<b>Integration of Expert Systems and Internet of Things for Real-time Decision Making: Review of Core Components, Architectures, Applications, and Future Prospects. </b>

Abstract

The expansion of the Internet of Things (IoT) has enabled a unique ability to collect data across multiple domains. However, conventional IoT systems face restrictions when trying to process this data for complicated, real-time decision-making because of challenges concerning latency, bandwidth, and inefficient computational power. This review analyses the integration of expert systems with IoT to resolve these challenges. Integrating rule-based reasoning and inference abilities straight into the IoT platform, using edge computing or fog computing, creates a system with the capacity to independently carry out real-time, context-aware acts. This paper offers an extensive overview of the essential concepts of expert systems and IoT, assesses state-of-the-art integration frameworks, and evaluates key application areas of expert-IoT systems, such as smart healthcare, smart agriculture, industrial automation, and smart homes. Moreover, it points out important technical and operational challenges like knowledge management, interoperability, and security concerns, while also recommending future research prospects that focus on areas such as machine learning, federated learning, and developing lightweight, standardized architecture. This paper concludes that the integration of expert systems with IoT is a revolutionary step towards the creation of more efficient, responsive, and smart environments, laying the groundwork for the upcoming smart, decentralized systems.