Mobile communications are organized by industrial devices, wireless protocols, and infrastructure, which presents certain advantages, such as high efficiency and centralized management, carrying signals such as data, voice, and messages. Currently, the Internet of Things (IoT) systems connect various collections of smart devices, cloud data centers, fog nodes and mobile applications in many smart environments. It enables millions of connected mobile devices to execute real-time applications directly at the network edge and provides sufficient mobility support. Furthermore, the sensors embedded in different entities of the mobile ecosystem make it possible to collect a large amount of data and information for real-time processing and decision-making, such as real-time traffic data, sensor data from driverless cars, or entertainment streaming recommendations. This data can be processed and analyzed to provide deeper insights into the systems to provide better services and facilities to the end-users.

Due to the generation of large volumes of mobile data, there are several issues and challenges associated with its implementations, including security and privacy, interoperability, data integration and transportation, data ownership, conventional mindset, etc. Furthermore, dealing with isolated mobile big data is important for companies to achieve higher quality production and agility, improve their decision-making and save labor costs. Therefore, the Intelligent Decision Support System (IDSS) paradigm is emerging as an attractive solution for processing and managing mobile and wireless communications in IoT systems. IDSS investigates the substantial amount of data interactions in industry, medical systems, smart transportations, and other smart applications. It is a dynamic information model as important data is added consistently and uses machine learning algorithms to improve the process of prediction and detection. Furthermore, IDSS-based wireless communications can benefit a range of IoT systems where complex and critical decisions are made under time pressure, when decision-makers are on the move, and the environment is dynamic and uncertain.

This Special Issue brings together leading research experts from industry and academia to present novel and original contributions on methodologies, applications, standards, and protocols for solving these issues and challenges. We are also interested in identifying possible solutions and opportunities for reliable, privacy preserved, and secured management of IDSS for mobile communication systems. Finally, we hope this Special Issue helps to identify promising directions and future trends for those seeking to contribute to this area of research. We welcome original research and review articles.

Potential topics include but are not limited to the following:

• IDSS for knowledge discovery in mobile communications
• IDSS for mobile health applications
• IDSS models for industrial mobile applications
• Big data management based on mobile IDSS
• IDSS for mobile robotic and control systems
• Cloud resource management based on mobile IDSS
• IDSS-enabled mobile smart farming and agriculture
• IDSS on financial and computational economics for mobile communications in IoT applications
• IDSS for trust, security, and privacy in mobile communications
• Analysis and interpretation of mobile IDSS
• Application of AI and big data in mobile IDSS
• Data process, evaluation and selection, and planning and design methods for mobile IDSS applications
• Combination of IoT and big data analytics for mobile IDSS