2690-6791ISSN (Online)
2769-786XISSN (Print)
Volume 18 , Issue 2 , PP: 01-19, 2026 | Cite this article as | XML | Html | PDF | Full Length Article
Saleh Ali Alomari 1 *
Doi: https://doi.org/10.54216/JISIoT.180201
In an increasingly fast-paced world of 6G-IoT networks, optimal beamforming techniques will be effective in improving strength, latency, and quality of service delivery in the networks. This work presents a new paradigm in beamforming optimization, especially in tackling dynamic environments and high computational costs in existing approaches. The problems of long training times with traditional methods, along with threats in security make them out rightly less applicable for real time applications. The data is collected from 6G IoT networks then, Laplacian Eigenmaps is used for feature extraction and modelling in time and applied for dimensionality reduction, ConvMarkov is used for model development RC4 encryption secures data exchange, while blockchain supports data logging and promotes transparency. This is a combination of deep learning techniques and advanced encryption methods, which will lead to a wide boost in beamforming efficiency, flexibility, and security. This study achieved the beamforming optimization achieved 97% accuracy with significant gain improvements, as indicated by an ROC curve (AUC = 0.9970) and precision-recall curve. The training loss stabilized below 0.01, while the validation loss fluctuated above 0.1, suggesting minor overfitting. The main achievements converge on proving improvements in optimization under real time conditions in a network, besides integrity and privacy of data. These become great merits into a strong solution for future 6G.
6G-IoT , Privacy-Preserving AI , Beamforming Optimization , ConvMarkov Model , Blockchain Integration
[1] M. W. Akhtar et al., "The shift to 6G communications: vision and requirements," Human-centric Computing and Information Sciences, vol. 10, pp. 1-27, 2020.
[2] M. Asif et al., "Energy-efficient beamforming and resource optimization for AmBSC-assisted cooperative NOMA IoT networks," IEEE Internet of Things Journal, vol. 10, no. 14, pp. 12434-12448, 2023.
[3] L. Rao et al., "5G beamforming techniques for the coverage of intended directions in modern wireless communication: in-depth review," International Journal of Microwave and Wireless Technologies, vol. 13, no. 10, pp. 1039-1062, 2021.
[4] L. Jiao et al., "Advanced deep learning models for 6G: overview, opportunities and challenges," IEEE Access, vol. 12, pp. 51070-51109, 2024.
[5] S. Goumiri, D. Benboudjema, and W. Pieczynski, "A new hybrid model of convolutional neural networks and hidden Markov chains for image classification," Neural Computing and Applications, vol. 35, no. 24, pp. 17987-18002, 2023.
[6] J. N. Chukwunweike, A. A. Adewale, and O. Osamuyi, "Advanced modelling and recurrent analysis in network security: Scrutiny of data and fault resolution," World Journal of Advanced Research and Reviews, vol. 23, no. 2, pp. 2373-2390, 2024.
[7] N. Y. Al-Matari, A. T. Zahary, and A. A. Al-Shargabi, "A survey on advancements in blockchain-enabled spectrum access security for 6G cognitive radio IoT networks," Scientific Reports, vol. 14, no. 1, p. 30990, 2024.
[8] Y. M. Al-Hatim and A. O. Al Janaby, "Artificial-intelligence-enhanced beamforming for power-efficient user targeting in 5G networks using reinforcement learning," International Journal of Computing and Digital Systems, vol. 16, no. 1, pp. 1083-1095, 2024.
[9] B. Haouari, R. Mzid, and O. Mosbahi, "A reinforcement learning-based approach for online optimal control of self-adaptive real-time systems," Neural Computing and Applications, vol. 35, no. 27, pp. 20375-20401, 2023.
[10] I. Mallioras et al., "A novel realistic approach of adaptive beamforming based on deep neural networks," IEEE Trans. Antennas Propag., vol. 70, no. 10, pp. 8833-8848, Oct. 2022.
[11] D. D. S. Brilhante et al., "A literature survey on AI-aided beamforming and beam management for 5G and 6G systems," Sensors, vol. 23, no. 9, p. 4359, 2023.
[12] A. M. Elbir et al., "Twenty-five years of advances in beamforming: From convex and nonconvex optimization to learning techniques," IEEE Signal Process. Mag., vol. 40, no. 4, pp. 118-131, Jul. 2023.
[13] W. Li et al., "Blockchain-based data security for artificial intelligence applications in 6G networks," IEEE Netw., vol. 34, no. 6, pp. 31-37, Nov./Dec. 2020.
[14] D. C. Nguyen et al., "6G Internet of Things: A comprehensive survey," IEEE Internet Things J., vol. 9, no. 1, pp. 359-383, Jan. 2021.
[15] B. Mao, J. Liu, Y. Wu, and N. Kato, "Security and privacy on 6G network edge: A survey," IEEE Commun. Surv. Tutor, vol. 25, no. 2, pp. 1095-1127, 2nd Quart. 2023.
[16] A. Jahid, M. H. Alsharif, and T. J. Hall, "The convergence of blockchain, IoT and 6G: Potential, opportunities, challenges and research roadmap," J. Netw. Comput. Appl., vol. 217, p. 103677, 2023.
