## 1 Introduction  
Fifth generation (5G) wireless and mobile systems have attracted long-standing interest from researchers owing to the clear advantages of 5G new radio milestone, realization of various high-frequency services, and security [1, 2, 3]. However, because of the massive data-intensive applications including augmented reality, brain interface, and remote surgery in future smart cities, the latency and data rate requirements of those promising applications should be beyond that of current 5G technologies. To overcome the inherent restrictions and drawbacks of 5G, the 6G (sixth generation) system is emerging as a key enabler with higher rates, massive antennas, expanded AI operations, routing algorithms, distributed structures, integrated terrestrial, and airborne features [4, 5, 6, 7, 8]. Specifically, in relation to 5G and 6G technologies, the terrestrial, non-terrestrial, space-air-terrestrial networks are attracting much interest from researchers [9, 10]. The remarkable concept of _virtual emotion_ was originally proposed by [11]. Through smart devices equipped with wireless transmitters and receivers, the virtual emotion is detected by wireless signals and their reflections, and derivation procedures including heartbeat segmentation and respiration procedure by [12]. Then, the _virtual emotion_ can be sent to other entities in the form of manipulatable data throughout AI-supported systems. Thanks to its promising applicabilities with various emotion-based services in smart cities, _virtual emotion_ is attracting much interest and is continuously expanding its applicable research areas including 5G, security, UAVs (Unmanned Aerial Vehicles), IoT (Internet of Things) devices, epidemic prevention, etc. [13, 14]. On the other hand, it is vital to develop these advanced systems for epidemic prevention in order to protect the wellbeing of citizens. Furthermore, it is verified that prompt epidemic prevention is indispensable to restrict unprecedented epidemic spread. Subsequently, we can consider that the utilization of autonomous UAVs, mobile robots and smart devices based on AI-supported 6G communications will play a role in preventing potential disease outbreak and spread because AI-enabled 6G infrastructure will motivate a massive amount of detected data and manipulated information by smart devices to be rapidly communicated as well as perform appropriately [15; 16; 17; 18; 19; 20; 21; 22]. Based on the motivation for epidemic prevention, there arises the issue of how to utilize the massive number of heterogeneous system components in a careful manner. Moreover, it is highly anticipated that the AI technologies including deep learning and machine learning will be driven by a wide range of innovative applications and systems such as 5G, edge computing, cloud, Internet of Things (IoT), tactile internet, wireless networks, vehicles, blockchain, etc. [23; 24; 25; 26; 27; 28; 29; 30; 31; 32; 33; 34]. Furthermore, several standpoints are studied regarding AI technology and its applicability. In particular, the _Red AI_ and the _Green AI_ were defined by [35] recently. The _Red AI_ comprises existing AI techniques and approaches which covers accuracy improvement, process speed, and minimum delay to perform specific missions and system goals as main metrics or factors. On the contrary, with another AI-enabled approach, _Green AI_ focuses on the economic, environmental and social costs of satisfying system requirements or task completions as evaluation metrics or standards. Figure 1 shows a brief comparison between _Red AI_ and _Green AI_ with their metrics, measures and used terms. In short, _Green AI_ focuses on efficiency and reduction in cost while _Red AI_ pursues improvement of accuracy. Subsequently, toward balanced realization for AI-enabled applications, the issue of how to conduct AI-assisted virtual emotion and epidemic prevention infrastructures consisting of heterogeneous components has to be critically addressed.