# Architectural Trends in Collaborative Computing: Approaches in the Internet of Everything Era
## 4 Results and Analysis
### Synthesis of Results
Collaborative rules define interactions between platforms, supported by consensus-based control. Incentive mechanisms reward participants, while a Provenance Data Repository logs transactions for auditing. This decentralized model reduces vulnerabilities, enhances scalability, and ensures transparent, efficient IoT collaborations. The term \"Cooperation\" is accordingly suitable here because the system intends to integrate various platforms and end-users. Essentially, the focus is on multiple systems cooperating harmoniously and cohesively. The following collaboration aspects are extracted from [PERSON] et al. (2020):  
**Openness:**: The use of Blockchain technology, which is transparent by nature, allows for an open cooperative system where all transactions are recorded and can be verified by any participant. **Participation:**: The framework aims at facilitating interactions between various IoT devices, ensuring that all devices can participate actively. **Effort:**: The system requires individual devices and platforms to sign each interaction, ensuring that each device's effort is recognized and recorded. **Responsibility:**: As each interaction is signed by participants and recorded, there is a sense of responsibility toward each transaction. [PERSON] et al. (2020) aim to present research trends in Fog Computing architecture to attend to IoT requirements and identify research gaps and open issues in infrastructures, platforms, and applications. The primary purpose of Fog Computing is to address the challenge of efficiently running IoT applications by facilitating resource allocation and task scheduling. They arranged the components of the Fog Computing architecture, as shown in Figure 4.2 Several layers are designed for efficient, real-time data processing at the network's Edge. The physical layer gathers data from sensors (both physical and virtual). The Fog device, server, and gateway layer process this data locally, enabling quick decision-making before Cloud involvement. The Monitoring Layer tracks performance and resource usage, while the Pre- and Post-Processing Layer handles data analysis and filtering, reducing the volume sent to the Cloud. The Storage Layer manages data with backup and virtualization. The Resource Management Layer allocates resources efficiently, ensuring reliability and scalability. The Security Layer protects communications and user  
Figure 4.3 Cross-platform access control with Blockchain-Based Trust, from [PERSON] et al. (2020). privacy through encryption and authentication. Finally, the Application Layer supports low-latency applications like IoT and Augmented Reality, enhancing service quality and responsiveness. This purpose requires \"Coordination\" between various components of the Fog Computing environment, including IoT devices, Fog devices, and Cloud infrastructure. The following collaboration aspects are extracted from [PERSON] et al. [52]:  
* Fog Computing is an intermediary between IoT devices and Cloud infrastructure, providing structured data processing. * IoT devices generate data, and Fog devices process and store them. * Fog Computing optimizes data processing for time-sensitive IoT applications. * Fog Computing establishes a structured network between IoT devices, Fog devices, and Cloud. Figure 4.4 Components of Fog Computing architecture, from [PERSON] et al. [52]. [PERSON] et al. [47] addresses the problem of real-time queries of objects within large-scale surveillance video streams. Therefore, they propose SurveilEdge, an adaptive Cloud-Edge system that significantly outperforms Cloud-only, Edge-only, and fixed Cloud-Edge approaches regarding query accuracy, bandwidth cost, and query latency. SurveilEdge integrates cameras, Edge devices, and a Cloud server to provide efficient real-time query responses (see Figure 13). The system operates in two stages: offline and online. In the offline stage, conducted on the Cloud, cameras are grouped based on similar contexts, creating context-specific clusters for tailored training datasets.