## 2 Architecture and Framework of Computing Methods
### Edge Computing  
EC is a network paradigm that aims to perform computations as close as possible to the data source in order to reduce latency and optimize bandwidth usage. In other words, it refers to offloading certain processes from centralized cloud environments to localized nodes to localized locations, such as a user's computer, an IoT device, or an edge server [38]. More specifically, ref. [12] defines EC as \"a new computing model performed at the edge of the network\" and encompassing network resources and computing processes between the data source and the CC center. Ref. [39] emphasizes that EC aims to provide more efficient services by placing computing and storage resources closer to mobile devices or sensors at the network edge. Similarly, ref. [40] states that EC delivers real-time services by reducing transmission delays and bandwidth usage with computing and storage resources placed at the edge of the network. This technology enhances data processing and network optimization by providing front-end computational support for applications such as mobile devices and the IoT. EC refers to the decentralization of storage and computing resources and locating them closer to the data source. In this approach, raw data are analyzed directly near the network edge rather than being sent to a central cloud server for processing and analysis [41]. Moreover, EC refers to the physical computing infrastructure positioned between the device and the hyperscale cloud, enabling support for various applications. In ref. [42], EC provides to provide more efficient and faster services by bringing the capabilities of CC closer to the device, i.e., the data source. EC is particularly effective in smart grid systems due to its ability to perform computations closer to the data source, reducing latency and optimizing bandwidth usage. It consists of a three-layer architecture: (1) the device layer, including edge devices (2) the edge server layer and (3) the cloud layer [43]. The device layer includes various edge devices with limited computing and storage resources, such as sensors, actuators, and IEDs (e.g., relays, energy analyzers, and quality recorders) located in distribution centers of the power grid, which are responsible for data collection. This layer offers faster and more flexible solutions by processing current, voltage, and fault current data from IEDs when anomaly conditions are detected. The network connecting the device layer to the edge server layer and the edge server to the cloud server layer requires high resolution, high data rates, and high bandwidth for data transmission. Wireless connections are often preferred over wired connections. The edge server layer, positioned between the cloud server layer and the device layer, brings the cloud server tasks closer to the data source, reducing latency and bandwidth usage [21]. The cloud server layer is responsible for further processing and managing large volumes of data that have been pre-processed at the edge server layer. Figure 2a illustrates the general hierarchical architecture of EC. The literature contains numerous applications of EC across various domains. In Industry 4.0, ref. [44] examines the integration of EC with Smart Manufacturing applications, highlighting its benefits during the digital phase of the Physical-Digital-Physical (PDP) cycle and advancements in Industry 4.0. Ref. [45] explores the use of computational intelligence (CI) techniques to address challenges such as resource allocation and task offloading in both CC and EC, emphasizing the complex nature of these optimization problems, including their convexity and NP-hardness. Furthermore, EC has been proposed as a solution to the big data problem generated by IoT-connected devices by offering distributed computation and storage services, and a distributed multi-level storage (DMLS) model and multi-factor least utilized (mLFU) algorithm have been used to manage constrained resources [46]. In the energy sector, ref.