## 2 Background on Computing Paradigms
### Differences and Similarities of Paradigms  
The main goal of Fog and Edge paradigms are similar in some areas, unlike the Cloud. Both of those bring the capabilities of the Cloud closer to the users and offer customers with lower latency services while making sure, on the one hand, that highly delay-tolerant applications would achieve the required QoS, and, on the other hand, lowering the overall network load [47]. It is not straightforward to differentiate and compare Cloud, Edge, and Fog Computing. This subsection attempts to discern and look into similar features between the computing paradigms [48]. The differences and similarities of the various paradigms are summarized in Table 1.  
Nonetheless, it is essential to overview each of these indicated paradigms to address security and privacy aspects in Cloud, Edge, and Fog paradigms. This subsection described some fundamental features that constitute each of the said paradigms, making them unique in their ways. We looked into the different architectures, how these paradigms are \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Attributes** & **Cloud Computing** & **Edge Computing** & **Fog Computing** \\ \hline Architecture & Centralized & Distributed & Distributed \\ \hline Expected Task Execution Time 1 & High & High-Medium & Low \\ \hline Provided Services & Universal services & Often uses mobile networks & Vital for a particular domain and distributed \\ \hline Security & Centralized (guaranteed by the Cloud provider) & Centralized (guaranteed by the Cellular operator) & Mixed (depending on the implementation) \\ \hline Energy Consumption & High & Low & Varying but higher than for Edge \\ \hline Identifying location & No & Yes & Yes \\ \hline Main Providers & Amazon and Google & Cellular network providers & Proprietary \\ \hline Mobility & Inadequate & Offered with limited support & Supported \\ \hline Interaction in Real-Time & Available & Available & Available \\ \hline Latency & High & Low & Varying but higher than for Edge \\ \hline Bandwidth Cost & High & Low & Low \\ \hline Storage capacity and Computation & High & Very limited & Varying \\ \hline Scalability & Average & High & High \\ \hline Overall usage & \begin{tabular}{c} Computation distribution for \\ huge data (Google MapReduce), \\ Apps virtualization, Storage of \\ data scalability \\ \end{tabular} & \
\begin{tabular}{c} Control of traffic, data caching, \\ wearable applications \\ \end{tabular} &
\begin{tabular}{c} CCTV surveillance, imaging of \\ subsurface in real-time, IoT, \\ Smart city, Vehicle-to-Vehicle \\ (V2X) \\ \end{tabular} \\ \hline \hline \end{tabular}  
* Importantly, Edge may provide higher results but only for computationally simple tasks (benefiting in terms of communication latency), while Fog would provide higher computational speed maintaining the latency (for, e.g., AR/VR applications). Executions in the Cloud would always provide the worst results as the computational unit is geographically distant from the user, which would naturally require tremendous communication overheads compared to geographically closer locations.  
\end{table} 
Table 1: Comparison on different computing paradigms. characterized and how beneficial they are to the industries, and addressed some scenarios in which they are applied. Cloud being a centralized architecture and an IoT promoter has several shortcomings such as high latency, location sensibility, and computation time, just to name a few. Researchers then suggested upgraded technologies known as Edge and Fog paradigms to lessen the burden on Cloud systems and resolve the issues indicated. Ultimately, we see that those two paradigms have helped decrease the large quantity of data sent to the Cloud. Finally, the Edge paradigm is advantageous over the Cloud paradigm, especially regarding security and privacy. However, the Fog paradigm consisting of Fog nodes is regarded as an outstanding architecture uniquely created so that IoT appliances render improved services and support.