In recent years, there have been advances in artificial intelligence (AI) for a broad range of machine learning techniques, such as deep learning, reinforcement learning, and transfer learning. These are considered efficient and advanced tools to address non-trivial technical challenges in both science and engineering. AI-based solutions have been adopted to address challenges in many applications, for example smart energy systems, intelligent manufacturing, intelligent transportation, healthcare, and public safety, among others. In parallel, the Internet of Things (IoT) has experienced rapid development in the past decade. It deploys a massive number of smart terminals, including sensors, actuators, and establishes ubiquitous connections between smart devices and objects, with which one can perceive the world more clearly and control various systems more accurately. IoT also interconnects various separated intelligent systems into a vast smart world.

At present, there is an increasing demand for the convergence of AI and IoT to tackle programming issues in both scientific and engineering applications, such as high-performance data processing and analysis for intelligent decision-making of large-scale complex systems, and lightweight machine learning-based solutions in IoT-driven applications. Novel methods, models, algorithms, and tools are considered worth further research to improve AI and IoT driven solutions in terms of efficiency, scalability, security, and resilience, which significantly benefits the scientific and engineering programming community.

This Special Issue will focus on state-of-the-art research efforts and new perspectives in scientific and engineering computing driven by the convergence of AI and IoT, with particular focus on scientific innovations in methodologies, approaches, and algorithms to enable the efficient, sustainable, and secure operation of complex systems, such as smart grids, or transportation systems. This Special Issue can serve as a forum for researchers and professionals to explore and develop knowledge and insights into AI and IoT driven applications in various domains. We welcome both original research and review articles.

Potential topics include but are not limited to the following:

• Scientific programming theories, methods and tools for the convergence of AI and IoT
• Security, scalability, reliability, and portability in machine learning and IoT driven applications
• AI and IoT driven framework for ubiquitous sensing and data analytics
• AI and IoT driven techniques in cloud-edge computing environments
• Architecture, operation paradigms, and resource management for the convergence of AI and IoT systems
• Cost-benefit analysis of AI and IoT enabled systems against legacy systems
• Testbeds, implementation, and practices of AI and IoT driven solutions in different application domains, e.g., smart grid, advanced manufacturing, intelligent transportation, healthcare, smart cities, and public safety