Complexity science focuses on the problems ignored by traditional science. It highlights postmodern concepts such as diversity and evolution. Due to some difficult and complicated projects and systems involved in complexity science, there is a lot of information that traditional methods and measures cannot handle. For example, nonlinear equations are difficult to solve by traditional linear differential equations. Computers make it possible to solve the nonlinear equations of complexity problems, and rapid development has been made in the research of nonlinear equations. Complexity science provides a significant chance to research new virtual information spaces with modeling and simulation. The topology of Wireless Sensor Networks (WSN) is similar to Artificial Neural Networks (ANN), which makes the ANN algorithm for distributed processing able to be mapped to WSN as a computing platform. Since such packets carrying the output value of neurons complete wireless communication through multihop routing, the latency caused by media access, packet processing, and frequency hopping is mainly affected by distance.

Along with the deepening of the application of the Internet of Things and the usage of big-scale sensors, real-time data becomes more important and abundant. Through kinds of sensors, real-time location and status can be obtained in the marine, air, or land environment. There are sensors such as weather monitoring equipment, vehicle monitoring equipment, and environmental protection monitoring sites. Acquiring and integrating these real-time locations and statuses help people to grasp the operating status of important properties such as machines and equipment. In a traditional virtual information space, virtual reality (VR) equipment needs accelerating sensors, gyroscopes, magnetometers, and infrared sensors to receive location information, movement information, the direction of movement, and speed of movement to get a picture that is symmetrical to the movement of a head-mounted display to finally display virtual reality. However, a clear delay exists in every system of traditional sensors, which reduces the experience for users. In education, sensor technologies can support teaching by changing the traditional teaching model. Sensor technology can make a prompt response to the learning situation, which makes aiming guidance impossible. Based on the full usage of complexity science and computer simulation research results, sensor data analysis can be combined with visual technology to excavate the learning scenes of users. This can provide different learning contents according to different scenes to get the best learning effect for the follow-up. An intelligent sensor system combines advanced data analysis methods with sophisticated sensor technology. Driven by the miniaturization of sensor hardware and the latest technology of machine learning algorithms, the relevant systems can realize the interaction, and independently extract and communicate advanced information about the sensing environment, thus promoting the current digital wave of industry, health care, electronic consumption, education, and other fields. At present, it is still necessary to conduct a lot of research on the integration of complex data collected by sensors, covering from a single sensor to a large intelligent sensor system, to achieve efficient and safe use of sensor data and achieve robust autonomous operation.

This Special Issue aims to collect high-quality research and the latest technologies in computer simulation of complexity problem areas related to sensors, providing a platform for researchers related to this area to communicate, where they can discuss their work and advanced progress in the area together. We welcome original research and review articles.

Potential topics include but are not limited to the following:

• Analysis methods of sophisticated data in sensor systems and WSN
• Cloud computing, fog computing, and edge computing applied in the analysis of complicated sensor data
• Data collecting and analyzing wearable equipment in the medical treatment of the Internet of Things
• Visual technology of data information in the sensor equipment of somatic sense network
• Complex and three-dimensional (3D) environment sensing of multi-sensors facing auto-driving
• Visualization of Geological Information of Combined Graphs Based on Multi-Sensors
• Visual Observation of Sensor Data in the Internet of Things Using 3D Geographic Information System (GIS)
• Sensors in intelligent sports and fitness equipment
• Human motion capture and microsensor data fusion and motion parameter estimation
• Fusion and visualization of multi-sensors in public art design
• Application of digital image art based on sensing technology
• Data acquisition in physical learning space based on sensor
• Wireless teaching sensor integrated with data analysis
• Intelligent safety management of students promoted by sensors
• Teaching mode innovation of practice course supported by sensors