# An Analytical Survey of WSNs Integration with Cloud and Fog Computing
## 3 Related Work  
In this section, we extensively review the related works on Fog computing and cloud interaction. All the related studies are about Fog and cloud data accessibility, data storage, safety, connectivity, and location awareness and all these features are stated in Table 1. All \begin{table}
\begin{tabular}{l l l} \hline \hline
**Requirement** & **Cloud Computing** & **Fog Computing** \\ \hline Latency & High & Low \\ \hline Connectivity & Dedicated line & Wireless \\ \hline Service location & In the Internet & Edge of the network \\ \hline Hops between server and client & Multiple & One \\ \hline attacks on en-route data & Higher probability & Very low probability \\ \hline Location awareness & No & Yes \\ \hline Mobility & Limited & Fully supported \\ \hline Interaction for real-time & Supported & Supported \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Requirements for cloud and FC. these are the Fog and cloud computing requirements. By considering all these requirements we enlisted only those frameworks that suggested according to such requirements. Fog structure is a three-layer system in which a different portable sink is proposed. The system comprises a sink layer, a fog layer, and a directing layer. In the sink layer, every sink position is utilized as a generator to get the Voronoi graph. At that point, in the fog layer, the sinks go as fog nodes, and each sink coordinates [31; 32]. The execution of Fog-to-Cloud structures demonstrates the advantages of organizing the diverse limits brought by the two devices at the cloud and the edge. With that in mind, the paper centers on the DSE issue. Two techniques; First-Fit and Random-Fit are also proposed for resource allocation [33; 34]. A service delay minimizes the use of IoT fog-cloud applications. To overcome this issue a study proposed a framework [35; 36]. A fog IFCIoT framework that guarantees expanded execution, vitality, productivity, and versatility, for IoT and CPS applications has been proposed in [37; 38]. This work discussed the objectives and difficulties of the fog platform. They have presented the strategies and execution of a prototyping stage for fog processing. Finally, they have assessed their prototyping phase in Smart Home applications [39; 40]. In a cloud-based framework, face identification and resolution to access personal information or data are proposed. At that point, a parallel coordinating system and distributed computing-based determination structure are proposed to effectively resolute confronted persons, control individual information, and secure a person's data [41; 42]. A FOGG framework is developed for sensor network integration with the internet in [43; 44]. In this framework, the authors used a dedicated device to work as an IoT gateway with additional services, security controller functionality, and protocol translation. ICN security feature is used in FOGG to provide data-centric instead of communication. In [45], the author proposed a new Stable Election Protocol (N-SEP) that elects the best cluster head based on distance from the base station, heterogeneity ratio, and energy consumption. This framework provides 50% and 25% stability to LEACH and SEP, respectively, in terms of network lifetime and energy efficiency [46]. WSN framework empowered for localization and activity monitoring in the context of Ambient Assisted Living AAL for real-time activity through the edge mining approach. For cloud base analysis, the genetic algorithm is used. The proposed system achieved high positional accuracy and low frequency of data computation on sensor devices [47; 48]. In Fog-based IoT applications, a Distributed Dataflow programming model is introduced in this paper [49]. They examine the center prerequisites, that is, Fog-based IoT applications distinguish various problems with existing ways to deal with Fog-based application improvement.