# From Cyber-Physical Convergence to Digital Twins: A Review on Edge Computing Use Case Designs
## 1 Introduction  
The introduction of fifth generation (5G) networks was a response to problems related to the exponential growth of mobile data from the new generation of wireless services. This enormous growth of mobile data was envisioned to overwhelm the network in terms of its ability to provide resources to support data-generating application with high flexibility [1]. Since provisioning of resources with high flexibility requires extensive upgrades to the technologies existing in the current network infrastructure, this presents itself as a huge challenge to a lot of mobile network operators (MNOs) [2]. The sixth generation (6G) of wireless communication has high expectations in terms of improved quality of service (QoS), i.e., providing network coverage, minimum latency, cost-effective deployments, and low energy consumption, as well as high fidelity [3]. This QoS can be further improved by incorporating proper resource management techniques through artificial intelligence (AI) and machine learning (ML) procedures. Resource management in 6G networks and beyond is envisioned to require massive big data analytics for knowledge discovery in order to achieve high-level intelligence in terms of decision making and on-demand service provisioning. Due to this postulation, this review investigates ways of exploiting the cyber-physical convergence to realize the digital twin (DT) technology, with specific focus on deriving DT technologies specifically tailored for 6G networks and beyond.  
The expansive and sporadic growth of mobile data applications suggests that the 6G wireless landscape will be characterized by plug-and-play deployments driven by massiveInternet of Things (mIoT) [4]. These deployments are all driven by the desire for better user experience, which will be achieved through higher QoS attainment. As the surge towards massive IoT continues to gain momentum, wireless network deployments are beginning to manifest in the following forms: (i) the login time, (ii) the logging subscriber, (iii) the location at which the login activity occurred, and (iv) the application that was used. The volume of network traffic received at each wireless access point (AP) is increasing, and in order to keep up with this increase, the computational capacity of the APs and other related infrastructure at the network edge require urgent improvement. Bandwidth demand is increasing daily, and it reaches an all-time high with each passing day due to the rapid proliferation of data-intensive applications and services, which contribute to overloading the frequency bands and the inefficiency of the current spectrum management techniques [5]. This situation presents network designers and performance analysts with a host of new challenges. The most pervasive problem is that the required transmission speeds tend to be so high that the ratio of propagation delay to packet transmission may become significantly greater than unity [6]. However, with the introduction of AI strategies into wireless communication, solutions for most of the pervasive challenges and problems can be attained.