W IRELESS networking technology is one of the main components that could empower a wide range of Internet-of-Things (IoT) applications including smart city, smart home, smart grid, e-health, smart transportation, etc. While providing an easily extensible solution for information exchange, wireless networks also have brought some crucial challenges due to the unstable characteristics of wireless communications. The first challenge, namely the spatial challenge, comes from the massive number of spatially-spread connected static or mobile devices affected by the limitations and disruptions of the operating environment, including propagation media, disasters, infrastructure failures, and so on. The second challenge, namely the temporal challenge, is due to the time evolution of the temporal features, such as the varying traffic rates, different quality-of-service requirements, and the state changes of the operating environment. Both spatial and temporal challenges can possibly be solved by using Computational Intelligence (CI) technologies, such as fuzzy logic and evolutionary computation. On the other hand, big data-based approaches, including deep neural networks and Long Short-Term Memory networks, could facilitate data-driven prediction and performance improvement by capturing time-dependent properties of network elements, such as user traffic and behaviors. Meanwhile, new CI technologies should be discussed in order to handle the large volume of IoT big data generated from various types of devices with different levels of speeds and characteristics. The design and the operation of a wireless network can benefit from data collected from widely deployed sensors, network devices, social networks, and other sources. We refer collectively to these data sources as “IoT big data” for convenience. These data can be highly dimensional, heterogeneous, complex, unstructured and unpredictable. The ready availability of IoT big data and the immense dividends on offer motivate a strong interest both in academia and in industry towards solving some of the vexing challenges that stand in the way of leveraging IoT big data to advance the state of the art in wireless network operations and applications. CI enables agents (or decision makers), for example, the computers and the smart devices, to computationally process and analyze the captured data and subsequently identify and explain the underlying patterns, as well as to efficiently learn the specific tasks.