## 4 The Application of Edge Computing System
### Data Transmission  
Data transmission in power systems plays a critical role in the efficient management and monitoring of energy networks. In these systems, large volumes of data are collected from sensors, smart meters, and other monitoring devices to facilitate the oversight of energy generation, transmission, and distribution. Figure 7 illustrates the EC architecture for power transmission applications. To ensure secure and timely data transfer, specialized communication protocols and infrastructures are required to ensure secure and timely data transmission [149]. A three-tier architecture has been proposed for implementing EC in smart grids, formed by the cloud-edge-thing continuum [150]. The total response time is determined by the sum of transmission and processing delays. In cases where IoT devices generate massive amounts of data but have limited computational resources, high network latency becomes unacceptable as they fail to meet QoS requirements. Unlike traditional cloud services, EC addresses the needs of IoT applications more efficiently by enabling real-time data collection and analysis with low latency [151]. Additionally, in [152], a fine-grained adaptive power control scheme is introduced to reduce latency, which optimizes data transmission paths using adaptive power management techniques.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Productions** & **Owners** \\ \hline TPU & Google \\ DianNao family & Cambrian \\ Turing GPUs & NVIDIA Corporation \\
7 Series FPGA & Xilinx \\ HiSilicon Ascend Series & Huawei \\ Exynos 9820 & Samsung \\ Xeon D-2100 & Intel \\ TrueNorth & IBM \\ \hline \hline \end{tabular}
\end{table}
Table 7: Some edge devices and owners [16; 148].  
Various optimization strategies have been investigated to enhance power transmission and energy efficiency. In [153], a long-term stochastic optimization problem was decomposed into multiple subproblems to maximize the transmission success rate. This approach integrates power control coefficients and aggregation coefficients, aiming to optimize the minimum time-average transmission success rate (TATSR) by determining the optimal coefficients for each period while accounting for the dynamic statistics of randomly arriving data. Additionally, power allocation strategies designed to improve energy efficiency were explored in [154]. This study employs fractional programming and convex optimization techniques, demonstrating that secondary users can achieve substantial energy savings even under average transmission power constraints.  
Furthermore, EC solutions for power transmission have been explored in [155], emphasizing the role of IoT sensor technologies in preventing faults in transmission lines. These technologies not only enhance transmission power capacity but also reduce economic losses by improving reliability. In the domain of voltage/VAR control (VVC) for distributed energy resources (DER), ref. [156] proposed an artificial neural network (ANN)-based strategy, enabling DER inverters to operate autonomously and overcome the limitations of conventional control methods. In a related effort, ref. [157] introduced a hierarchical-hybrid architecture for VVC in grids with high penetration of distributed generators (DG). Similarly, in [158], a three-tier voltage control framework tailored for grid clusters equipped with smart PV-based inverters (SPVIs) was presented, aiming to maintain voltage stability and minimize power losses. Finally, ref. [159] developed a monitoring system that combines Zigbee wireless communication with EC for remote transmission line monitoring. This system demonstrated low energy consumption, strong anti-interference capabilities, and improved fault detection accuracy. The proposed EC architecture for power distribution applications is illustrated in Figure 8.  
Figure 7: EC architecture for power transmission applications.