Real-time Multimedia Analytics for IoT Applications: Leveraging Machine Learning for Insights

Abstract

The combination of real-time multimedia analytics and Internet of Things (IoT) applications, along with machine learning techniques, has shown great potential in improving the capabilities of IoT systems. This study investigates the potential of machine learning to gain insights into IoT applications. By thoroughly examining existing literature and analyzing current trends, this study explores essential goals such as improving IoT systems' data processing, decision-making, and security. This study extensively examines the literature on real-time multimedia analytics, machine learning algorithms, and IoT applications using a systematic approach. Doing so aims to provide a comprehensive overview of the field's current state and highlight the main challenges and opportunities. The significant discoveries highlight the impressive capabilities of machine learning algorithms, including Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), in efficiently handling intricate multimedia data. These algorithms empower organizations to gain real-time insights and make informed decisions. Addressing challenges such as computational constraints, data privacy, and multimodal data integration is crucial for policy implications. This can be achieved through investments in edge computing infrastructure, developing low-power machine learning algorithms, and implementing robust privacy and security measures.