Volume 11 • Issue 2 • PP: 21-34 • 2023
Enhancing IoT-Based Intelligent Video Surveillance through Multi-Sensor Fusion and Deep Reinforcement Learning
Open Access & Copyright
© 2023 The Author(s). Published by ASPG. This article is licensed under the Creative Commons Attribution 4.0 International License (CC BY 4.0).
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Currenlty, wireless communication that is successful in the Internet of Things (IoT) must be long-lasting and self-sustaining. The integration of machine learning (ML) techniques, including deep learning (DL), has enabled IoT networks to become highly effective and self-sufficient. DL models, such as enhanced DRL (EDRL), have been developed for intelligent video surveillance (IVS) applications. Combining multiple models and optimizing fusion scores can improve fusion system design and decision-making processes. These intelligent systems for information fusion have a wide range of potential applications, including in robotics and cloud environments. Fuzzy approaches and optimization algorithms can be used to improve data fusion in multimedia applications and e-systems. The camera sensor is developing algorithms for mobile edge computing (MEC) that use action-value techniques to instruct system actions through collaborative decision-making optimization. Combining IoT and deep learning technologies to improve the overall performance of apps is a difficult task. With this strategy, designers can increase security, performance, and accuracy by more than 97.24 %, as per research observations.
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References
[1] Pham, D. V., Nguyen, G. L., Nguyen, T. N., Pham, C. V., & Nguyen, A. V. (2020). Multi-topic misinformation blocking with a budget constraint on online social networks. IEEE Access, 8, 78879-78889.
[2] Gao, J., Wang, H., & Shen, H. (2020, May). Smartly handling renewable energy instability in supporting a cloud datacenter. In 2020 IEEE international parallel and distributed processing symposium (IPDPS) (pp. 769-778). IEEE.
[3] Kumar, A., Biswas, A.K., Kumar, A. and Yadav, R.K., 2022, June. Optimising IOT Based SmartHome Systems Using Machine-Learning Algorithm. In 2022 7th International Conference on Communication and Electronics Systems (ICCES) (pp. 1133-1139). IEEE.
[4] Elgendy, I. A., Zhang, W. Z., He, H., Gupta, B. B., & Abd El-Latif, A. A. (2021). Joint computationoffloading and the task is caching for multi-user and multi-task MEC systems: reinforcement learningbased algorithms. Wireless Networks, 27(3), 2023-2038.
[5] Jan, M. A., Cai, J., Gao, X. C., Khan, F.,Mastorakis, S., Usman, M.,& Watters, P. (2020). Security and blockchain convergence with the Internet of Multimedia Things: Current trends, research challenges, and future directions. Journal of Network and Computer Applications, 102918.
[6] Jaber, M.M., Yussof, S., Ali, M.H., Abd, S.K., Jassim, M.M., Alkhayyat, A. and Mubarak, H., 2022. PIRAP: PPDA-FAF: Maintaining Data Security and Privacy in Green IoT-Based Agriculture. International Journal of Cooperative Information Systems.
[7] Shams N. Abdul-wahab, Mostafa Abdulghafoor Mohammed, & Omar A. Hammood. (2021). Theoretical Background of steganography. Mesopotamian Journal of CyberSecurity, 2021, 22–32. https://doi.org/10.58496/MJCS/2021/005
[8] Ranjan, G., Nguyen, T. N., Mekky, H., & Zhang, Z. L. (2020, December). On virtual id assignment in networks for high resilience routing: a theoretical framework. In GLOBECOM 2020-2020 IEEE Global Communications Conference (pp. 1-6). IEEE.
[9] Gao, J., Wang, H., & Shen, H. (2020). Task failure prediction in cloud data centers using deep learning. IEEE Transactions on Services Computing.
[10] Zahraa Faiz Hussain, & Hind Raad Ibraheem. (2023). Novel Convolutional Neural Networks based Jaya algorithm Approach for Accurate Deepfake Video Detection. Mesopotamian Journal of CyberSecurity, 2023, 35–39. https://doi.org/10.58496/MJCS/2023/007.
[11] Abd EL-Latif, A. A., Abd-El-Atty, B., & Venegas-Andraca, S. E. (2020). Controlled alternate quantum walk-based pseudo-random number generator and its application to quantum color image encryption. Physica A: Statistical Mechanics and its Applications, 547, 123869.
[12] Awuson-David, K., Al-Hadhrami, T., Alazab, M., Shah, N., &Shalaginov, A. (2021). BCFL logging: An approach to acquire and preserve admissible digital forensics evidence in the cloud ecosystem. Future Generation Computer Systems, 122, 1-13.
