An Innovative IoT Framework using Machine Learning for Predicting Information Loss at the Data Link Layer in Smart Networks
Received: 12 November 2024 | Revised: 15 December 2024 | Accepted: 29 December 2024 | Online: 3 April 2025
Corresponding author: Srikantaswamy Mallikarjunaswamy
Abstract
In smart networks, data are becoming increasingly complex, and enhancement methods are required to ensure data integrity and reliability. This paper proposes a novel IoT framework using machine learning for the prediction and mitigation of information loss at the data link layer, where conventional methods have many limitations. These methods cannot handle dynamic networking conditions and complex data traffic on any network, yielding smaller accuracy with a high false positive ratio. This work proposes a Machine Learning-based Information Loss Prediction Framework (ML-ILPF) using machine learning algorithms such as Support Vector Machine (SVM) and Long Short-Term Memory (LSTM) networks to overcome these challenges. These models analyze the historical data of the network to identify anomalies and predict possible loss. Compared to traditional methods, the proposed ML-ILPF outperformed both Static Threshold-Based Methods (STBM) and Basic Statistical Models (BSM) with an increase of 0.25% in accuracy and a reduction of 0.30% in false positives. This improvement shows real strength in the inclusion of machine learning in IoT frameworks toward smarter and more reliable network management. ML-ILPF is a promising solution that can help predict information loss at DLLS and improve the reliability and efficiency of smart networks, opening the window for more resilient IoT applications.
Keywords:
IoT framework, machine learning, information loss prediction, data link layer, smart networks, supervised learning, deep learningDownloads
References
M. Bagaa, T. Taleb, J. B. Bernabe, and A. Skarmeta, "A Machine Learning Security Framework for Iot Systems," IEEE Access, vol. 8, pp. 114066–114077, 2020.
A. Hameed, J. Violos, and A. Leivadeas, "A Deep Learning Approach for IoT Traffic Multi-Classification in a Smart-City Scenario," IEEE Access, vol. 10, pp. 21193–21210, 2022.
M. Akter, N. Moustafa, T. Lynar, and I. Razzak, "Edge Intelligence: Federated Learning-Based Privacy Protection Framework for Smart Healthcare Systems," IEEE Journal of Biomedical and Health Informatics, vol. 26, no. 12, pp. 5805–5816, Dec. 2022.
P. Kumar et al., "PPSF: A Privacy-Preserving and Secure Framework Using Blockchain-Based Machine-Learning for IoT-Driven Smart Cities," IEEE Transactions on Network Science and Engineering, vol. 8, no. 3, pp. 2326–2341, Jul. 2021.
G. Dhiman and N. S. Alghamdi, "SMoSE: Artificial Intelligence-Based Smart City Framework Using Multi-Objective and IoT Approach for Consumer Electronics Application," IEEE Transactions on Consumer Electronics, vol. 70, no. 1, pp. 3848–3855, Feb. 2024.
R. Alfred, J. H. Obit, C. P.-Y. Chin, H. Haviluddin, and Y. Lim, "Towards Paddy Rice Smart Farming: A Review on Big Data, Machine Learning, and Rice Production Tasks," IEEE Access, vol. 9, pp. 50358–50380, 2021.
V. Gotarane, S. Abimannan, S. Hussain, and R. R. Irshad, "A Hybrid Framework Leveraging Whale Optimization and Deep Learning With Trust-Index for Attack Identification in IoT Networks," IEEE Access, vol. 12, pp. 36296–36310, 2024.
P. K. Gkonis et al., "Leveraging Network Data Analytics Function and Machine Learning for Data Collection, Resource Optimization, Security and Privacy in 6G Networks," IEEE Access, vol. 12, pp. 21320–21336, 2024.
A. Musaddiq, R. Ali, S. W. Kim, and D.-S. Kim, "Learning-Based Resource Management for Low-Power and Lossy IoT Networks," IEEE Internet of Things Journal, vol. 9, no. 17, pp. 16006–16016, Sep. 2022.
S. Sai, K. S. Bhandari, A. Nawal, V. Chamola, and B. Sikdar, "An IoMT-Based Incremental Learning Framework With a Novel Feature Selection Algorithm for Intelligent Diagnosis in Smart Healthcare," IEEE Transactions on Machine Learning in Communications and Networking, vol. 2, pp. 370–383, 2024.
A. P. Sayakkara and N. A. Le-Khac, "Electromagnetic Side-Channel Analysis for IoT Forensics: Challenges, Framework, and Datasets," IEEE Access, vol. 9, pp. 113585–113598, 2021.
E. Eldeeb, M. Shehab, and H. Alves, "A Learning-Based Fast Uplink Grant for Massive IoT via Support Vector Machines and Long Short-Term Memory," IEEE Internet of Things Journal, vol. 9, no. 5, pp. 3889–3898, Mar. 2022.
M. A. Ferrag, L. Shu, O. Friha, and X. Yang, "Cyber Security Intrusion Detection for Agriculture 4.0: Machine Learning-Based Solutions, Datasets, and Future Directions," IEEE/CAA Journal of Automatica Sinica, vol. 9, no. 3, pp. 407–436, Mar. 2022.
L. Nkenyereye, J. Hwang, Q. V. Pham, and J. Song, "Virtual IoT Service Slice Functions for Multiaccess Edge Computing Platform," IEEE Internet of Things Journal, vol. 8, no. 14, pp. 11233–11248, Jul. 2021.
S. Siddiqui et al., "Toward Software-Defined Networking-Based IoT Frameworks: A Systematic Literature Review, Taxonomy, Open Challenges and Prospects," IEEE Access, vol. 10, pp. 70850–70901, 2022.
P. Franco, J. M. Martinez, Y. C. Kim, and M. A. Ahmed, "A Framework for IoT Based Appliance Recognition in Smart Homes," IEEE Access, vol. 9, pp. 133940–133960, 2021.
U. Khalil, Mueen-Uddin, O. A. Malik, and S. Hussain, "A Blockchain Footprint for Authentication of IoT-Enabled Smart Devices in Smart Cities: State-of-the-Art Advancements, Challenges and Future Research Directions," IEEE Access, vol. 10, pp. 76805–76823, 2022.
A. Y. A. B. Ahmad, N. Verma, N. M. Sarhan, E. M. Awwad, A. Arora, and V. O. Nyangaresi, "An IoT and Blockchain-Based Secure and Transparent Supply Chain Management Framework in Smart Cities Using Optimal Queue Model," IEEE Access, vol. 12, pp. 51752–51771, 2024.
O. G. Manzanilla-Salazar, F. Malandra, H. Mellah, C. Wette, and B. Sanso, "A Machine Learning Framework for Sleeping Cell Detection in a Smart-City IoT Telecommunications Infrastructure," IEEE Access, vol. 8, pp. 61213–61225, 2020.
N. K. Al-Shammari, T. H. Syed, and M. B. Syed, "An Edge – IoT Framework and Prototype based on Blockchain for Smart Healthcare Applications," Engineering, Technology & Applied Science Research, vol. 11, no. 4, pp. 7326–7331, Aug. 2021.
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Copyright (c) 2025 Poornima Madaraje Urs, Anitha Thulavanur Narayana Reddy, Srikantaswamy Mallikarjunaswamy, Umashankar Mynayakanahally Lakshminarayan
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