In recent years, with the rapid development of mobile Internet of Things (IoT) infrastructure and the increasing popularity of Internet of Things applications, the complexity and operability of various mobile applications are also increasing. The development of the Internet of Things extends the scope of mobile communication from person-to-person communication to broader industries and fields such as the intelligent interconnection between people and things, even things and things. The mobile IoT will be one of the network applications with the largest amount of terminal data, the largest number of users, and the most common applications in the future mobile Internet. It will also become the main driving force for the development of network applications in the future and provide broad development prospects for the next generation network. The explosive development of the Internet of things is bound to bring new development opportunities and technical challenges to the mobile Internet. Various intelligent terminals provide a new paradigm for new social and new media in the era of mobile IoT. As an extension of the human body, wearable devices provide a tool-level solution for the perception and adaptation of mobile information services.

The mobile IoT has opened a new era of full intelligence, such as smart family, smart community, smart municipal administration, smart commerce, smart medical treatment, smart city, etc. With the wide adoption of mobile IoT, the need of developing effective methods and tools to gain insights from the collected big data is essential for establishing intelligence applications such as smart family, smart community, smart municipal administration, smart medical treatment, smart city, etc. It is believed that large data sets can be analyzed computationally to reveal patterns, trends, and associations, especially relating to human behavior and interaction. However, given the sheer volume of data and heterogeneity of data format, current practices of mobile IoT intelligence driven by big data are overwhelmed.

This Special Issue will focus on the theory and applications of big data-driven methods for improving mobile IoT intelligence. Both original research and review articles on big data acquisition, pre-processing, big data analytics, and big data-driven decision making with clear relevance in mobile IoT intelligence are strongly encouraged.

Potential topics include but are not limited to the following:

• Multi-sensor data acquisition and data representation in mobile IoT (e.g., protocols development for heterogeneous data curation)
• Data mining on cloud infrastructure in mobile IoT (e.g., communication and storage)
• Multi-source heterogeneous information fusion in mobile IoT
• Fusion of real-time data and historical data in mobile IoT
• Advanced big data analytics in mobile IoT
• Big data-driven methods of uncertainty mitigation in multiple scenarios of mobile IoT (e.g., family, community, municipal administration, commerce, medical treatment, and city)
• Applications of big data in mobile IoT (e.g., family service, municipal operation management, community security, mobile medical treatment, environmental protection, mobile clinic)
• Big data-driven models for human-machine interactions and collaborations in mobile IoT
• Big data-driven decision making based on deep learning and reinforcement learning in mobile IoT
• Big data-driven global dynamic optimization based on analytical target cascading in mobile IoT
• Big data-driven online coordinated control of multiple mobile IoTs
• Dynamic resource allocation based on intelligent algorithms in mobile IoT