Abstract
Wireless cellular networks can be used to establish machine-to-machine (M2M) connections, enabling the use of the Internet of Things (IoT). Access class barring (ACB) is one of the most efficient strategies for directly regulating M2M traffic. However, traditional ACB techniques use a set factor to manage the traffic flow of machine-type communication devices (MTCD). This research proposes a unique intelligent traffic management system that uses Bayesian inference-based learning automatons (BI-LA) to estimate M2M traffic by dynamically changing the ACB factor. The BI-LA-based ACB system uses the self-adaptive learning property of learning automata (LA) to adjust the ACB factor for determining M2M traffic. To adjust the ABC factor, the problem can be phrased based on the collision probability, and it is addressed by integrating Bayesian inference (BI) into LA to compute the environment’s response to each action. The proposed method is simulated with the network simulator-3 (NS3), and performance measures such as average access delay, average number of access attempts, access success rate, and access success are evaluated. The experiment results reveal that the BI-LA ACB technique achieves nearly theoretically ideal performance when compared to conventional LSTM-based ACB schemes, dynamic ACB approaches, and reinforcement learning (RL)-based ABC approaches. The minimum access delay attained by the proposed BI-LA ACB system is approximately 1876 ms and 27.6 ms, respectively.
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References
Wang, X., Sun, X., Ahmad, M., & Chen, J. (2024). Energy transition, ecological governance, globalization, and environmental sustainability: Insights from the top ten emitting countries. Energy, 292, 130551.
Zhao, N., Zhang, H., Yang, X., Yan, J., & You, F. (2023). Emerging information and communication technologies for smart energy systems and renewable transition. Advances in Applied Energy, 9, 100125.
Nassereddine, M., & Khang, A. (2024). Applications of Internet of Things (IoT) in smart cities. In C. R. C. Press (Ed.), Advanced IoT technologies and applications in the industry 4.0 digital economy (pp. 109–136). Delhi.
Anser, M. K., Ahmad, M., Khan, M. A., Zaman, K., Nassani, A. A., Askar, S. E., Abro, M. M., & Kabbani, A. (2021). The role of information and communication technologies in mitigating carbon emissions: Evidence from panel quantile regression. Environmental Science and Pollution Research, 28, 21065–21084.
Ghiasi, M., Wang, Z., Mehrandezh, M., Jalilian, S., & Ghadimi, N. (2023). Evolution of smart grids towards the Internet of energy: Concept and essential components for deep decarbonisation. IET Smart Grid, 6(1), 86–102.
Riker, A., Mota, R., Rośario, D., Pereira, V., & Curado, M. (2022). Autonomic management of group communication for internet of things applications. International Journal of Communication Systems, 35(11), e5200.
Abubakar, A. I., Mollel, M. S., Ozturk, M., Hussain, S., & Imran, M. A. (2022). A lightweight cell switching and traffic offloading scheme for energy optimization in ultra-dense heterogeneous networks. Physical Communication, 52, 101643.
Mohajer, A., Sorouri, F., Mirzaei, A., Ziaeddini, A., Rad, K. J., & Bavaghar, M. (2022). Energy-aware hierarchical resource management and backhaul traffic optimization in heterogeneous cellular networks. IEEE Systems Journal, 16(4), 5188–5199.
Stevens, B. W. (2022). Interweave Cognitive Radio for 4G Long Term Evolution and 5G New Radio Self-Reliant Networks (Doctoral dissertation, University of Maryland, Baltimore County).
Larsen, L. M., Christiansen, H. L., Ruepp, S., & Berger, M. S. (2023). Toward greener 5G and beyond radio access networks—A survey. IEEE Open Journal of the Communications Society, 4, 768–797.
Ruiz, D., San Miguel, G., Rojo, J., Teriús-Padrón, J. G., Gaeta, E., Arredondo, M. T., Hernández, J. F., & Pérez, J. (2022). Life cycle inventory and carbon footprint assessment of wireless ICT networks for six demographic areas. Resources, Conservation and Recycling, 176, 105951.
Darzanos, G., Kalogiros, C., Stamoulis, G. D., Hallingby, H. K., & Frias, Z. (2022, March). Business Models for 5G Experimentation as a Service: 5G Testbeds and Beyond. In 2022 25th Conference on Innovation in Clouds, Internet and Networks (ICIN) (pp. 169–174). IEEE.
Kansal, L., Berra, S., Mounir, M., Miglani, R., Dinis, R., & Rabie, K. (2022). Performance analysis of massive MIMO-OFDM system incorporated with various transforms for image communication in 5G systems. Electronics, 11(4), 621.
