## 5 Open Issues and Future Work
Additionally, future work could explore adaptive Digital Twin models that evolve based on real-time operational data, enabling more accurate simulations for predictive maintenance. Security and privacy challenges are another key consideration that cannot be overlooked in future work. With an increasing number of devices connected to this hyperdistributed platform, the attack surface expands exponentially, creating vulnerabilities at various levels, from the IoT layer to the Cloud. As more data are collected and exchanged between nodes, privacy concerns also grow, especially in applications that involve personal or sensitive data. AI/ML could play a role in detecting and mitigating attacks in real time, but lightweight, efficient algorithms are necessary to avoid additional computational burden. Techniques like federated learning, where data remain local while models are trained in a decentralized fashion, could address privacy concerns while still allowing for the development of intelligent systems. The platform's hyper-distributed nature introduces unique challenges in securing device-to-Cloud data flows, as well as ensuring data integrity across multiple network slices. One specific challenge lies in detecting and mitigating cross-layer attacks that exploit both the IoT and Cloud layers. Future work should also explore implementing cross-layer intrusion detection systems (IDSs) that use federated learning across Edge nodes to identify attack patterns without compromising data privacy. Developing these IDS solutions will require algorithms optimized for Edge deployment, with a minimal footprint to avoid disrupting essential real-time applications. An area that deserves further exploration is interoperability and standardization. The platform integrates a variety of devices, applications, and communication protocols, making it imperative that these components work seamlessly together. The lack of standardization in IoT ecosystems remains a significant hurdle in achieving full interoperability. Future work should focus on establishing common protocols, communication standards, and APIs that allow diverse devices and applications to interact with each other. Standardization is crucial for scalability, enabling the integration of new technologies without disrupting existing workflows. Open architectures and collaboration between industry and academia could help in defining these global standards. To address interoperability, research could also focus on developing open-source interoperability frameworks for multi-vendor IoT ecosystems, enabling standardized data exchange protocols across devices and Cloud services. For instance, future work could involve implementing Edge-centric gateways that translate proprietary device protocols into a common framework, like MQTT or CoAP, facilitating seamless integration. Collaboration with standardization bodies (e.g., IEEE, ISO) could also help establish industry-wide protocols for Edge-Cloud interoperability. Finally, social and ethical considerations must be addressed as hyper-distributed platforms become more embedded in critical infrastructures, such as healthcare, transportation, and smart cities. Issues of data ownership, transparency, and the ethical use of AI in decision-making processes must be carefully considered. Future work should explore the development of explainable AI (XAI) models that provide human-readable explanations for decisions made by AI systems. This will be essential for ensuring trust and accountability in applications where AI plays a central role in real-time decision-making. Ethical challenges also arise in ensuring the transparency and accountability of AI-driven decisions made within the IoT-Edge-Cloud platform. One concrete step in this direction would be to integrate explainable AI (XAI) components that provide clear rationales for decisions in critical applications, such as healthcare monitoring or autonomous systems.