## 2 Related Works  
According to [3], the growth, development, and popularity of the IoT and big cloud services caused the need for edge computing. Edge intelligence or edge AI represents the combination of edge computing (EC) and distributed computing. In such environments, data processing is relocated from the cloud to the network edge. The goal is to implement computing and data storage nodes on edge devices, e.g., mobile devices or sensors. This goal is facilitated by the rapid changes and development in recent years [4]. As stated in [5], the main significance of EC is as follows:  
* The combination of sensor technology and edge devices impacts efficient real-time data acquisition, especially in smart buildings and smart home systems;
* There is evident growth in edge-oriented communication technologies, e.g., device-to-device (D2D) communication;
* EC is targeted as an important component of edge intelligence, helping in reducing the response time, network traffic, and latency and saving energy and bandwidth;
* Edge devices store temporary real-time information;
* There is the possibility of extensive use of mobile phones as the edge devices;
* Edge devices can achieve significant collaboration taking advantage of their mobility, proximity, and deployment. There are numerous examples of using edge intelligence for monitoring and managing solar panels. In [6], EI enables the integration of solar energy into the electrical grid and solves problems related to solar energy production. A remote monitoring system with an integrated artificial neural network (ANN) for detecting shading in photovoltaic panels is presented in [7]. Paper [8] presents a system that uses Siamese-twin neural networks for anomaly detection. The system is implemented in a solar farm on edge devices based on a Raspberry PI, Nvidia Nano, and Google Coral. Authors from [9] propose a framework for the detection of anomalies in decentralized photovoltaic plants. This proposal contributes to hybridization between edge, fog, and cloud layers. Because Arduino-based clones are used in this research, it is important to show how popular Arduino is in academic institutions for research and education. The authors of [10] justified the popularity of Arduino development boards by analyzing the number of Arduino-based scientific papers. The research covers the period from 2010 to 2020 and shows constant growth in the platform's usage. Further, the examples of the implementation of AI on Arduino-based development boards are presented in the following papers. Paper [11] proposes a real-time non-intrusive load classification (RT-NILC) IoT-based system with an Arduino-based data acquisition system. In [12], a small-scale two-wheel system connected to a control unit is developed using an ARDUINO Uno Rev3 microcontroller and a Support Vector Regression (SVR) model. The Arduino UNO boards have certain limitations considering their memory and processing capabilities. In addition, they do not have integrated network connectivity modules. The low-cost Arduino-clone development boards based on ESP8266 chips, such as NodeMCU and equivalent ESP32-chip-based boards, have integrated Wi-Fi, and in the case of ESP32, Wi-Fi and Bluetooth Low Energy (BLE) connectivity. Paper [13] proposes a low-cost parking management system. The system is based on the Alibaba Cloud platform, machine learning, and an ESP8266-based Arduino-clone development board as a control module. In addition, [14] presents a similar ESP866 Wi-Fi module garbage management system for monitoring flame and several other ambient parameters. In [15], Node MCUESP8266 is used to monitor the quality of soil and predict crop types suitable for the monitored location [15]. ESP8266 is also used in the system for recognizing the character gestures [16]. Here, KNN (K-Nearest Neighbours), Decision Tree, and SVM (Support Vector Machine) classifications are implemented. The same development board is used in the system for real-time weather prediction systems [17].