Integration of Machine Learning and Internet of Things in Various Industries

This is revolutionizing the most important areas in which one could think: Water Quality Monitoring, Precision Agricultural Systems, Smart Manufacturing Systems, Fire Safety Systems, and finally Cyber Security with ML, DL, and IoT. This comprehensive literature review integrates latest developments on AI and IoT convergence, including architectures, applications, assessment techniques, and challenges. Relevant conclusive proof and results establish that Simplified, Scalable, and Efficient Machine Learning Algorithms such as Random Forest, SVM, KNN are more efficient than Deep Learning Algorithms, especially concerning battery life, processing, and memory of edge nodes in an IoT environment, even though there are other methods such as XGBoost that result in high accuracy, especially in intrusion detection systems. This research emphasizes domain-level applications, such as SDG-Goals through ML-IoT Platforms, Aeroponic Systems integrated through real-time Photosynthesis Equations, Fire Safety Systems integrated through AI, and Industry-۴.۰/۵.۰ Manufacturing Systems. Edge/Fog Computing, Federated Learning platforms, Blockchain security solutions, and Distributed AI platforms are some of the new solutions analyzed and shortlisted as the promising solutions to counter the rising challenges of scalability, privacy, efficiency, and real-time processing. The conclusion highlights the imperative needs of intelligent systems.

کلیدواژه ها:

AI-IoT Convergence ، Edge Computing ، Machine Learning ، Internet of things