–88, 2022.
Crossref
Tsinghua Science and Technology
Volume 29 Issue 6,
December 2024
Pages 1728-1751
DOI: 10.26599/TST.2024.9010005
Cite this article:
Bagwari A, Logeshwaran J, Raja M, et al. Intelligent Computational Model for Energy Efficiency and AI Automation of Network Devices in 5G Communication Environment. Tsinghua Science and Technology, 2024, 29(6): 1728-1751. https://doi.org/10.26599/TST.2024.9010005

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Received: 13 February 2023
Revised: 11 November 2023
Accepted: 26 December 2023
Published: 20 June 2024
© The Author(s) 2024.

The articles published in this open access journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).
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