A COMPREHENSIVE SURVEY ON MACHINE LEARNING AND DEEP LEARNING APPLICATIONS IN INTERNET-OF-THINGS: CURRENT DEVELOPMENTS AND FUTURE PROSPECTS
Gyana Ranjan Patra, Shakitjeet Mahapatra · 2023
Machine learning and deep learning are two distinct subfields within the domain of artificial intelligence that have found extensive applications across diverse disciplines, including but not limited to computer vision, natural language processing, speech recognition, and robotics. Nevertheless, the conventional approaches to machine learning and deep learning frequently encounter difficulties when confronted with the vast data produced by Internet of Things (IoT) devices, including sensors, cameras, smartphones, and wearables. Hence, an increasing demand arises for the creation of novel methodologies and frameworks capable of harnessing the capabilities of machine learning and deep learning in order to facilitate intelligent and efficient IoT applications. This paper presents a comprehensive examination of the present state-of-the-art and forthcoming developments in machine learning and deep learning as applied to IoT applications. In this paper, we commence by presenting a comprehensive overview of the fundamental concepts and principles underlying machine learning and deep learning. Subsequently, we proceed to examine a selection of notable applications of these methodologies within the context of IoT scenarios, encompassing domains such as smart home, smart city, smart health, and smart industry. The primary challenges and outstanding concerns that require attention in this burgeoning industry are also deliberated upon, including but not limited to data quality, security, privacy, scalability, and interoperability. In conclusion, this study emphasises potential avenues and prospects for further investigation and advancement in the domain of machine learning and deep learning as applied to IoT applications.