## 4 6G and Dependability  
### Brief Introduction to 6G Networks  
The rapid development of multimedia applications for use cases such as high-fidelity holograms, tactile Internet, and the support of MCAs require a higher bandwidth, lower latency, and higher reliability than that offered by the current 5G communication networks [40; 41]. Therefore, 6G aims to fulfill these requirements through base-station densification (mmWave and terahertz tiny cells, temporary hotspots) with other means for distribution of network functions, such as extended edge computing, and exploration of higher frequencies above 300 GHz, as discussed in [1]. The resulting 6G networks, thus,will be expected to provide more than just communications, i.e., to interconnect communication, computing, and sensing technologies with the physical, biological, and cyber worlds, thereby acting as distributed neural networks that will enable intelligence of everything. Sixth-generation networks will transform the way we communicate, from connected people and devices to connected intelligence. This means bringing intelligence closer to every person, home, or business, for example, in the form of edge intelligence. Therefore, 6G networks are bound to be large-scale, use heterogeneous access with cell-free or cell-less coverage, and dynamic with heavily-distributed storage and computation capabilities [42]. The transition from 5G to 6G requires changes not only in bandwidth, but from the physical to the application layer in order to meet the new requirements of emerging services, such as the Internet of Everything (IoE) [43]. Furthermore, 6G networks are expected to achieve data rates in the range of terabits per second, thanks to the developments in terahertz communications; and improvements in massive MIMO, beamforming, and novel coding schemes. A successful combination of these next-generation wireless networks with cloud, edge, and fog platforms is vital in order to realize increased network intelligence. To meet the requirements of latency and privacy, bringing cloud platforms closer to the sources of data, for instance, in the far edge, will be inevitable. Edge intelligence [28; 44] thus opens up new horizons for achieving and exploiting the full potential of 6G networks. Since the first generation (1G), the complexity of communication networks have increased while expanding both horizontally and vertically, thereby rendering them difficult to manage. Furthermore, along with complexity, the security threat landscape has also increased constantly [22]. Edge computing can play an important role in addressing both of these challenges, i.e., complexity and security. By devolving control into multiple control units, compared to centralized ones, security through redundancy can be increased as a general phenomenon. For instance, the chances of single points of failure, and of a single target for denial of service (DoS) and resource exhaustion attacks, are highly complicated in such distributed environments. Furthermore, edge computing plays a vital role in 6G communication networks, as it provides the computing the resources necessary for carrying out management and analysis close to end-users' devices [45]. Fast and focused data processing through edge computing is the cornerstone of applications in 6G, for example, in vehicle-to-everything communications [46]. In-depth data analysis could be carried out by the centralized cloud at the expense of delays [47]. Figure 1 shows a simplified architecture of an AI-based 6G network, which is divided into three parts: user environment, networks, and management and control. In the management and control, functions such as parameter optimization, resource management, and task scheduling are carried out. In the network part, some of the tasks performed are data filtering, knowledge discovery, and feature extraction for data analytics, besides the usual network layers' work. Finally, in the user's environment, all the sensing, monitoring, and data collection occurs.