# Artificial Intelligence of Things for Solar Energy Monitoring and Control
## 2 AIoT in PV Systems: Background
### Internet of Things
However, it faces security concerns, leading to research on vulnerabilities and defense mechanisms [55]. In PV systems, MQTT enables real-time monitoring and efficient data transfer between solar panels, inverters, batteries, and monitoring systems, helping track performance metrics like voltage and current, while facilitating remote monitoring and analysis. IoT data analysis is important for optimizing PV systems, improving energy efficiency, and enabling timely decision-making. PV systems generate large volumes of data, which present challenges due to their high volume, speed, and variety. Traditional methods struggle with these \"Big Data\", necessitating advanced computing techniques like edge computing, cloud computing, or fog computing to process and extract insights. These methods enable real-time decision-making, predictive maintenance, and optimization of energy output, especially for large-scale or remote PV installations [56; 57; 58]. Starting with the edge computing paradigm, widely applied, it involves processing data closer to the source of generation, which in this case is the solar panel. In PV systems, edge computing enables real-time monitoring of energy production, system performance, and environmental conditions by analyzing data locally, without relying heavily on distant cloud servers. This results in reduced latency, quicker response times for control systems, and enhanced efficiency in energy management. By decentralizing computation, edge computing improves scalability and reliability in remote PV installations, ensuring continuous operation even in areas with unreliable internet access [59]. Additionally, it strengthens security and privacy by limiting the transmission of sensitive data to the cloud, reducing vulnerability to cyber threats [60]. Cloud computing refers to delivering computing services, applications, storage, and processing over the internet. It relies on centralized hardware systems located in data centers to handle vast amounts of IoT data, providing robust computational capabilities [61; 62]. It is cost-reduced since it eliminates the need for expensive hardware and software [63]; also, it offers enhanced capacity by increasing computational power and storage through multi-core architectures [64], and it enables efficient analysis of large IoT datasets from any location [65]. Additionally, cloud computing minimizes the energy required for local data processing, offering an environmentally friendly solution. Its secure infrastructure further ensures reliable data storage and management [65]. In the context of PV systems, cloud computing is used to store and analyze vast amounts of data generated by IoT devices embedded in solar panels, inverters, and other system components. This enables remote monitoring, predictive maintenance, and optimization of energy generation, ensuring the efficient operation of PV systems. Cloud platforms provide the scalability needed to manage data from large-scale solar farms or multiple PV systems, while offering high-level data-processing capabilities that enhance the overall management and performance of solar energy operations. While fog computing extends cloud computing by decentralizing data processing and moving it closer to the edge of the network, near IoT devices, this distributed paradigm addresses issues of latency and bandwidth limitations associated with centralized systems [66]. Devices such as personal computers, routers, and industrial controllers serve as fog nodes, processing data on site [67], offering faster response times and reduced network traffic compared to cloud computing [68], as well as ensuring secure processing and storage for applications requiring real-time decision-making [68]. Fog computing's ability to deliver faster, localized, and secure services makes it an efficient complement to cloud computing, especially for IoT applications that demand quick responses.