# A Set of Tools and Data Management Framework for the IoT-Edge-Cloud Continuum
## 4 Results
### Comparative Analysis  
To evaluate the effectiveness of our IECC DMF, we conducted a comparative analysis with several existing solutions in the market. Table 1 provides a summary of the main features and capabilities of our framework compared to other leading tools. This comparison highlights the comprehensive nature of our IECC framework, particularly in areas such as differential privacy support and energy status monitoring, where it outperforms other solutions. Our framework distinguishes itself by offering a unique combination of features, including an extensible plugin system that offers support for differential privacy, energy status monitoring, that are fully open-source and cross-platform-compatible. This makes it particularly suitable for organizations that require a flexible, secure, and privacy-focused IoT management solution. The IECC DMF has significantly improved the configuration, visualization, and management of complex IECC systems. The key results include the following: Configurator and Visualizer Plugins: developed and integrated successfully, providing directed rooted tree generation, automatic node arrangement, data flow visualization, and an interactive system \begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Feature** & **IECC DMF** & **Things Board** & **AWS IoT Core** & **Azure IoT Hub** \\ \hline End-to-end data flow management & ✓ & Partial & ✓ & ✓ \\ Differential privacy support & ✓ & ✗ & ✗ & ✗ \\ Customizable plugin system & ✓ & Limited & ✓ & Limited \\ Visual data flow analysis & ✓ & Partial & ✓ & ✓ \\ Energy status monitoring & ✓ & ✗ & Partial & Partial \\ Open-source & ✓ & ✓ & ✗ & ✗ \\ Cross-platform compatibility & ✓ & ✓ & Limited & Limited \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of IECC framework with other IoT management solutions. management interface. System Architecture and Data Flow Management: the effective management of complex data flows across the IoT, Edge, and Cloud layers using MQTT and a customizable plugin system. Privacy and Security Enhancements: a differential privacy plugin for data obfuscation while maintaining utility, and a traceability plugin for monitoring data flows at specific nodes. Energy Efficiency: a plugin for tracking IoT devices' power consumption and estimating battery life. Comparative Analysis: the IECC framework excels in differential privacy support and energy status monitoring, uniquely offering open-source, cross-platform compatibility. Validation in Critical Infrastructure: the framework was tested in the hydro-monitoring and structural integrity monitoring of a broadcast tower, proving its real-world effectiveness. Machine Learning Integration: evaluated with differential privacy for water quality monitoring, allowing centralized model training across utilities without exposing private data. These results highlight the IECC DMF's effectiveness in addressing complex challenges of managing large-scale, heterogeneous IoT-Edge-Cloud systems, particularly in areas requiring high security, privacy, and efficiency. The proposed IECC DMF has been validated in two critical infrastructure sensor applications: the hydro-monitoring and structural integrity monitoring of the television broadcast tower. The framework's applicability was demonstrated through qualitative observations and system functionality in two real-world settings:  
1. Hydro-Monitoring: The framework supported continuous data acquisition and processing from distributed IoT sensors monitoring water treatment data. Using the differential privacy plugin ensured compliance with data privacy during the machine learning model training without compromising functionality. Future work is required to quantitatively assess the privacy-utility trade-offs. 2. Structural Integrity Monitoring: Implemented for a 368-m broadcast tower, the IECC DMF facilitated seamless data flow from vibration sensors and Fiber Bragg Grating Sensors to meta-edge servers. The framework demonstrated its ability to manage hierarchical data flows and validate configurations.