[17] H. F. Atlam, M. A. Azad, M. Altamimi, and N. Fadhel, "Role of Blockchain and AI in Security and Privacy of 6G," in AI and Blockchain Technology in 6G Wireless Network. Singapore: Springer, 2022, pp. 93-115.
[18] T. Nguyen et al., "Privacy-aware blockchain innovation for 6G: Challenges and opportunities," in Proc. 2020 2nd 6G Wireless Summit (6G SUMMIT), 2020, pp. 1-5.
[19] A. A. Zainuddin, N. F. Omar, N. N. Zakaria, and N. A. M. Camara, "Privacy-preserving techniques for IoT data in 6G networks with Blockchain Integration: a review," Int. J. Perceptive Cogn. Comput, vol. 9, no. 2, pp. 80-92, 2023.
[20] H. El Ghor and B. Nakhal, "A Blockchain-Enabled Approach for Secure Data Sharing in 6G-based Internet of Things Networks," in Wireless Networks: Cyber Security Threats and Countermeasures. Cham: Springer, 2023, pp. 227-246.
[21] Y. Siriwardhana, P. Porambage, M. Liyanage, and M. Ylianttila, "AI and 6G security: Opportunities and challenges," in *Proc. 2021 Joint Eur. Conf. Netw. Commun. 6G Summit (EuCNC/6G Summit)*, 2021, pp. 616-621.
[22] R. Gupta et al., "Blockchain and AI-based secure onion routing framework for data dissemination in IoT environment underlying 6G networks," in Proc. 2022 Sixth Int. Conf. Smart Cities, Internet Things Appl. (SCIoT), 2022, pp. 1-6.
[23] H. Liu, Y. Wang, and F. Jia, "Security and Privacy Protection of Internet of Things Devices in 6G Networks," Int. J. Syst. Assur. Eng. Manag., pp. 1-18, 2024.
[24] V. L. Nguyen et al., "Security and privacy for 6G: A survey on prospective technologies and challenges," IEEE Commun. Surv. Tutor, vol. 23, no. 4, pp. 2384-2428, 4th Quart. 2021.
[25] P. Kumar et al., "Distributed AI and Blockchain for 6G-assisted terrestrial and non-terrestrial networks: Challenges and future directions," IEEE Netw., vol. 37, no. 2, pp. 70-77, Mar./Apr. 2023.
[26] P. Porambage, G. Gür, D. P. M. Osorio, M. Livanage, and M. Ylianttila, "6G security challenges and potential solutions," in *Proc. 2021 Joint Eur. Conf. Netw. Commun. 6G Summit (EuCNC/6G Summit)*, 2021, pp. 622-627.
[27] M. Fozi, A. R. Sharafat, and M. Bennis, "Fast MIMO beamforming via deep reinforcement learning for high mobility mmWave connectivity," IEEE J. Sel. Areas Commun., vol. 40, no. 1, pp. 127-142, Jan. 2022.
[28] Y. Han et al., "Dynamic neural networks: A survey," IEEE Trans. Pattern Anal. Mach. Intell., vol. 44, no. 11, pp. 7436-7456, Nov. 2022.
[29] S. K. Joshi and P. M. Shukla, "Reinforcement Learning for Dynamic Resource Allocation in 6G Cognitive Radio Networks," IEEE Trans. Cogn. Commun. Netw., vol. 8, no. 2, pp. 742-756, Jun. 2022.
[30] S. A. Alomari, P. Sumari, and A. Taghizadeh, "A Comprehensive Study of Wireless Communication Technology for the Future Mobile Devices," Eur. J. Sci. Res., vol. 60, no. 4, pp. 583-591, 2011.
[31] D. Kaul and R. Khurana, "AI to detect and mitigate security vulnerabilities in APIs: encryption, authentication, and anomaly detection in enterprise-level distributed systems," Eigenpub Rev. Sci. Technol., vol. 5, no. 1, pp. 34-62, 2021.
[32] E. Focante, N. J. Myers, G. Joseph, and A. Pandharipande, "Adaptive beamforming for situation-aware automotive radars under uncertain side information," IEEE Trans. Radar Syst., early access, 2024.
[33] S. Alomari and P. Sumar, "A video on demand system architecture for heterogeneous mobile ad hoc networks for different devices," in Proc. 2nd Int. Conf. Comput. Eng. Technol., vol. 7, 2010, pp. V7-700–V7-707.
[34] P. Deepanramkumar and N. Jaisankar, "BlockCRN-IoCV: secure Spectrum Access and Beamforming for defense against attacks in mmWave massive MIMO CRN in 6G internet of Connected vehicles," IEEE Access, vol. 10, pp. 74220-74243, 2022.
[35] L. Chettri and R. Bera, "A Comprehensive Survey on Internet of Things (IoT) Toward 5G Wireless Systems," IEEE Internet Things J., vol. 7, no. 1, pp. 16-32, Jan. 2020.
[36] Z. Zhou, X. Chen, E. Li, L. Zeng, K. Luo, and J. Zhang, "Edge Intelligence: Paving the Last Mile of Artificial Intelligence With Edge Computing," Proc. IEEE, vol. 107, no. 8, pp. 1738-1762, Aug. 2019.
