# A Set of Tools and Data Management Framework for the IoT-Edge-Cloud Continuum
## 4 Results
### Comparative Analysis
More studies are needed to benchmark the performance of the system in terms of latency and resource utilization. While the validation highlights the framework's potential, further quantitative experiments are recommended to measure performance metrics, including the latency, energy efficiency, and privacy level under various configurations. The structural integrity use case is based on TV tower monitoring in the city of Riga, Latvia. The tower is 368 m high. The deployed IoT system consists of vibration sensors and Fiber Bragg Grating Sensors deployed at the TV tower connected to a micro-edge device that collects and transmits the data to the meta-edge server, where the data are stored. An effective initial method for data acquisition has been developed, utilizing strategies created for the TV tower, and the optical model of the Fiber Bragg Grating Sensor has been confirmed to evaluate the structural health of buildings. The toolkit was successfully evaluated using a differential privacy approach for handling water quality monitoring data, safeguarding data privacy while enabling centralized training of the machine learning model across multiple utilities without exposing private data, showing that it is possible to achieve a similar performance when training neural networks with differentially private data in comparison to training the network with the initial non-private dataset. The increasing integration of IoT devices with Edge and Cloud computing introduces significant complexities related to data security, privacy, and interoperability. Addressing these challenges is essential to ensure reliable system performance and user trust in sensitive applications. The developed framework is a rapid and efficient tool for producing comprehensive technological solutions that address the complexities of data management and security across the IoT-Edge-Cloud Continuum. Through a unified configuration interface, this tool enables the seamless management of data flows at each layer--from IoT devices through Edge computing to Cloud infrastructure. This integrated approach ensures that data configurations and operations remain consistent across all tiers, significantly simplifying the coordination of data flow processes across diverse network components. Additionally, the tool incorporates functionalities that allow for the real-time visualization and analysis of data streams. By offering in-depth insights into data flow behavior, the software helps detect, assess, and report potential security or privacy vulnerabilities, facilitating prompt interventions to mitigate risks. The integration of real-time notifications for such events further supports pre-emptive responses to potential threats, enhancing the overall system resilience. To strengthen data protection and privacy, the tool provides extension plugins that embed advanced security features. Notably, it supports the application of differential privacy methods, which allow for the training of machine learning models on sensitive datasets while preserving data confidentiality. This privacy-preserving capability ensures that no sensitive data can be reconstructed or inferred from trained models, making it suitable for high-stakes environments where data privacy is paramount. Furthermore, the tool employs robust methods for securing data transmission across IoT, Edge, and Cloud layers, safeguarding the integrity and confidentiality of data as they traverse multiple network boundaries. The tool also offers dedicated features for the design, deployment, and validation of system configurations, establishing a high level of trust in both the security and privacy of data flows. By ensuring that each component within the system adheres to security protocols, the tool reinforces user confidence in the system's capacity to maintain data integrity and prevent unauthorized access. The proposed framework presents several key advantages that enhance the robustness, scalability, and operational efficiency of IECC systems.