5G-Enabled Internet of Things: Latency Optimization through AI-Assisted Network Slicing
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The confluence of Fifth-Generation (5G) wireless technology and the Internet of Things (IoT) heralds a new era of hyper-connectivity, enabling transformative applications from autonomous vehicles and industrial automation to extended reality and remote surgery. A critical performance indicator for many of these applications is ultra-reliable low-latency communication (URLLC), where delays must be bounded within milliseconds. However, the heterogeneous and dynamic nature of IoT traffic presents a monumental challenge to consistently meeting these stringent latency requirements. Traditional network management paradigms, which are largely static and reactive, are ill-suited for this task.This paper posits that the synergy of two cornerstone 5G technologies—Network Slicing and Artificial Intelligence (AI)—provides the foundational architecture and intelligent control mechanism necessary to achieve dynamic latency optimization at scale. Network Slicing allows for the creation of multiple logical, end-to-end virtual networks on a shared physical infrastructure, each tailored to specific service requirements. Meanwhile, AI and Machine Learning (ML) offer the predictive and adaptive capabilities to manage these slices proactively.This comprehensive review and analytical paper delves into the architecture of 5G-standalone (SA) systems to elucidate the enablers of low latency. It then provides a detailed exposition of network slicing as a resource isolation mechanism. The core of the paper is a thorough investigation into how various AI/ML paradigms—including supervised learning, reinforcement learning, and deep learning—can be integrated into the network control loop to predict traffic surges, dynamically allocate resources, and proactively reconfigure slices. We present a conceptual framework for an AI-assisted Network Slicing orchestration system, detailing its functional components and operational workflow. Furthermore, we analyze the significant challenges impeding widespread deployment, such as data collection, model training, security, and standardization. Through this analysis, we demonstrate that AI-assisted network slicing is not merely an enhancement but a critical imperative for realizing the full potential of latency-critical 5G-IoT ecosystems.
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X. Liu, R. H. Deng, Y. Miao, and A. V. Vasilakos, “Guest Editorial: 5G-Enabled Intelligent Application for Distributed Industrial Internet-of-Thing System,” 2022. doi: 10.1109/TII.2021.3123971. DOI: https://doi.org/10.1109/TII.2021.3123971
S. Kaushik, “Blockchain and 5G-enabled internet of things: Background and preliminaries,” in Blockchain for 5G-Enabled IoT: The new wave for Industrial Automation, 2021. doi: 10.1007/978-3-030-67490-8_1. DOI: https://doi.org/10.1007/978-3-030-67490-8_1
X. Cheng, Q. Luo, Y. Pan, Z. Li, J. Zhang, and B. Chen, “Predicting the APT for Cyber Situation Comprehension in 5G-Enabled IoT Scenarios Based on Differentially Private Federated Learning,” Security and Communication Networks, vol. 2021, 2021, doi: 10.1155/2021/8814068. DOI: https://doi.org/10.1155/2021/8814068
Z. Ning et al., “Mobile Edge Computing Enabled 5G Health Monitoring for Internet of Medical Things: A Decentralized Game Theoretic Approach,” IEEE Journal on Selected Areas in Communications, vol. 39, no. 2, 2021, doi: 10.1109/JSAC.2020.3020645. DOI: https://doi.org/10.1109/JSAC.2020.3020645
