In recent years, software applications for smart environments (e.g., smart healthcare systems, smart farming, smart industrial automation and smart surveillance systems) have become a major trend in computing. All these smart applications of the Internet of Things (IoT) need intelligent and scientific control for smooth integration of sensor-gathered data. For example, in smart industrial automation, scientific programming can help diagnose faulty sensors that generate erroneous data. Scientific programming can also help identify sensor-embedded pipes in industrial automation that need prioritizing in terms of data. This ensures a smoother operation of the chosen application.

Similar to industrial automation, the role of scientific programming is undeniable in almost every application of IoT. All these applications of IoT have seen phenomenal growth due to the convergence of smart sensing technology, edge intelligence, cloud computing, and advanced artificial intelligence (AI) techniques. This convergence can further be augmented with the advancement in scientific programming by designing software models, mobile applications and scalable platforms. The interplay of scientific programming and these advanced technological trends can help IoT applications grow on a massive scale. There is a need to explore and identify new models and new methods of programming to incorporate demanding features into classical programming techniques.

The aim of this Special Issue is to bring together academic and industrial practitioners to exchange and discuss the latest innovations in programming aspects by focusing on modelling and system design for various IoT applications. Original research and review articles are welcome.

Potential topics include but are not limited to the following:

• Theoretical frameworks and models for IoT applications
• Advanced programming techniques for diagnosing faulty sensors for IoT applications
• System modelling and design for a novel application of IoT
• AI-enabled programming platform for smart applications of IoT
• Machine/deep learning algorithms for IoT applications
• Programming challenges for interoperability in IoT
• Advanced security and privacy algorithms for IoT applications
• Scalable models and platforms for heterogeneous applications of IoT
• Scientific programming approaches for fusing heterogeneous data for IoT applications