## 4 The Application of Edge Computing System
### Monitoring and Control  
In power grids, the continuous monitoring and transmission of critical data in distribution networks enhance operational efficiency. However, the increasing density and volume of exchanged information across the network can degrade QoS, particularly under centralized processing models. To address this challenge, numerous studies have investigated decentralized computing approaches and the integration of EC into power distribution systems.  
Several studies have focused on integrating IoT paradigms into the Internet of Services (IoS) to enable decentralized computation and management. For instance, ref. [160] investigates decentralized computing potentials by embedding IoT principles into IoS architectures, while [161] develops an online monitoring system designed for transient electromechanical simulation in distribution grids. Similarly, ref. [162] proposes an EC-based monitoring framework for evaluating reactive power behavior in smart distribution transformers, aiming to improve real-time data analytics and responsiveness.  
The potential of cloud-edge collaborative computing models has also been extensively explored in intelligent monitoring applications. Ref. [163] introduces a hybrid architecture to enable real-time diagnostics of power distribution equipment via a cloud-edge framework. Likewise, ref. [164] improves grid security and reliability by integrating IoT and EC into a smart grid monitoring platform. Collectively, these contributions highlight how EC enhances operational performance and system resilience in power distribution networks.  
Another critical dimension of EC applications involves resource management and service orchestration in complex power systems. In [165], an EC-based service scheduling  
Figure 8: Proposed EC architecture for power distribution applications.  
approach is proposed to address the increasing computational and bandwidth demands in smart city and Power Distribution IoT (PD-IoT) infrastructures. Complementarily, ref. [166] introduces a task allocation strategy for real-time monitoring and early warning services, aiming to optimize the utilization of computational resources.  
In addition to monitoring and resource allocation, EC has been employed to enhance system integration and metering infrastructure. In ref. [167], a smart integrated terminal is designed using EC principles to improve metering efficiency and operational coordination. Similarly, ref. [168] introduces an EC-based power grid mapping structure for the digital management of distributed power networks. To establish a structured framework for power distribution applications, ref. [169] defines a four-layer EC-based architecture, comprising cloud, network, edge, and terminal layers, which serves as a reference model for future implementations, as shown in Figure 9. Further expanding on this architecture, ref. [170] proposes a \"cloud-side-edge collaboration\" framework to enhance the scalability and availability of substation operation support systems. This model ensures seamless coordination between monitoring, data processing, and decision-making tasks across multiple layers.  
Moreover, EC enables optimization of communication latency and computational load management. For instance, ref. [171] develops a node scheduling model based on EC that employs a genetic algorithm with a fighter search strategy to reduce transmission delays and minimize economic costs.  
Despite these advancements, traditional centralized monitoring systems remain insufficient to address the growing complexity and dynamism of modern power grids. To overcome these limitations, ref. [43] proposes a five-tier EC framework for real-time monitoring. As shown in Figure 10, the system automates line monitoring by processing image data from IoT devices at the edge, thereby enhancing threat detection and response times.  
Figure 9: The architectural framework of the power distribution network based on EC [169].