V. Chandra Shekhar Rao, P. Kumarswamy, M. S. B. Phridviraj, S. Venkatramulu, and V. Subba Rao, “5G Enabled Industrial Internet of Things (IIoT) Architecture for Smart Manufacturing,” in Lecture Notes on Data Engineering and Communications Technologies, vol. 63, 2021. doi: 10.1007/978-981-16-0081-4_20. DOI: https://doi.org/10.1007/978-981-16-0081-4_20
X. Luo, Z. Yu, Z. Zhao, W. Zhao, and J. H. Wang, “Effective short text classification via the fusion of hybrid features for IoT social data,” Digital Communications and Networks, vol. 8, no. 6, 2022, doi: 10.1016/j.dcan.2022.09.015. DOI: https://doi.org/10.1016/j.dcan.2022.09.015
S. Mukherjee, S. Gupta, O. Rawlley, and S. Jain, “Leveraging big data analytics in 5G-enabled IoT and industrial IoT for the development of sustainable smart cities,” Transactions on Emerging Telecommunications Technologies, vol. 33, no. 12, 2022, doi: 10.1002/ett.4618. DOI: https://doi.org/10.1002/ett.4618
S. A. Gbadamosi, G. P. Hancke, and A. M. Abu-Mahfouz, “Interference Avoidance Resource Allocation for D2D-Enabled 5G Narrowband Internet of Things,” IEEE Internet Things J, vol. 9, no. 22, 2022, doi: 10.1109/JIOT.2022.3184959. DOI: https://doi.org/10.1109/JIOT.2022.3184959
S. H. A. Shah, D. Koundal, V. Sai, and S. Rani, “Guest Editorial: Special Section on 5G Edge Computing-Enabled Internet of Medical Things,” IEEE Trans Industr Inform, vol. 18, no. 12, 2022, doi: 10.1109/TII.2022.3193708. DOI: https://doi.org/10.1109/TII.2022.3193708
X. Wang et al., “QoS and Privacy-Aware Routing for 5G-Enabled Industrial Internet of Things: A Federated Reinforcement Learning Approach,” IEEE Trans Industr Inform, vol. 18, no. 6, 2022, doi: 10.1109/TII.2021.3124848. DOI: https://doi.org/10.1109/TII.2021.3124848
B. Wu, Y. Pi, and J. Chen, “Privacy Protection of Medical Service Data Based on Blockchain and Artificial Intelligence in the Era of Smart Medical Care,” Wirel Commun Mob Comput, vol. 2022, 2022, doi: 10.1155/2022/5295801. DOI: https://doi.org/10.1155/2022/5295801
K. N. Qureshi, O. Kaiwartya, G. Jeon, and F. Piccialli, “Neurocomputing for internet of things: Object recognition and detection strategy,” Neurocomputing, vol. 485, 2022, doi: 10.1016/j.neucom.2021.04.140. DOI: https://doi.org/10.1016/j.neucom.2021.04.140
V. O. Nyangaresi, M. Ahmad, A. Alkhayyat, and W. Feng, “Artificial neural network and symmetric key cryptography based verification protocol for 5G enabled Internet of Things,” Expert Syst, vol. 39, no. 10, 2022, doi: 10.1111/exsy.13126. DOI: https://doi.org/10.1111/exsy.13126
A. Rana, A. Taneja, and N. Saluja, “Beyond 5G Enabled Internet-Of-Things for Next Generation Smart Systems: A Use Case Scenario,” in AIP Conference Proceedings, 2022. doi: 10.1063/5.0095407. DOI: https://doi.org/10.1063/5.0095407
A. S. Rajawat et al., “Securing 5G-IoT Device Connectivity and Coverage Using Boltzmann Machine Keys Generation,” Math Probl Eng, vol. 2021, 2021, doi: 10.1155/2021/2330049. DOI: https://doi.org/10.1155/2021/2330049
M. Kumhar and J. Bhatia, “Emerging communication technologies for 5G-enabled internet of things applications,” in Blockchain for 5G-Enabled IoT: The new wave for Industrial Automation, 2021. doi: 10.1007/978-3-030-67490-8_6. DOI: https://doi.org/10.1007/978-3-030-67490-8_6
