# From Cyber-Physical Convergence to Digital Twins: A Review on Edge Computing Use Case Designs
## 1 Introduction
### Research Motivation  
Through the integration of AI and big data analytics into the core of wireless devices and infrastructure, the IoT has completely transformed from the Cognitive IoT (CIoT) to the Cognitive Internet of People Processes Data & Things (CIoPPD&T). However, there are still aspects that need to be addressed in terms of (i) network maturity and diversity and (ii) network flexibility towards digital transformation. * Network Maturity and Diversity: The on-going deployment of 5G networks is showing that wireless communication has greatly matured from the previous two generations in terms of coverage and offered services. Parallel to maturity is the level of network diversity that has been reached, which comes from different perspectives. * From the technology perspective, millimeter wave (mmWave) communications and the network backhaul technology are the main aspects that determine network maturity in the evolution towards 6G. There are actually three significant classes of this technological perspective, defined according to use case situations: (i) extreme/enhanced mobile broadband (eMBB), (ii) ultra-reliable low-latency communication (URLLC), and (iii) massive machine-type communication (mMTC). These are network augmentations, among which mixes of utilization situations will revolve to quickly prosper 6G networks into realization in no time [7]. * From the element management system (EMS) and business perspectives, the relevant aspects are business models, such as (i) ecosystem maturity, (ii) coordination of industry verticals, and (iii) the regulation aspects, including those related to spectrum management and fragmentation. * From the network intelligence perspective, the determination towards realizing 6G networks can be viewed as a dispersed neural system that connects (i) the physical, (ii) the cyber, as well as (iii) the biological universes, which genuinely introduces a period wherein all network operations will be recognized as linked and smart [3]. These perspectives of network maturity and diversity have set a solid foundation for all things intelligent. * Network Flexibility Towards Digital Transformation: With the introduction of some underlying technologies through AI, digital transformation is becoming a reality. The whole idea of digital transformation is the orchestration of network automation technologies to make the design and maintenance processes comprehensible and more natural to apply [8]. Further entrenchment of big data through the increase in digitally available data means that new methodologies to formulate and understand the transient behavior of network systems need to be developed. That is why, as network data operations continue to soar, network data analytics need to be incorporated into the operations of wireless networks, more especially the network components that are pushing significantly large amounts of multimedia traffic to the internet [4]. Therefore, effective ways to improve the computational capabilities of the relevant network infrastructure will bring the necessary capacity to meet the unprecedented computational demands of future network users. The current network design and control methods based on deep neural network (DNN) architectures are not enough for 5G problems, hence they may not be adequate for the \"tsunami\" of use cases and multi-platform environments brought by 6G networks. To this effect, when it comes to network big data, network infrastructure should become a major priority among enterprise executives. Despite the popularity of AI strategies with diverse capabilities that have improved connectivity and network flexibility for the dynamic virtual environments, rapid scalability is needed to handle the intermittent nature of big data loads [9]. Towards realization of the DT technology in wireless networks, it is believed that the incorporation of AI strategies in network infrastructure can improve their operational efficiency [10].