Multi-Model Secure Redundant Storage for IoT Data Using Random Forests, Deep Neural Networks, and Adaptive Particle Swarm Optimization
Abstract
With the increasing deployment of IoT systems, secure and efficient data storage has become a critical challenge. This paper proposes a multi-model secure redundant storage approach for IoT data by integrating Random Forest (RF), Deep Neural Network (DNN), and Adaptive Particle Swarm Optimization (APSO), refers to the complete proposed system that integrates Random Forest (RF) for feature extraction, Deep Neural Network (DNN) for anomaly detection and sensitivity classification, and Adaptive Particle Swarm Optimization (APSO) for dynamic storage strategy adjustment. The RF extracts key features from high-dimensional data, DNN detects and classifies anomalies, and APSO dynamically adjusts storage parameters for optimized redundancy. The model was evaluated on the SmartHomeIoTData-v1.0 dataset, comprising 1,000 devices and over 1,000,000 data entries across temperature, humidity, and status metrics. Compared to baseline models (KNN, SVM), our approach improves accuracy from 90% to 95%, increases storage resource utilization to 75%, and reduces data loss probability to 0.01%. These results demonstrate enhanced system security, efficiency, and responsiveness on resource-constrained devices.
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Gonzalez-Gil, P., Martinez, J.A., & Skarmeta, A.F. (2020). Lightweight data-security ontology for IoT. Sensors, 20(3): 18. https://doi.org/10.3390/s20030801
Yakhni, S., Tekli, J., Mansour, E., & Chbeir, R. (2023). Using fuzzy reasoning to improve redundancy elimination for data deduplication in connected environments. Soft Computing, 27(17): 12387-418. https://doi.org/10.1007/s00500-023-07880-z
Zhang, T.M., Chen, R.H., Li, Z.J., Gao, C.M., Wang, C.K., & Shu, J.W. (2024). Design and Implementation of Deduplication on F2FS. Acm Transactions on Storage, 20(4): 50. https://doi.org/10.1145/3662735
Chandnani, N., & Khairnar, C.N. (2022). Bio-inspired multilevel security protocol for data aggregation and routing in IoT WSNs. Mobile Networks & Applications, 27(3): 1030-49. https://doi.org/10.1007/s11036-021-01859-6
Hameedi, S.S., & Bayat, O. (2022). Improving IoT data security and integrity using lightweight blockchain dynamic table. Applied Sciences-Basel, 12(18): 18. https://doi.org/10.3390/app12189377
Lin, S.S., Lin, W.W., Wu, K.Y., Wang, S.B., Xu, M.X., & Wang, J.Z. (2024). Cocv: A compression algorithm for time-series data with continuous constant values in IoT-based monitoring systems. Internet of Things, 25: 14. https://doi.org/10.1016/j.iot.2023.101049
Wang, Y., Gu, S.S., Zhao, L., Zhang, N., Xiang, W., & Zhang, Q.Y. (2020). Repairable fountain coded storage systems for multi-tier mobile edge caching networks. IEEE Transactions on Network Science and Engineering, 7(4): 2310-22. https://doi.org/10.1109/tnse.2019.2932727
Saura, J.R., Palacios-Marqués, D., & Ribeiro-Soriano, D. (2021). Using data mining techniques to explore security issues in smart living environments in Twitter. Computer Communications, 179: 285-95. https://doi.org/10.1016/j.comcom.2021.08.021
Xie, Y., Huang, K., Yuan, S., Li, X., & Li, F.G. (2024). Versatile remote data checking scheme for cloud-assisted internet of things. IEEE Internet of Things Journal, 11(7): 12346-61. https://doi.org/10.1109/jiot.2023.3332873
Yang, Y., Li, X.F., Zhu, D.J., Hu, H., Du, H.W., Sun, Y.D., et al. (2021). A resource-constrained edge IoT device data-deduplication method with dynamic asymmetric maximum. Intelligent Automation and Soft Computing, 30(2): 481-94. https://doi.org/10.32604/iasc.2021.019201
Chen, X., Yu, Q.X., Dai, S.H., Sun, P.F., Tang, H.C., & Cheng, L. (2024). Deep reinforcement learning for efficient IoT data compression in smart railroad management. IEEE Internet of Things Journal, 11(15): 25494-504. https://doi.org/10.1109/jiot.2023.3348487
Liao, D., Li, H., Wang, W.T., Wang, X., Zhang, M., & Chen, X. (2021). Achieving IoT data security based blockchain. Peer-to-Peer Networking and Applications, 14(5): 2694-707. https://doi.org/10.1007/s12083-020-01042-w
