## 6 Discussion and Future Research Directions  
Although EC was developed to address issues such as bandwidth limitations, latency, and real-time data processing arising from the integration with CC, there are still some fundamental challenges and unresolved research areas in this field, despite significant progress. In particular, the rapid increase in IoT devices has made scalability between these devices a necessity. However, managing this scalability within EC remains a major challenge. Moreover, the limited storage capacity of edge devices further increases the dependency on cloud computing. Therefore, the development of new communication protocols and the optimization of existing ones are essential to ensure interoperability between edge and cloud computing systems. The advantage of EC in reducing latency presents a significant potential for developing effective architectures in critical processes, such as real-time control and monitoring in power systems. However, these processes require a more detailed investigation to develop applicable and scalable solutions. One key research challenge is optimizing the performance of connectivity layers within the EC architecture. Achieving high-speed data transfer and low-latency communication between hierarchical layers is fundamental to ensuring system efficiency and reliability. To establish a well-defined taxonomy, the EC architecture can be categorized into three primary layers: the Device-Edge Layer, the Edge-Server Layer, and the Cloud Layer. The Device-Edge Layer consists of resource-constrained IoT devices, sensors, and smart meters deployed at the network periphery. These devices perform preliminary data processing, local analytics, and real-time decision-making to reduce communication overhead and minimize latency. The computational capacity in this layer is typically limited, necessitating efficient workload distribution and lightweight data processing techniques. The Edge-Server Layer acts as an intermediary between the device layer and the cloud, comprising edge servers, gateways, and micro data centers with enhanced computational and storage capabilities. These nodes aggregate, filter, and process data received from multiple IoT devices before transmitting the refined information to the cloud. Additionally, this layer supports low-latency services and real-time analytics, enabling localized intelligence while reducing dependency on centralized cloud infrastructures. The Cloud Layer represents the centralized computing infrastructure responsible for deep learning, large-scale data analytics, and long-term storage. It provides extensive computational resources for complex model training, historical data analysis, and large-scale optimization. While CC ensures high processing power, it introduces latency and bandwidth constraints, making efficient coordination with the edge layers essential. The effectiveness of EC depends on the seamless interaction between these layers. Therefore, selecting appropriate networking technologies, optimizing communication protocols, and defining standardized roles for each layer are critical research directions. Future work should focus on refining these hierarchical structures to enhance the performance, scalability, and interoperability of EC-enabled power systems. With the shift in data processing to the network's edge, data privacy and security have become even more critical. In this context, developing and testing effective mechanisms to protect sensitive data is vital for EC to be adopted as a sustainable and reliable technology. In the future, new cryptographic approaches, security mechanisms, and hybrid architectures aimed at enhancing data privacy could significantly increase the effectiveness of EC. Additionally, network optimization, energy efficiency, and system resilience will remain significant research topics in the expanding application areas of EC.