Machine Learning Empowerment for IoT Edge Devices: Enhancing Intelligence at the Network's Edge
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Abstract
As the Internet of Things (IoT) continues to burgeon, the demand for intelligent processing at the network's edge becomes increasingly imperative. This research paper delves into the realm of Machine Learning (ML) applications tailored for IoT edge devices, exploring innovative approaches to enhance their intelligence and decision-making capabilities. The abstract elucidates the key components of the research, including the integration of ML algorithms at the edge, the optimization of resource-constrained environments, and the potential transformative impact on real-time analytics. By harnessing the synergy between ML and IoT edge devices, this study contributes to the evolving landscape of smart and responsive edge computing paradigms.
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Smith, J. A. (2019). Machine Learning Applications in Smart Manufacturing. Journal of Industrial Engineering, 45(3), 102-128.
Johnson, R. B. (2020). Edge Computing Architectures: A Comprehensive Review. IEEE Transactions on Cloud Computing, 8(2), 245-261.
Chen, L., & Wang, Q. (2018). Federated Learning: A Survey. Journal of Machine Learning Research, 20(45), 1-29.
Garcia, M. C. (2021). Security and Privacy Challenges in Decentralized Machine Learning. ACM Transactions on Privacy and Security, 18(3), 102-129.
Wang, Y., & Li, Z. (2017). Real-time Analytics for IoT Edge Devices: A Case Study in Smart Cities. IEEE Internet of Things Journal, 4(6), 1783-1792.
Brown, A. M. (2019). Wearable Devices in Healthcare: A Comprehensive Review. Journal of Medical Devices, 14(2), 12-28.
Kim, S., & Lee, S. (2020). Optimizing Traffic Flow in Smart Cities Using Machine Learning. Transportation Research Part C: Emerging Technologies, 112, 45-61.
Whig, P., Yathiraju, N., Modhugu, V. R., & Bhatia, A. B. (2024). 13 Digital Twin for. AI-Driven Digital Twin and Industry 4.0: A Conceptual Framework with Applications, 202.
Whig, P., Battina, D. S., Venkata, S., Bhatia, A. B., & Alkali, Y. J. (2024). Role of Intelligent IoT Applications in Fog Computing. Fog Computing for Intelligent Cloud IoT Systems, 99-118.
Whig, P., Kouser, S., Bhatia, A. B., Purohit, K., & Modhugu, V. R. (2024). 9 Intelligent Control for Energy Management. Microgrid: Design, Optimization, and Applications, 137.
Whig, P., Kouser, I. S., Bhatia, A. B., Nadikattu, I. R. R., & Alkali, Y. J. (2024). 6 IoT Industrial. Wireless Communication Technologies: Roles, Responsibilities, and Impact of IoT, 6G, and Blockchain Practices, 101.
Whig, P., Kasula, B. Y., Bhatia, A. B., Nadikattu, R. R., & Sharma, P. (2024). Digital Twin-Enabled Solution for Smart City Applications. In Transforming Industry using Digital Twin Technology (pp. 259-280). Cham: Springer Nature Switzerland.
Whig, P., Bhatia, A. B., Nadikatu, R. R., Alkali, Y., & Sharma, P. (2024). GIS and Remote Sensing Application for Vegetation Mapping. In Geo-Environmental Hazards using AI-enabled Geospatial Techniques and Earth Observation Systems (pp. 17-39). Cham: Springer Nature Switzerland.
Whig, P., & Kautish, S. (2024). VUCA Leadership Strategies Models for Pre-and Post-pandemic Scenario. In VUCA and Other Analytics in Business Resilience, Part B (pp. 127-152). Emerald Publishing Limited.
Whig, P., Sharma, P., Bhatia, A. B., Nadikattu, R. R., & Bhatia, B. (2024). Machine Learning and its Role in Stock Market Prediction. Deep Learning Tools for Predicting Stock Market Movements, 271-297.
Whig, P., Gera, R., Bhatia, A. B., & Reddy, R. (2024). Convergence of Blockchain and IoT in Healthcare. Convergence of Blockchain and Internet of Things in Healthcare, 277.
Whig, P., Bhatia, A. B., Nadikatu, R. R., Alkali, Y., & Sharma, P. (2024). 3 Security Issues in. Software-Defined Network Frameworks: Security Issues and Use Cases, 34.
Xu, W., & Zhang, H. (2018). Challenges and Solutions in Machine Learning on Resource-Constrained Edge Devices. Proceedings of the ACM/IEEE Conference on Internet of Things Design and Implementation, 13-18.
Hernandez, C. D. (2019). A Review of Machine Learning Applications in Precision Agriculture. Computers and Electronics in Agriculture, 158, 123-134.
Liu, Y., & Wang, Y. (2017). Machine Learning Approaches for Predictive Maintenance in Smart Manufacturing. Procedia CIRP, 72, 1013-1018.
Johnson, M. E. (2021). Edge Intelligence for Smart Homes: A Comprehensive Analysis. IEEE Transactions on Consumer Electronics, 67(2), 234-248.
Chen, X., & Liu, Z. (2018). A Comprehensive Study on Machine Learning in Industrial IoT. IEEE Transactions on Industrial Informatics, 14(4), 1923-1932.
Li, Q., & Zhang, M. (2020). Privacy-Preserving Machine Learning on Edge Devices: Challenges and Solutions. IEEE Transactions on Dependable and Secure Computing, 18(1), 112-128.
Kim, H., & Lee, J. (2019). Machine Learning and AI for Traffic Management in Smart Cities. Sustainable Cities and Society, 45, 112-128.
Sharma, R., & Verma, N. (2018). Neuromorphic Computing: A Comprehensive Review. Frontiers in Neuroscience, 12, 112-128.
Neha Dhaliwal. (2021). Contributions as a Scrum Master: Facilitating Agile Project Management in Bioinformatics Research with AI. International Journal on Recent and Innovation Trends in Computing and Communication, 9(3), 44–52. Retrieved from https://www.ijritcc.org/index.php/ijritcc/article/view/10671
Priyanka Koushik, S. M. (2024). Elevating Customer Experiences and Maximizing Profits with Predictable Stockout Prevention Modelling. International Journal of Intelligent Systems and Applications in Engineering, 12(21s), 1171–1178. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/5547
Sumit Mittal, "Framework for Optimized Sales and Inventory Control: A Comprehensive Approach for Intelligent Order Management Application," International Journal of Computer Trends and Technology, vol. 72, no. 3, pp. 61-65, 2024. Crossref, https://doi.org/10.14445/22312803/IJCTT-V72I3P109