# Architectural Trends in Collaborative Computing: Approaches in the Internet of Everything Era
## 4 Results and Analysis
### Synthesis of Results  
[PERSON] et al. (2019) discuss the future of Edge Computing (EC) in the context of Industry 4.0, specifically in intelligent manufacturing and smart factories. They survey recent EC applications in Industry 4.0, focusing on processing and analyzing latency-critical data for security and privacy objectives. From this perspective, EC enables collaborative data processing directly on Edge and network devices, pushing computation down to Edge devices. This collaborative processing can improve operational efficiency, storage, and latency; reduce costs; and improve productivity with higher-quality maintenance and customer satisfaction. Additionally, they note that EC can support real-time asset and process monitoring, digital quality management, data analysis, and control, which can help improve the overall performance of the manufacturing system. They provide a taxonomy that organizes Edge Computing in intelligent manufacturing by Architectures, Intelligence Platforms, Edge Objectives, and Potential Applications, as shown in Figure 3. Architectures are classified into general hierarchical (field, Edge, Cloud layers), application-specific (customized components for specific needs), and standard architectures (reference models from industry collaborations). Intelligence platforms are divided into Edge-only, Cloud-only, and Hybrid, based on the environments used for training and inferencing. Edge Objectives include real-time performance, computational offloading, efficient deployment strategies, and enhanced security and privacy. Finally, their potential applications include machine fault diagnosis, process monitoring, and prognosis. Therefore, in this context, the type of collaboration that best encompasses these features is \"Coordination\" because it highlights how EC facilitates coordination between various stages of the Physical-Digital-Physical \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Ref.**} & \multicolumn{4}{c}{**Collaboration Types**} & \multicolumn{4}{c}{**Computing Architectures**} \\ \cline{2-9}  & **Communication** & **Coordination** & **Cooperation** & **Edge** & **Cloud** & **Fog** &
\begin{tabular}{c} **Web Services,** \\ **Blockchain** \\ \end{tabular} & **Application** \\ \hline
[41] & 1 & 10, 11 & & ✓ & ✓ & & Intrusion-Detection \\
[42] & & 7, 8, 10, 11 & ✓ & & & & Intelligent Manufacturing \\
[43] & & & 12, 13 & ✓ & ✓ & ✓ & & Internet of Things (IoT) \\
[44] & 1 & 10, 11 & & ✓ & ✓ & & & Deep-Learning-based services \\
[45] & \(1,2,3\) & 10, 11 & & & ✓ & ✓ & & Medical Cyber–Physical System \\
[46] & & 7, 8, 9, 10, 11 & & ✓ & & & ✓ & Software-Defined Edge Computing \\
[47] & & 7, 8, 9, 10, 11 & & ✓ & ✓ & & & Cloud–Edge collaborative system \\
[48] & 1, 2 & 10, 11 & 12, 13, 14, 15 & ✓ & & & & Smart Cities \\
[49] & 1, 2 & 9, 10 & 13, 15 & & ✓ & & ✓ & Smart-Everything City \\
[50] & & & 12, 13, 14, 15 & & & ✓ & & Cross-platform collaboration \\
[51] & \(1,3,5,6\) & & & ✓ & & & & Smart Construction \\
[52] & & & 7, 8, 10, 11 & & ✓ & ✓ & & Fog Computing challenges \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Collaborative computing architecture in articles. loop and different components in intelligent manufacturing. The following collaboration aspects are extracted from [PERSON] et al. [42]:  
**Order:**: Facilitates structured data flow and decision-making through low latency and near real-time performance. **Roles:**: Allows for the seamless integration of various industrial components, thereby establishing roles within the system. **Efficiency:**: Low latency and reduced Cloud overhead contribute to efficient operation. **Network:**: Discusses \"collaborative Edge scenarios\" that emphasize building a collaborative network. [PERSON] et al. [43] give a comprehensive overview of the Edge/Fog/Cloud Computing paradigm, including requirements and challenges, in the Internet of Things. They also cover the role of privacy preservation, distributed machine learning, and collaborative analytics to harvest the data deluge created by IoT devices.