As a new computing paradigm, the Internet of Things (IoT) acts as the network of physical objects that contain embedded technology to communicate and sense or interact with their internal states or the external environment. In the last few years, the number of devices operating in wireless IoT has experienced tremendous growth, thus wireless IoT is taking center stage as devices are expected to form a major portion of wireless communications and mobile computing. In wireless IoT applications, millions of developers are required to select and evaluate an appropriate wireless IoT platform considering their actual demands. Therefore, how to select and evaluating appropriate ones are crucial during the development and deployment process of wireless IoT applications.

Granular computing is a newly emerged concept and computing paradigm in the field of artificial intelligence, which mainly focuses on the formation, processing, and communicating information granules. Essentially, information granules arise in the process of abstraction of data and derivation of knowledge from various real-world applications. Constructed within several specific theories including fuzzy sets, rough sets, three-way decisions, formal concept analysis, quotient space, cloud models, etc., granular computing theory and its applications have been deeply explored by scholars and practitioners over the past decades. Inspired by the merits of granular computing, individuals can observe, analyze and solve many complicated problems from diverse information granules, which is conducive to enhancing the validity and efficiency of problem-solving from the perspective of intelligent information depiction and processing. Therefore, exploring advances in granular computing methods is likely to provide an efficient way for evaluating wireless IoT platforms.

This Special Issue aims to bring together original research and review articles discussing advances in granular computing methods for evaluating wireless IoT platforms. Experimental and theoretical studies for the evaluation of wireless IoT platforms are encouraged.

Potential topics include but are not limited to the following:

• Theory and methods of rough set theory in the evaluation of wireless IoT platforms
• Three-way decisions in the evaluation of wireless IoT platforms
• Fuzzy set and logic in applications of wireless communication networks and mobile computing
• Formal concept analysis in the evaluation of wireless IoT platforms
• Cloud models in the evaluation of wireless IoT platforms
• Uncertainty in granular computing and complex data processing in wireless environments
• Soft computing and its applications in wireless communication networks
• Intelligent decision making in the evaluation of wireless IoT platforms
• Knowledge discovery and data mining in the evaluation of wireless IoT platforms
• Machine learning techniques in the evaluation of wireless IoT platforms