Development of Artificial Intelligence of Things and Cloud Computing Environments Through Semantic Web Control Models

Introduction

The integration of Artificial Intelligence of Things (AIoT) and cloud computing has led to a new era of interconnectedness and intelligence in digital ecosystems. The proliferation of smart devices and the growing amount of data generated by these devices have prompted innovative approaches to harness this information. The Semantic Web is at the core of this convergence, enhancing interoperability, intelligence, and control in AIoT and cloud computing environments. AIoT applications are transforming various domains, from smart homes and wearable devices to industrial automation and smart cities, revolutionizing how we interact with and harness the capabilities of connected devices (Sadeeq et al., 2021).

Cloud computing has become a crucial tool for scalability, flexibility, and accessibility of digital services and applications. It allows organizations to access advanced analytics, machine learning, and AI-driven capabilities. However, integrating AIoT devices with cloud computing environments presents challenges due to the heterogeneity of data generated by IoT devices, which often differs in format, structure, and semantics, preventing interoperability and hindering the seamless exchange of information between devices and cloud platforms. The security and privacy of data transmitted between AIoT devices and cloud servers is crucial due to increasing cyber threats and data breaches (Mukhopadhyay et al., 2021). Robust security measures are needed to protect sensitive information and maintain user trust in AIoT systems. Scalability and resource management challenges in cloud computing environments pose logistical challenges, potentially leading to performance bottlenecks and degraded user experiences.

Researchers and practitioners are increasingly using Semantic Web control models to enhance AIoT and cloud computing. These models imbue digital data with well-defined meaning and context, allowing machines to understand and interpret information intelligently. They use ontologies, semantic reasoning, and knowledge representation techniques to facilitate enhanced data interoperability, semantic integration, and context-aware decision-making in AIoT and cloud computing environments (Hansen & Bøgh, 2021). This enables seamless data exchange and interoperability across heterogeneous systems. This chapter explores the application of Semantic Web control models in AIoT-cloud integration, focusing on their foundational concepts, practical implementations, and future trends. Through case studies, practical implementations, and a forward-looking analysis, the chapter aims to highlight the transformative potential of these models in shaping AIoT and cloud computing landscapes (Chang et al., 2021).

AIoT is the integration of AI technologies with IoT infrastructure, enabling intelligent, interconnected systems to perceive, reason, and act autonomously. It extends traditional IoT devices' capabilities by integrating AI algorithms, allowing real-time data analysis, actionable insights, and adapting behavior based on changing environmental conditions. The advancements in AIoT are primarily driven by several significant technological advancements (Zhang & Tao, 2020). Recent AI research advancements, including machine learning, deep learning, and natural language processing, have enabled advanced AIoT applications. These algorithms enable devices to learn from experience, recognize patterns, and make intelligent decisions without explicit programming. Edge computing architectures have democratized AIoT deployment by bringing computational resources closer to data sources, reducing latency, bandwidth usage, and enhancing privacy and security (L. Sun et al., 2020).

Sensor technologies, miniaturization, and cost reduction have led to the rise of connected devices in various sectors, including smart homes, wearables, industrial machinery, and autonomous vehicles. These sensors provide real-time data, enabling AIoT systems to monitor and control environments with unprecedented granularity. Standardization efforts, such as interoperability protocols and communication standards, have facilitated seamless integration and interoperability between different manufacturers and ecosystems (Balas et al., 2020).