## 2 6G Convergence with Red AI Perspective in Terrestrial and Non-Terrestrial Applications
### Application 1: Red-AI-Enabled 6G Virtual Emotion Barrier  
Definition: Red-AI-Enabled 6G Virtual Emotion Barrier, referred to as _RedAI-6G-VEmoBAR_, is a barrier with a _Red AI_ view that is able to detect human emotion types such as joy, pleasure, neutrality, sadness, fury and concentrates on accuracy and speed in a 6G environment. Given that a set of heterogeneous devices including UAVs, mobile robots, smart devices equipped with wireless transmitters and reflection receivers in a 6G-supported square-shaped area, _RedAI-6G-VEmoBAR_ detects the specific emotion type of any person passing through a given field. System GoalAs an ultimate system goal and maintenance, _RedAI-6G-VEmoBAR_ aims to increase system performance and then eventually provide virtual-emotion-based AI services to residents in smart cities. The first objective is to maximize system lifetime by building multiple _RedAI-6G-VEmoBAR_ instead of single barrier construction. The second aim is maximizing emotion detection accuracy. Furthermore, the third objective is to concentrate on minimizing recovery delay from failure of barriers regardless of energy consumption of devices. OperationAccording to [12], the system assumes that human emotions such as joy, pleasure, neutral, sadness, and fury can be detected through wireless signals, their reflections, camera sensor, temperature sensor, heartbeat segmentation, derivation procedures with heartbeat segmentation and respiration signals. Furthermore, we can consider that several group of devices including a fleet of UAVs, and a group of mobile robots, smart devices have random locations initially where UAVs are randomly deployed in the air as well as mobile robots and smart devices that are active on the ground. According to service type and purpose, the formed barriers detect the heartbeat segmentation, respiration signal and body temperature through wireless signal and its reflection, and sensors. It follows that the virtual emotion information and the feature for epidemic prevention are derived. That is, the specific derived information can be focused differently. For example, if the surveillance and patrol service based on virtual emotion information are required for specific region, the proposed barrier system focuses on the detection of fury emotion type in order to prevent potential criminal and terror threat. Furthermore, an _Integrated Aerial-Ground Station_, referred to as _IAGS_, is installed in 6G convergent infrastructure to recharge UAVs, mobile robots, autonomous vehicles and smart devices during staying _IAGS_. Because sleep-wake scheduling can be applied alternately among multiple barriers, the multiple numbers of _RedAI-6G-VEmoBAR_ are formed by heterogeneous devices to maximize system life. Then, the detected virtual emotion information through _RedAI-6G-VEmoBAR_ is stored. Subsequently, the large amount of accumulated data is sent to other entities near devices or the closest edge-AI agent with a large amount of data implementing deep learning so that AI-assisted virtual emotion services can be provided properly. It is noted that the virtual emotion barrier is composed of several system devices using wireless signal, and the accuracy of detection through wireless signal and its reflection depends on the distance between device and person. So, as one of the metrics for accuracy in virtual emotion barrier, the accumulated accuracy estimated by each pair between each device in the barrier can be considered. Hence, the barrier with high accumulated accuracy by devices is chosen from multiple barriers with priority so that the system increases the accumulative accuracy as a whole in the given area with a view of _Red AI_. Furthermore, after constructing multiple _RedAI-6G-VEmoBAR_, the mobile system components of smart devices, UAVs, mobile robots may move to other locations or may have failures so thatthose failures will cause weak detection points and holes in _RedAI-6G-VEmoBAR_.