Al-Zubi, R. T., Darabkh, K. A., Khattabi, Y. M., & Abu Issa, M. T. (2022). Markov-based analysis for cooperative HARQ-aided NOMA transmission scheme in 5G and beyond. Transactions on Emerging Telecommunications Technologies, 33(5), e4444.
Miuccio, L., Panno, D., & Riolo, S. (2022). An energy-efficient DL-aided massive multiple access scheme for IoT scenarios in beyond 5G networks. IEEE Internet of Things Journal, 10(9), 7936–7959.
Pradhan, D., & Tun, H. M. (2022). Security challenges: M2M communication in IoT. Journal of Electrical Engineering and Automation, 4(3), 187–199.
Aragão, D., Rodrigues, C., Vieira, D., & de Castro, M. F. (2023). A random access channel resources allocation approach to control machine-to-machine communication congestion over LTE-advanced networks. International Journal of Communication Systems. https://doi.org/10.1002/dac.5493
3GPP,” Study on 5G enhanced Mobile Broadband Media Distribution,” 3GPP TR 26.891 V1.1.0, May. 2018.
Tavana, M., Rahmati, A., & Shah-Mansouri, V. (2018). Congestion control with adaptive access class barring for LTE M2M overload using Kalman filters. Computer Networks, 141, 222–233.
Althumali, H., Othman, M., Noordin, N. K., & Hanapi, Z. M. (2022). Priority-based load-adaptive preamble separation random access for QoS-differentiated services in 5G networks. Journal of Network and Computer Applications, 203, 103396.
He, Y., Ren, G., & Liang, S. (2020). Spatial group based access class barring for massive access in M2M communications. IEEE Communications Letters, 25(3), 812–816.
Shinkafi, N. A., Bello, L. M., Shu’aibu, D. S., & Mitchell, P. D. (2021). Priority-based learning automata in Q-learning random access scheme for cellular M2M communications. ETRI Journal, 43(5), 787–798.
Shukry, S., & Fahmy, Y. (2021). Traffic load access barring scheme for random-access channel in massive machine-to-machine and human-to-human devices coexistence in LTE-A. International Journal of Communication Systems, 34(8), e4777.
Lee, C. H., Kao, S. J., & Chang, F. M. (2020). LSTM-based ACB scheme for machine type communications in LTE-A networks. Computer Communications, 152, 296–304.
Sim, Y., & Cho, D. H. (2020). Performance analysis of priority-based access class barring scheme for massive MTC random access. IEEE Systems Journal, 14(4), 5245–5252.
Santos, H.L., Souza, J., Marinello, J.C., & Abrão, T. (2023). LSTM-ACB-Based Random Access for Mixed Traffic IoT Networks. arXiv preprint arXiv:2303.01511.
Orim, P., Ventura, N., & Mwangama, J. (2023). Random access scheme for machine type communication networks using reinforcement learning approach. In 2023 IEEE AFRICON, 1–6.
Bui, A.-T.H., & Pham, A. T. (2020). Deep reinforcement learning-based access class barring for energy-efficient mMTC random access in LTE networks. IEEE Access, 8, 227657–227666.
Li, S., Yang, L., & Fan, P. (2022). Dynamic ACB scheme based on neural networks and Markov chain. In 2022 10th International Workshop on Signal Design and Its Applications in Communications (IWSDA), IEEE, 1–5.
Jang, H. S., Jin, H., Jung, B. C., & Quek, T. Q. S. (2021). Resource-optimized recursive access class barring for bursty traffic in cellular IoT networks. IEEE Internet of Things Journal, 8(14), 11640–11654.
Tello-Oquendo, L., Vidal, J.-R., Pla, V., & Guijarro, L. (2018). Dynamic access class barring parameter tuning in LTE-A networks with massive M2M traffic. In 2018 17th Annual Mediterranean Ad Hoc Networking Workshop (Med-Hoc-Net), IEEE, 1–8.
Tello-Oquendo, L., Pacheco-Paramo, D., Pla, V., & Martinez-Bauset, J. (2018). Reinforcement learning-based ACB in LTE-A networks for handling massive M2M and H2H communications. In 2018 IEEE international conference on communications (ICC), 1–7.
Abera, W., Olwal, T., Marye, Y., & Abebe, A. (2021). Learning based access class barring for massive machine type communication random access congestion control in LTE-A networks. In 2021 International Conference on Electrical, Computer and Energy Technologies (ICECET), IEEE, 1–7.
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Varsha, V., Prakash, S.P.S. & Krinkin, K. An Intelligent Bayesian Inference Based Learning Automaton Approach for Traffic Management in Radio Access Network. Wireless Pers Commun 135, 233–260 (2024). https://doi.org/10.1007/s11277-024-10943-5
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DOI: https://doi.org/10.1007/s11277-024-10943-5