## 2 Literature Review
### Types of Edge Computing  
A 2017 study compared edge technologies regarding their computing, cashing, and communication convergence [19]. The potential benefits of mobile edge computing (MEC) providing cloud servers at base stations are short delays, high bandwidth, context awareness, and real-time services. Fog computing uses near-to-user edge devices as edge routers for computing and cashing. It has the same benefits as MEC. Cloudlets are another edge computing-based technology that is only one step from the user. It is self-configuring and energy efficient. These technologies are compared in Table 1. The differences and common features of fog, cloudlet, and MEC technologies are shown in Figure 3[20]. Figure 3: Comparing edge computing technologies. Figure 2: Cloud implementation challenges. Fog computing is an emerging technology to address computing and networking bottlenecks in the large-scale deployment of IoT applications. It is a promising complementary computing paradigm to cloud computing where computational, networking, storage, and acceleration elements are deployed at the edge and network layers in a multi-tier, distributed, and possibly cooperative manner [21]. Internode cooperation, context awareness, the ability to have more than one layer, content awareness, and short delays are the main features of fog computing that make it a suitable choice for healthcare systems. Fog computing is a process that brings some of the cloud processing structures to the network edge. It adds new features to the network, including shorter delays due to processing data at the edge of the network (2018). In 2016, [PERSON] et al. described the general structure of fog computing [22]. As shown in Figure 4, fog-cloud communication manages fog with the cloud; exchanges information bilaterally, when necessary; provides required services to each other; and offers end-to-end services. The fog nodes communicate with each other to support applications, share data, and collaborate for computing, storage, or backup. The fog-user interaction is necessary for presentations and data transfer. Establishing communication every time with the cloud is not required with the introduction of fog, and thus, the latency is reduced. Healthcare is a latency-sensitive application area. Therefore, the deployment of fog computing in this area is of vital importance. Proper analytics and research may lead to better care, improved treatment, and enhanced patient satisfaction [23]. Fog computing benefits healthcare by enhancing services. A proposed platform combines fog and cloud technologies for medical data collection, automatic medication prescription, and robotic medication delivery [24]. As shown in Figure 5, the platform consists of three parts: (1) medical data collecting, (2) automatic medication prescription, and (3) robotic medication delivery. \begin{table}
\begin{tabular}{c c c c} \hline  & **MEC** & **Fog Computing** & **Cloudlet** \\ \hline Introduced by & ETSi & Cisco & Prof. [PERSON] \\ \hline Layers count & 3 Layers & 3 Layers or more & 3 Layers \\ \hline Delay & Low & Low & Low \\ \hline Owner & Mobile Operators & Distributed Fog Owners & Local Businesses \\ \hline Share rate & Medium & Little & Little \\ \hline Content Awareness & Yes & Yes & Probably \\ \hline Node Cooperation & No & Yes & No \\ \hline \end{tabular}
\end{table} 
Table 1: Comparing edge computing technologies. Figure 4: Fog three-layer structure. During the fog implementation and operation phases, the gateways are adjacent to the IoT domain and accessible through short-range radio communications such as Bluetooth. The cloud is further away and accessible through a wide area network (WAN). It provides the needs of healthcare systems through the platform as a service (PaaS) and infrastructure as a service (IaaS). Fog computing has changed the quality, accuracy, and speed of healthcare services. A fog-based approach to improve the IoT structure for healthcare was presented in 2017 [25].