IoT Revolutionized: How Machine Learning is Transforming Data, Applications, and Industries

• Published in: Traektorii͡a︡ nauki : mezhdunarodnyĭ ėlektronnyĭ nauchnyĭ zhurnal Vol. 10; no. 6; pp. 8001 - 8007
• Main Authors: Akintayo, Taiwo Abdulahi, Olusola, Raphael Aduramimo, Enabulele, Ewemade Cornelius, Oyesanya, Ayodele, Olanrewaju, Samuel Ayanwunmi, Celestina, Moyosore Owoeye, Sulaimon, Balogun Oluwaseyi, Anoliefo, Ogechukwu Ada Lorretta, Olumide, Ayanwunmi Victor, Ridwan, Olamilekan Jamiu, Adediran, Adedokun Seyi
• Format: Journal Article
• Language: English
• Published: Altezoro, s. r. o. Dialog 30.06.2024
• Subjects: ICT Information and Communications Technologies; Methodology and research technology; Internet of Things (IoT); Internet of Behavior; Data Analysis; Machine Learning (ML); Intelligent DecisionMaking; Sensor Data; Smart Environments
• ISSN: 2413-9009; 2413-9009
• DOI: 10.22178/pos.105-30

Integrating machine learning (ML) with the Internet of Things (IoT) reveals hidden patterns and insights from extensive sensor data, enabling IoT to become omnipresent and make intelligent decisions without explicit programming. ML is essential for IoT to meet the future needs of businesses, governments, and individuals. IoT aims to sense its environment and automate decision-making through intelligent methods, emulating human decisions. This paper reviews and categorises existing literature on ML-enabled IoT from three perspectives: data, applications, and industries. We examine advanced methods and applications by reviewing various sources, emphasising how ML and IoT work together to create more innovative environments. We also discuss emerging trends such as the Internet of Behavior, pandemic management, autonomous vehicles, edge and fog computing, and lightweight deep learning. Furthermore, we identify challenges to IoT in four categories: technological, individual, business, and societal. This paper aims to leverage IoT opportunities and address challenges for a more prosperous and sustainable future.