## 4 The Application of Edge Computing System
### Smart Metering System  
Advanced metering infrastructure (AMI) is a critical component of modern power systems, enabling bidirectional data communication between smart meters and utility centers [182]. The integration of EC and edge intelligence with AMI enhances operational efficiency by reducing communication costs, protecting user privacy, and increasing situational awareness [183]. Figure 14 presents an overview of the EC-enabled smart meter architecture. In such systems, distributed deep learning based on federated learning (FL) has proven to be an effective approach for applications like non-intrusive load monitoring (NILM).  
Smart meters, as key consumer-side components of AMI, perform several essential functions, including real-time pricing updates, energy consumption monitoring, power loss detection, power quality assessment, P2P communication with other meters, and remote switch control [184]. The meter data management system (MDMS) plays a crucial role in processing and storing the large volumes of data collected from smart meters via data concentrators. Furthermore, MDMS supports decision-making processes that optimize energy consumption by integrating with other application systems. Smart meters collect real-time energy consumption data from IoT devices within the home area network (HAN) and transmit these data to the MDMS, where control instructions are generated. To enhance communication efficiency, ref. [185] proposes an aggregation-based Low Power Loss Area Network (RPL) scheme for power distribution networks. This scheme modifies the ContikiRPL protocol using the Low Power Personal Area Network (6 LoWPAN) protocol, improving communication between smart meters.  
Figure 13: EC-based system model for microgrid applications [172; 173].  
EC enables distributed computing capabilities, supporting energy control and enhancing the functionality of smart grids (SGs) through deep learning models [186; 187]. In [188], an optimization framework is proposed for code and data placement to enhance system stability and cost-effectiveness. Moreover, an advanced AMI model presented in [178] facilitates accurate electricity price forecasting, local-level service analysis, resource allocation optimization, and cost reduction by improving metering efficiency. Similarly, ref. [189] introduces a knowledge-based deployment strategy to optimize the energy consumption and lifespan of smart meters. The role of smart metering in IoT-based smart grids extends beyond conventional applications to improving power quality and reliability. Ref. [190] examines the applicability of smart metering technologies in modern power grids, emphasizing their role in enhancing grid modernization efforts. Furthermore, ref. [191] introduces a LoRa-based smart metering system designed to minimize energy consumption during data transmission, thereby enhancing the overall efficiency of the network. This system employs deep learning-based compression and optimization techniques, enabling energy-efficient data transfer with reduced latency. Ref. [192] develops an EC-supported data analytics system that utilizes a multi-process architecture for smart meters. The results demonstrate that incorporating edge analytics into smart meters significantly enhances data processing performance, with multiprocessing further improving system responsiveness.