## 5 Open Issues and Future Work  
One of the key challenges that need to be addressed by the architecture of this platform is ensuring energy efficiency and sustainability across the IoT-Edge-Cloud continuum. The orchestration layer of the platform should be improved, with energy efficiency as a core objective, leveraging AI/ML algorithms to optimize the allocation of resources and reduce unnecessary energy expenditure. The orchestration layer should intelligently distribute tasks to Edge nodes with the appropriate computational capacity and energy profile, minimizing power usage while ensuring the required performance for time-sensitive applications. Techniques like neural network pruning and quantization, as well as the development of low-power hardware, will be necessary to reduce the energy demands of AI/ML processing. In particular, the platform faces technical challenges in managing the energy consumption of high-performance Edge nodes during peak processing loads, which require fine-grained scheduling and adaptive power management mechanisms. Future work could also explore implementing real-time energy monitoring at both the node and cluster levels to detect and adjust for energy spikes, thus optimizing consumption dynamically. Future work should also explore the potential of green computing technologies in reducing the platform's environmental impact. The integration of renewable energy sources into Edge nodes and IoT devices is one possibility. Solar-powered IoT sensors or energy-harvesting technologies, for example, could significantly reduce the platform's energy consumption, especially in remote or off-grid areas. Research into energy-efficient hardware, such as low-power microcontrollers and processors, will also be critical for minimizing the platform's overall energy footprint. Future research should also investigate integrating green computing solutions, such as low-power AI inference engines specifically optimized for Edge applications. These engines could utilize workload-aware adaptation techniques that dynamically scale AI model complexity according to energy constraints without compromising performance. Additionally, exploring Edge node designs with modular renewable energy inputs--such as solar or wind modules--could facilitate the deployment of sustainable, off-grid solutions, particularly beneficial for rural or hard-to-reach IoT sites. Predictive maintenance, driven by AI/ML, plays a significant role in extending the lifecycle of hardware components and reducing waste. By analyzing data from IoT devices and Edge nodes, the system could predict potential failures before they occur, minimizing downtime and reducing the need for frequent hardware replacements. For example, predictive analytics could be used not only for hardware maintenance but also for optimizing network performance, predicting congestion, or identifying potential security threats. The system could predict when certain devices or components are likely to experience low activity and adjust their energy consumption accordingly. Additionally, by simulating future system states, Digital Twins could help in planning energy-efficient expansion strategies, such as determining when and where to deploy new Edge nodes to meet the growing demand without over-provisioning resources. For example, algorithms could be developed to take into account not only the current workload but also the energy profile of individual devices and their surrounding environment. To address predictive maintenance challenges, the platform could implement lightweight, on-device AI modules that provide real-time analytics on component health, thus minimizing data transfer and processing costs. For instance, anomaly detection algorithms tailored to the behavior of specific hardware components could be deployed at the Edge. This approach would enable early fault detection with minimal impact on computational resources, crucial for sustaining continuous operations in resource-constrained environments.