Unleashing the Power of AI and Machine Learning: Integration Strategies for IoT Systems

S. Mohanarangan¹ , D. Karthika² , B. Moohambigai³ , R. Sangeetha⁴

Section:Research Paper, Product Type: Journal-Paper
Vol.12 , Issue.2 , pp.25-32, Apr-2024

Online published on Apr 30, 2024

Copyright © S. Mohanarangan, D. Karthika, B. Moohambigai, R. Sangeetha . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
 

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Abstract :
In the realm of the Internet of Things (IoT), the integration of Artificial Intelligence (AI) and Machine Learning (ML) stands as a transformative force, offering unparalleled capabilities to leverage the vast amounts of data generated by connected devices. This abstract delves into the strategies and implications of integrating AI and ML within IoT systems, elucidating key concepts, challenges, and opportunities. The amalgamation of AI and ML with IoT enables enhanced data analysis, predictive insights, and autonomous decision-making at the edge of the network. This synergy empowers IoT devices to not only collect data but also interpret it intelligently, paving the way for predictive maintenance, anomaly detection, and optimization of operational processes. Keywords such as "edge computing," "real-time analytics," and "predictive maintenance" underscore the pivotal role of AI and ML in maximizing the efficiency and efficacy of IoT deployments. One of the primary challenges in this integration lies in the efficient processing and analysis of data amidst the constraints of IoT devices, including limited computational power and bandwidth. Edge computing emerges as a solution, facilitating data processing closer to the data source, thereby reducing latency and conserving network resources. Additionally, the utilization of lightweight ML algorithms optimized for resource-constrained environments becomes imperative, ensuring the feasibility of AI-powered applications on IoT devices. Furthermore, the integration of AI and ML within IoT extends beyond traditional use cases, permeating diverse domains such as healthcare, manufacturing, transportation, and smart cities. In healthcare, IoT-enabled wearables and medical devices coupled with AI-driven analytics revolutionize patient care, enabling remote monitoring, early disease detection, and personalized treatment. Similarly, in manufacturing, Industrial IoT (IIoT) solutions empowered by AI and ML optimize production processes, enhance quality control, and enable predictive maintenance, thereby augmenting productivity and competitiveness.

Key-Words / Index Term :
IoT, Artificial Intelligence, Machine Learning, Edge Computing, Predictive Maintenance, Cybersecurity, Interoperability Standards, Healthcare, Industrial IoT (IIoT), Smart Cities.

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