# A Survey of Deep Learning for Data Caching in Edge Network
## 5 Research Challenges and Future Directions
### Caching for Mobile Augmented Reality (MAR) Applications and Digital Twins (DTs)  
Mobile augmented reality (MAR) applications can be considered as a way to augment the physical real-world environment with artificial computer-based generated information and is an area that has received significant research attention recently. In order to successfully superimpose different digital object in the physical world, MAR applications include several computationally and storage complex concepts, such as image recognition, mobile camera calibration, and also the use of advanced 2D and 3D graphics rendering. These functionalities are highly computationally intensive and as such require support from an edge cloud, in addition the virtual objects to be embedded in the physical world are expected to be proactively cached closer to the end user so that latency is minimized. Ultra low latency in these type of applications is of paramount importance so that to provide a photorealistic embedding of virtual objects in the video view of the end user. However, since computational and augmented reality objects need to be readily available, the caching of those objects should be considered in conjunction with the computational capabilities of the edge cloud. In addition to the above, when MAR is considered under the lenses of an NFV environment, the application might inherently require access to some VNFs; therefore, the above discussion on VNF chaining for MAR applications is also valid, in this case.  
Recently, the concept of Digital Twin (DT) [103; 104] has received significant research attention due to the plethora of applications ranging from industrial manufacturing and health to smart cities. In a nutshell, a DT can be defined as an accurate digital replica of a real world object across multiple granularity levels; and this real world object could be a machine, a robot or an industrial process or (sub) system. By reflecting the physical status of the system under consideration in a virtual space open up a plethora of optimization, prediction, fault tolerance and automation process that cannot be done using solely the physical object. At the core of DT applications is the requirement of stringent two-way real time communication between the digital replica and the physical object. This requirement inevitably requires support from edge clouds to minimize latency and efficient storage and computational resources, including caching. In that setting, the use of the aforementioned deep learning technologies will have a key role to play in order to provide high quality real time decision making to avoid misalignment between the digital replica of the physical object under consideration. Efficient machine-to-DT connectivity would require capabilities similar to the above mentioned augmented reality application, but, due to the continuous real-time control-loop operation, DTs will require a completely new set of network optimization capabilities, and, in that frontier, efficient caching and data-driven techniques will have a central role to play. Hence, as the research regarding the inter-play between low latency communications and DTs is still in the embryonic stage, there is significant scope in the investigation of suitable data-driven deep learning techniques to be utilized for distributed allocation of caching and computing resources.