# A Set of Tools and Data Management Framework for the IoT-Edge-Cloud Continuum
## 2 Literature Review  
This review consolidates the literature on IoT, Edge, and Cloud computing frameworks, highlighting the shortcomings our framework aims to address. An evaluation analysis of existing solutions supports our analytical approach. As IoT solutions become more prevalent [3], numerous companies are creating bespoke IECC solutions, either as proprietary tools or integrated into their product offerings. IECC solutions provide many benefits, such as bringing responsiveness and innovation where they are most needed, thus accelerating the convergence of information and fueling the digital transformation of many industries. The demand for IECC solutions is growing across industries, providing the mentioned benefits to critical areas, and accelerating digital transformation. These systems enable quick experimentation, adaptable changes, real-time analysis and feedback, and provide on-demand services, optimizing operations and enhancing competitiveness [4]. However, IECC systems are inherently complex and require high standards of reliability, security, and efficiency [5]. Scientific research highlights that optimal IECC systems depend on secure architecture topology planning, robust data protection, and ease of management, especially in applications such as AI training [6; 7]. It is evident that IECC systems require the careful management of data flow, the preservation of privacy, and security measures at all continuum layers [8]. However, the correct design and implementation of these systems pose considerable challenges. IECC solutions are very difficult to design and deploy correctly due to the fact that IoT, Edge computing, and Cloud computing expertise do not fully overlap. This gap leads to situations where either an expert in one of these domains has to rapidly learn the basics of the other two domains, or multiple experts have to collaborate to design a coherent system without understanding key nuances of the other domains and how they interact. Inefficient deployments frequently occur due to complexity, along with potential security weaknesses. Consequently, this leads to suboptimal implementations and security weaknesses, e.g., Cloud or Edge encryption algorithms might not be fast or energy-efficient enough for IoT devices, mixing wired and wireless protocols, and different data types in the same system can cause confusion and errors, such as packet loss, overflows, etc. Inefficient deployments due to security lapses or integration issues also result in exploitable vulnerabilities, such as inefficient encryption or errors in data handling. In addition, even if a company manages to invest in a sufficient group of experts, including not only general security experts but also specific IoT security experts, Edge security experts, and Cloud security experts, IECC technologies as a whole have developed a reputation for being inherently complex and prone to security risks. This perception limits the ability to build a trusted customer base, while at the same time, customers do not have an external authority they can trust to enforce the best security and implementation practices in these systems. Many current solutions are fragmented, focusing on specific components. For instance, platforms like AWS IoT Core and Azure IoT Hub provide strong cloud integration capabilities, yet do not offer native support for differential privacy or detailed energy monitoring. In comparison, the proposed IECC DMF addresses this gap by enabling end-to-end data flow configuration management with integrated privacy support features. Additional studies should investigate its scalability and applicability across diverse IoT use cases.