Internet of Things (IoT) enables the interconnection between billions of devices, industrial machines, processes, and users to exchange data without any central coordination.

However, handling large amounts of data is immensely complex in the storage, processing, and inferencing processes. Therefore, artificial intelligence (AI) has become the most promising combination with IoT for better use, storage and avoid the uncertainty management in decision making. AI in IoT is playing a significant role and can improve the value of diverse types of data sensed and collected by IoT devices. For proper utilization of this diverse type of data will offer an efficient solution for the development of products and services to achieve the user’s expectation from different sectors. Despite the various advantages of the integration of AI with different intelligent systems for various industrial applications, the appropriate application of AI poses several challenges with respect to data quality, data volume, integration, and accuracy of the inferences drawn from the collected data. In recent decades, machine learning (ML) based methods and technologies have emerged in AI and the convergence of ML and IoT will complement each other to produce a greater impact and availability of different services including healthcare, supply chain, transportation, and power sectors.

The primary purpose of this Special Issue is to attract, collate, and previously unpublished original research and review papers on the use of AI-based technologies for the development, provisioning, and performance improvement of systems, functions, and processes in diverse industrial scenarios.

Potential topics include but are not limited to the following:

• Security issues in the IoT devices
• AI-based scalable hybrid systems for IoT
• AI-based learning methods and algorithms for IoT
• Real/Industrial application-based AI systems for IoT
• Prescriptive, predictive, and descriptive analytics for IoT device issues
• Machine learning algorithms for addressing IoT device problems
• Embedded solutions in the cloud for IoT device problems
• IoT technologies with AI for smart cities, precision agriculture, industry 4.0 solutions, self-driving vehicles and health tracking, etc
• Novel machine learning and data science methods for IoT security
• Use of ML techniques for security, trust, and privacy in IoT systems
• Machine learning for emerging network management, service, and automation
• Blockchain technology supports supply chain operation
• Cloud based big data