# Architectural Trends in Collaborative Computing: Approaches in the Internet of Everything Era
## 4 Results and Analysis
### Analysis and Findings
#### 4.3.2 Analysis by Computing  
Edge Computing emerges as a predominant architecture, with 50.0% representation in the sample, as shown in Figure 14 (right), in scenarios requiring low latency, real-time processing, and localized data analysis. The adoption of this architecture is particularly evident in applications such as intelligent manufacturing, smart construction, and autonomous vehicle systems within Smart Cities. These applications benefit from Edge Computing's ability to provide immediate data processing at or near the source of data generation, facilitating timely decision-making and interaction. Fog Computing, with its 41.7% representation, characterized by its intermediary computational layer between the Edge and the Cloud, is noted for enhancing efficiency in medical cyber-physical systems and IoT environments. It addresses the need for distributed processing, storage, and networking capabilities, enabling a balanced resource allocation and task scheduling approach. Cloud Computing, found in 25.0% of the articles, often in conjunction with Edge or Fog Computing, supports scalable and resource-intensive computations that are less time-sensitive. This architecture's utility is highlighted in contexts where extensive data storage and computational power are paramount, such as deep-learning-based services and large-scale IoT systems. Technologies related to Blockchain and Web Services, featured in 25.0% of the articles, point to an increasing emphasis on security, trust, and decentralized management within  
Figure 14: Analysis by collaboration (**left**) and by architecture/technology focus (**right**). collaborative computing environments. This trend is particularly relevant in cross-platform collaborations in IoT environments, where security, privacy, and interoperability are crucial. The review reveals that collaborative potential depends on the chosen architectural framework, reflecting the specific needs of IoT and IoE environments. The strong presence of Edge Computing, which prioritizes real-time interaction and decision-making, aligns with aspects of collaboration such as coordination and communication, particularly in time-critical applications like Smart Cities and intelligent manufacturing. The growing emphasis on security and trust, particularly with Blockchain [49; 50], highlights a shift towards cooperation between decentralized entities [41; 50]. This emphasis has significant implications for cross-platform IoT systems, where ensuring trust and security across distributed components is essential for effective collaboration. Our findings show a reliance on Edge [43; 44; 46; 47; 48], Cloud [41; 43; 44], and Fog [41; 43; 45; 52] Computing, introducing a more dynamic interpretation of coordination in IoT systems, where the flow of data and decision-making is distributed rather than centralized. This model supports real-time data processing at multiple points within the system, which can lead to increased autonomy and agility within collaborative networks. The role of Blockchain in enhancing cooperation suggests an evolution of the 3C model, where trust management becomes a core aspect of cooperation, particularly in decentralized systems. Smart City [48; 49] and smart application [42; 51] infrastructures within IoE environments suggest a growing overlap between communication and automation, where decision-making may increasingly rely on AI-driven algorithms. This AI integration requires rethinking collaborative dynamics, as autonomous systems may require new forms of interaction and accountability, reshaping how coordination is traditionally understood in IoE applications. These results highlight governments' need to invest in Edge infrastructures for Smart Cities, enabling more localized decision-making to improve public services such as traffic management and healthcare systems. The distributed nature of Edge and Fog Computing allows municipalities to allocate resources more effectively, reducing latency in critical services while maintaining robust data privacy standards.