[13] Chi, X. C., Yang, Y. S., Wang, Y. H., Gao, J. C., Sui, N., Yang, H. G., ... & Zhang, H. Z. (2015). Studying of photoluminescence characteristics of CdTe/ZnS QDs manipulated by TiO2 inverse opal photonic crystals. Optical Materials, 46, 350-354.
[14] Amudha, G. (2021). Dilated Transaction Access and Retrieval: Improving the Information Retrieval of Blockchain-Assimilated Internet of Things Transactions. Wireless Personal Communications, 1-21.
[15] Farooqui, N.A., Mishra, A.K. and Mehra, R., 2022. IOT based automated greenhouse using machine learning approach. International Journal of Intelligent Systems and Applications in Engineering, 10(2), pp.226-231.
[16] Nassar, A., & Yilmaz, Y. (2021). Deep reinforcement learning for adaptive network slicing in 5G for intelligent vehicular systems and smart cities. IEEE Internet of Things Journal.
[17] Liu, Y., Zhang, W., Pan, S., Li, Y., & Chen, Y. (2020). Analyzing the robotic behavior in a smart city with deep enforcement and imitation learning using IoRT. Computer Communications, 150, 346-356.
[18] Dai, Y., Xu, D., Maharjan, S., Chen, Z., He, Q., & Zhang, Y. (2019). Blockchain and deep reinforcement learning empowered intelligent 5G beyond. IEEE Network, 33(3), 10-17.
[19] Nguyen, T. T., &Reddi, V. J. (2019). Deep reinforcement learning for cyber security. arXiv preprint arXiv:1906.05799.
[20] Janakiramaiah, B., Kalyani, G., & Jayalakshmi, A. (2021). Automatic alert generation in a surveillance system for smart city environment using a deep learning algorith m. Evolutionary Intelligence, 14(2), 635-642.
[21] Chen, Q., Wang, W., Wu, F., De, S., Wang, R., Zhang, B., & Huang, X. (2019). A survey on an emerging area: Deep learning for smart city-data. IEEE Transactions on Emerging Topics in Computational Intelligence, 3(5), 392-410.
[22] Luong, N. C., Hoang, D. T., Gong, S., Niyato, D., Wang, P., Liang, Y. C., & Kim, D. I. (2019). Applications of deep reinforcement learning in communications and networking: A survey. IEEE Communications Surveys & Tutorials, 21(4), 3133-3174.
[23] Habibzadeh, H., Kaptan, C., Soyata, T., Kantarci, B., &Boukerche, A. (2019). Smart city system design: A comprehensive study of the application and data planes. ACM Computing Surveys (CSUR), 52(2), 1-38.
[24] Yu, L., Qin, S., Zhang, M., Shen, C., Jiang, T., & Guan, X. (2021). A review of deep reinforcement learning for smart building energy management. IEEE Internet of Things Journal.
[25] Sreenu, G., &Durai, M. S. (2019). Intelligent video surveillance: a review through deep learning techniques for crowd analysis. Journal of Big Data, 6(1), 1-27.
[26] Dai, Y., Xu, D., Zhang, K., Maharjan, S., & Zhang, Y. (2020). Deep reinforcement learning and permissioned blockchain for content caching in vehicular edge computing and networks. IEEE Transactions on Vehicular Technology, 69(4), 4312-4324.
[27] Xu, S., Liu, Q., Gong, B., Qi, F., Guo, S., Qiu, X., & Yang, C. (2020). RJCC: Reinforcementlearning-based joint communicational-and-computational resource allocation mechanism for smart city IoT. IEEE Internet of Things Journal, 7(9), 8059-8076.
[28] Cai, Z., Li, D., Deng, L., & Yao, X. (2021). Smart city framework based on intelligent sensor network and visual surveillance. Concurrency and Computation: Practice and Experience, 33(12), e5301.
[29] Cao, Z., Zhang, H., Cao, Y., & Liu, B. (2019, December). A deep reinforcement learning approach to multi-component job scheduling in edge computing. In 2019 15th International Conference on Mobile Ad-Hoc and Sensor Networks (MSN) (pp. 19-24). IEEE.
[30] Alli, A. A., &Alam, M. M. (2019). SecOFF-FCIoT: Machine learning-based secure offloading in FogCloud of things for smart city applications. Internet of Things, 7, 100070.
[31] Park, J. H., Salim, M. M., Jo, J. H., Sicato, J. C. S., Rathore, S., & Park, J. H. (2019). CIoT -Net: a scalable cognitive IoT-based smart city network architecture. Human-centric Computing and Information Sciences, 9(1), 1-20.
[32] Alsudani, M.Q., Jaber, M.M., Ali, M.H., Abd, S.K., Alkhayyat, A., Kareem, Z.H. and Mohhan, A.R., 2023. Smart logistics with IoT-based enterprise management system using global manufacturing. Journal of Combinatorial Optimization, 45(2), p.57.
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