Ullah, F., Salam, A., Amin, F., Khan, I. A., Ahmed, J., Zaib, S. A., & Choi, G. S. (2024). Deep trust: A novel framework for dynamic trust and reputation management in the Internet of Things (IoT)-based networks. IEEE Access, 12: 87407-87419. https://doi.org/10.1109/ACCESS.2024.3409273
Navaneethan, M., & Janakiraman, S. (2023). An optimized deep learning model to ensure data integrity and security in IoT based e-commerce block chain application. Journal of Intelligent & Fuzzy Systems, 44(5): 8697-709. https://doi.org/10.3233/jifs-220743
Dai, D., & Boroomand, S. (2022). A review of artificial intelligence to enhance the security of big data systems: State-of-art, methodologies, applications, and challenges. Archives of Computational Methods in Engineering, 29(2): 1291-309. https://doi.org/10.1007/s11831-021-09628-0
Chaudhary, A., & Peddoju, S.K. (2024). ADA2 - IoT: An adaptive data aggregation algorithm for IoT infrastructure. Internet of Things, 27: 19. https://doi.org/10.1016/j.iot.2024.101299
Salam, A., Abrar, M., Ullah, F., Khan, I.A., Amin, F., & Choi, G.S. (2023). Efficient Data Collaboration Using Multi - Party Privacy Preserving Machine Learning Framework. IEEE Access, 11: 138151-138164. https://doi.org/10.1109/ACCESS.2023.3339750
Jaigirdar, F.T., Tan, B.Y., Rudolph, C., & Bain, C. (2023). Security-aware provenance for transparency in IoT data propagation. IEEE Access, 11: 55677-91. https://doi.org/10.1109/access.2023.3280928
Amanullah, M.A., Habeeb, R.A.A., Nasaruddin, F.H., Gani, A., Ahmed, E., Nainar, A.S.M., et al. (2020). Deep learning and big data technologies for IoT security. Computer Communications, 151: 495-517. https://doi.org/10.1016/j.comcom.2020.01.016
Takele, A.K., & Villányi, B. (2023). LSTM-Autoencoder-Based incremental learning for industrial internet of things. IEEE Access, 11: 137929-36. https://doi.org/10.1109/access.2023.3339556
Moulahi, T. (2022). Joining formal concept analysis to feature extraction for data pruning in cloud of things. Computer Journal, 65(9): 2484-92. https://doi.org/10.1093/comjnl/bxab085
Zhang, G.P., Chen, P.H., & Liao, Y.W. (2024). Blockchain-based secure and verifiable deduplication scheme for cloud-assisted internet of things. IEEE Internet of Things Journal, 11(8): 13995-4006. https://doi.org/10.1109/jiot.2023.3339837
Tchernykh, A., Babenko, M., Chervyakov, N., Miranda-López, V., Avetisyan, A., Drozdov, A.Y., et al. (2020). Scalable data storage design for nonstationary IoT environment with adaptive security and reliability. IEEE Internet of Things Journal, 7(10): 10171-88. https://doi.org/10.1109/jiot.2020.2981276
Zhang, S.Q., Bai, G.Y., Li, H., Liu, P.P., Zhang, M.Z., & Li, S.J. (2021). Multi-source knowledge reasoning for data-driven IoT security. Sensors, 21(22): 19. https://doi.org/10.3390/s21227579
Asif, M., Abrar, M., Salam, A., Amin, F., Ullah, F., Shah, S., & AlSalman, H. (2025). Intelligent two - phase dual authentication framework for Internet of Medical Things. Scientific Reports, 15(1): 1760. https://doi.org/10.1038/s41598-024-84713-5
Leek, E.C., Leonardis, A., & Heinke, D. (2022). Deep neural networks and image classification in biological vision. Vision Research, 197: 108058. https://doi.org/10.1016/j.visres.2022.108058
Nguyen, H.T., Phi, M.K., Ngo, X.B., Tran, V., Nguyen, L.M., & Tu, M.P. (2024). Attentive deep neural networks for legal document retrieval. Artificial Intelligence and Law, 32(1): 57-86. https://doi.org/10.1007/s10506-022-09341-8
Gonon, L. (2024). Deep neural network expressivity for optimal stopping problems. Finance and Stochastics, 28(3): 865-910. https://doi.org/10.1007/s00780-024-00538-0
Howlader, A.M., Patel, D., & Gammariello, R. (2023). Data-driven approach for instantaneous vehicle emission predicting using integrated deep neural network. Transportation Research Part D: Transport and Environment, 116: 103654. https://doi.org/10.1016/j.trd.2023.103654
Ehtemam, H., Ghaemi, M.M., Ghasemian, F., Bahaadinbeigy, K., Sadeghi-Esfahlani, S., Sanaei, A., & Shirvani, H. (2024). From data to hope: Deep neural network-based prediction of poisoning (DNNPPS) suicide cases. Iranian Journal of Public Health, 53(12): 2802-2811.
DOI: https://doi.org/10.31449/inf.v49i34.9005
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