## 6 Dependability for MCAs in 6G
### MCA Use Cases  
This section will focus on briefly introducing and surveying three specific use cases that are in high demand for automated solutions: telesurgery, autonomous driving, and augmented reality. Considering an emergent surgical system, telesurgery is the use of wireless networking and robotics, which allows surgeons to operate on patients located far away. Among its benefits, we can mention the capacity to offer surgery in underserved remote locations and enabling collaboration between surgeons from different medical centers. The most important requirement for telesurgery systems is latency. In [69], the authors determined the ideal latency for telesurgery systems to be 100 ms or less, while presenting a feasibility study for trans-sphenoidal resection of a pituitary tumor, where latency of 10 ms was achieved. The work in [70] presents a drone-assisted telesurgery system that makes use of blockchain and 6G networks to become trusted and ultra-responsive. The authors favored an analysis of performance for the AI techniques used to classify diseases, and a cost analysis for the blockchain; dependability was not studied in this case. In [71], the authors introduced a 6G blockchain-based scheme for telesurgery that aims at being intelligent and efficient from the latency, throughput, and storage points of view. Although dependability is not directly mentioned, the authors did study the security and safety of the framework, and the reliability of the underlying 6G network. No availability analysis was performed. The importance of security, a dimension of dependability, in remote healthcare enabled by existing and future communication networks, such as 5G and future 6G, was thoroughly studied in [72]. The authors conclude that without proper security in place, remote healthcare will be detrimental rather than beneficial. This makes the dependability of communications networks highly important for such a critical use case. Dependability must not be considered as an add-on, but used as a benchmark from the basic initial design stage, which should be revisited during the working stages and must be continuously improved. Autonomous driving technology refers to self-driving cars which are capable of sensing the environment and safely moving without human intervention [46]. Self-driving cars' set-ups are quite complex, usually comprising cameras, laser scanners, radars, laser beams, and an inertial measurement unit. Furthermore, autonomous driving represents the convergence of intelligent wireless sensing, communication, computing, and caching [46; 73]. In this work we focus on EdgeAI, which represents the computation and communication parts of this use case. EdgeAI allows self-driving cars to accurately sense their surroundings and timely react using techniques for data offloading from the vehicles to the edge servers. In [74], the authors introduced a framework for EdgeAI-powered autonomous driving that achieves near-real-time task offloading while preserving privacy, and reducing communication delay. Additionally, reliability was explored as the inference accuracy. Security was enhanced through local training, and safety was analyzed as a crucial element that depends on the offloading and inferring time window and feasible sensing. The work in [75] satisfies the QoS requirements of future vehicular networks through the use of idle resources from parked cars. The authors also successfully simulated user density as a way to predict resource availability and demonstrated a reduction in deployment costs. However, the paper does not discuss dependability or any of its components. Other techniques, such as game theoretic approaches, can be used in improving security, where contradictions in interest, among actors, exist, as discussed in [76; 77]. The works in [78; 79; 80; 81] investigate the improvement of dependability in vehicular networks; however, none of them clearly defines nor exposes all the factors that affect dependability.