Z. Zhang, F. Wen, Z. Sun, X. Guo, T. He, and C. Lee, “Artificial Intelligence‐Enabled Sensing Technologies in the 5G/Internet of Things Era: From Virtual Reality/Augmented Reality to the Digital Twin,” Advanced Intelligent Systems, vol. 4, no. 7, 2022, doi: 10.1002/aisy.202100228. DOI: https://doi.org/10.1002/aisy.202100228
M. A. Al Sibahee, V. O. Nyangaresi, J. Ma, and Z. A. Abduljabbar, “Stochastic Security Ephemeral Generation Protocol for 5G Enabled Internet of Things,” in Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST, 2022. doi: 10.1007/978-3-030-95987-6_1. DOI: https://doi.org/10.1007/978-3-030-95987-6_1
S. P. Chen, J. Wu, and X. Y. Liu, “EMORL: Effective multi-objective reinforcement learning method for hyperparameter optimization,” Eng Appl Artif Intell, vol. 104, 2021, doi: 10.1016/j.engappai.2021.104315. DOI: https://doi.org/10.1016/j.engappai.2021.104315
S. Lai, R. Zhao, S. Tang, J. Xia, F. Zhou, and L. Fan, “Intelligent secure mobile edge computing for beyond 5G wireless networks,” Physical Communication, vol. 45, 2021, doi: 10.1016/j.phycom.2021.101283. DOI: https://doi.org/10.1016/j.phycom.2021.101283
B. M. Robaglia, A. Destounis, M. Coupechoux, and D. Tsilimantos, “Deep Reinforcement Learning for Scheduling Uplink IoT Traffic with Strict Deadlines,” in Proceedings - IEEE Global Communications Conference, GLOBECOM, 2021. doi: 10.1109/GLOBECOM46510.2021.9685561. DOI: https://doi.org/10.1109/GLOBECOM46510.2021.9685561
S. M. Almufti, R. B. Marqas, Z. A. Nayef, and T. S. Mohamed, “Real Time Face-mask Detection with Arduino to Prevent COVID-19 Spreading,” Qubahan Academic Journal, vol. 1, no. 2, pp. 39–46, Apr. 2021, doi: 10.48161/qaj.v1n2a47. DOI: https://doi.org/10.48161/qaj.v1n2a47
R. Rajab Asaad, R. Ismael Ali, A. Ahmad Shaban, and M. Shamal Salih, “Object Detection using the ImageAI Library in Python,” Polaris Global Journal of Scholarly Research and Trends, vol. 2, no. 2, pp. 1–9, Apr. 2023, doi: 10.58429/pgjsrt.v2n2a143. DOI: https://doi.org/10.58429/pgjsrt.v2n2a143
R. Rajab Asaad, R. Ismael Ali, Z. Arif Ali, and A. Ahmad Shaaban, “Image Processing with Python Libraries,” Academic Journal of Nawroz University, vol. 12, no. 2, pp. 410–416, Jun. 2023, doi: 10.25007/ajnu.v12n2a1754. DOI: https://doi.org/10.25007/ajnu.v12n2a1754
H. Chen, Y. Yang, and S. Xie, “Topic Search Algorithm for Network Multimedia Tennis Teaching Resources Using 5G-Enabled Internet of Things Technology,” Wirel Commun Mob Comput, vol. 2022, 2022, doi: 10.1155/2022/1155522. DOI: https://doi.org/10.1155/2022/1155522
R. W. L. Coutinho and A. Boukerche, “Transfer Learning for Disruptive 5G-Enabled Industrial Internet of Things,” IEEE Trans Industr Inform, vol. 18, no. 6, 2022, doi: 10.1109/TII.2021.3107781. DOI: https://doi.org/10.1109/TII.2021.3107781
I. Mistry, S. Tanwar, S. Tyagi, and N. Kumar, “Blockchain for 5G-enabled IoT for industrial automation: A systematic review, solutions, and challenges,” Mech Syst Signal Process, vol. 135, 2020, doi: 10.1016/j.ymssp.2019.106382. DOI: https://doi.org/10.1016/j.ymssp.2019.106382
P. Varga et al., “5g support for industrial iot applications – challenges, solutions, and research gaps,” Sensors (Switzerland), vol. 20, no. 3, 2020, doi: 10.3390/s20030828. DOI: https://doi.org/10.3390/s20030828
K. Shafique, B. A. Khawaja, F. Sabir, S. Qazi, and M. Mustaqim, “Internet of things (IoT) for next-generation smart systems: A review of current challenges, future trends and prospects for emerging 5G-IoT Scenarios,” 2020. doi: 10.1109/ACCESS.2020.2970118. DOI: https://doi.org/10.1109/ACCESS.2020.2970118