# Architectural Trends in Collaborative Computing: Approaches in the Internet of Everything Era
## 5 Discussions  
This work highlights the relevance of collaboration across key technology areas, including Industry 4.0, IoT, and Blockchain-based services. Successful collaboration depends on strategies specifically designed to meet the unique demands of each context. Technological environments differ in their characteristics, making using flexible, customizable approaches crucial. Standardized methods are often ineffective because they fail to address these environments' complexity and dynamic nature. Collaborative architectures are essential for enabling interaction, maximizing resource efficiency, and significantly improving system performance. As technology continues to evolve, the essence of collaboration emerges as a key element in unlocking the full potential of computing systems. This is crucial as emerging technologies demand more integrated and sophisticated systems where stakeholders must work harmoniously. Tables 4 and 5 offer a comprehensive view of collaboration and their interplay with collaborative computing architectures across various contexts. The insights are valuable for understanding the role of collaboration in the success of advanced technologies. Our findings show that successful collaboration in computing environments relies not solely on technical factors but is also heavily dependent on effective communication, coordination, and cooperation among various stakeholders. Clear communication, efficient resource management, and inclusive participation have emerged as central themes, each playing an indispensable role in the diverse applications of these cutting-edge technologies. The taxonomies of Intrusion-Detection Systems [41] and Edge Computing in intelligent manufacturing [42] offer detailed, domain-specific frameworks compared to the broader, generalized taxonomy of IoE [14]. The Intrusion-Detection System taxonomy focuses on categorizing detection methods (trust-based, mathematics-driven, data-driven) and highlights the use of advanced techniques like Blockchain and smart contracts to enhance security and reliability through diverse architectures (centralized, distributed, and federated) [41]. In contrast, the taxonomy for Edge Computing in intelligent manufacturing prioritizes process optimization in manufacturing, emphasizing architecture, intelligence platforms (Edge, Cloud, hybrid), and practical objectives such as computational offloading, real-time analysis, and fault diagnosis [42]. While both frameworks are specialized, aiming to solve particular challenges in cybersecurity and industrial efficiency, the IoE taxonomy [14] adopts a comprehensive approach, organizing knowledge, observation, and capability to provide a broader categorization of diverse IoT data and its potential applications across multiple domains. The provided architectures showcase various approaches to integrating Edge, Fog, and Cloud layers, highlighting different aspects of hierarchical data processing, computational distribution, and decision-making frameworks. Firstly, the classic hierarchical model (seen in [43; 45]) emphasizes a top-down approach where raw data are generated at the Edge, processed in the Fog layer, and ultimately integrated into the Cloud. This setup supports scalability, with the Cloud handling heavy data analytics while Edge nodes focus on real-time data collection. The Fog layer acts as an intermediary, reducing latency by processing data closer to the source, which is ideal for time-sensitive applications like IoT. [PERSON] et al. [44] and [PERSON] et al. [47] focus on a simplified Edge architecture, showcasing a direct connection between the Edge layer and the Cloud, bypassing the Fog layer. In this setup, Edge servers handle critical tasks locally and offload only complex processing to the Cloud when necessary. This architecture is efficient for scenarios requiring low latency and immediate processing, such as video streaming and real-time applications that demand quick local decision-making.