# BDS: An Efficient Spark-Based Big Data Processing Scheme for Cloud Fog-IoT Orchestration
## 4 System Model and Methodology
### BDPS Data Processing in Cloud, Fog and IoT Network Layer  
We developed a high-speed data delivery mechanism to increase efficient data distribution at the IoT network end. The illustrated scheme enhances the data distribution pattern by working through in-memory Spark-RDD accelerator-based process schedulers across the fog-IoT network ends while performing distributed data sharing activities. The layered architecture is divided into three segments--cloud, fog, and IoT layers (e.g., end devices). The three-layer data distribution architecture (Figure 4) works in a bottom-up fashion. IoT network layers are the concerning part for major data loads to distribute toward the end devices. We contrived the in-memory Spark-RDD accelerator-based process schedulers (i.e., BDPS scheme) through a fog layer that works within the fog-IoT orchestration to reduce the data loads at the network end, specifically in the end devices. Here, the fog layer serves as a primary helper for the IoT and a secondary helper for the cloud end to overcome the extra data processing load of mesh networks over the IoTs by providing the utmost data delivery enhancement or service efficiency toward the end devices. The fog has Spark-RDD process schedulers (e.g., in-memory depth-first search accelerator) for the concerned IoTs or end devices. For data jamming, excess process overload in the IoT layer \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline \multirow{2}{*}{**Works**} & \multicolumn{6}{c}{**Key Technologies**} & \multicolumn{1}{c}{**Contributions and Research Gap**} \\ \cline{2-7}  & **Cloud** & **Fog** & **IoT** & **Map.R** & **Big.D** & \\ \hline [PERSON] et al. (2020) [2] & ✓ & ✓ & ✓ & X & X & Security issues declared in fog only services. \\ \hline [PERSON] et al. (2012) [4] & ✓ & X & X & X & X & Dynamic graph query module for NaaS Cloud. \\ \hline [PERSON] et al. (2018) [23] & X & X & ✓ & X & X & WSN platform for serving lower fogs. \\ \hline [PERSON] et al. (2020) [27] & X & X & ✓ & X & X & DoS security aware framework for WSANs. \\ \hline [PERSON] et al. (2021) [28] & X & X & X & ✓ & X & Application placement for map reduced services. \\ \hline [PERSON] et al. (2017) [29] & X & X & ✓ & ✓ & ✓ & Weighted Linear Regression for hadoop clusters. \\ \hline [PERSON] et al. (2020) [30] & X & ✓ & ✓ & X & X & TDMA task offload scheduler for fog-IoT nodes. \\ \hline [PERSON] et al. (2020) [31] & X & X & ✓ & X & X & Wireless message subscription model for IoT nodes. \\ \hline [PERSON] et al. (2020) [32] & X & X & ✓ & X & X & Scheduler problem in distributed IoT labeling. \\ \hline [PERSON] et al. (2022) [33] & ✓ & ✓ & ✓ & X & X & Inappropriate 3-tier interfacing for large data set. \\ \hline [PERSON] et al. (2020) [34] & ✓ & X & X & X & X & Distributed algorithmic access for cloud datacenter. \\ \hline [PERSON] et al. (2018) [36] & ✓ & ✓ & ✓ & X & ✓ & Only big data based task schedulers. \\ \hline [PERSON] et al. (2021) [37] & X & ✓ & ✓ & X & X & Dynamic IoT traffic model for fog analytics. \\ \hline [PERSON] et al. (2019) [38] & ✓ & ✓ & ✓ & X & X & Shortest paths algorithms for road networks. \\ \hline [PERSON] et al. (2021) [39] & ✓ & ✓ & ✓ & X & X & Data deliverable services and shared architectures. \\ \hline [PERSON] et al. (2021) [40] & ✓ & X & ✓ & X & ✓ & Facing trouble in middle ware latency support. \\ \hline [PERSON] et al. (2020) [41] & ✓ & ✓ & ✓ & X & X & Gap in middleware feasibilities. \\ \hline [PERSON] et al. (2021) [46] & ✓ & ✓ & ✓ & X & X & Gaps in between Proposed and ES scheme. \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Emerging key technologies, contributions and research gaps. end, the fog carrier schedules the in-memory Spark-RDD accelerator toward the fog-IoT gateway connection and sends back the information to the cloud end for a process log list and further possible in-memory depth-first search (i.e., DFS) schedule verification. The fog layer has a SP (e.g., session password) timestamp to shut off the BDPS process to run after the regular processed data loads.