The aim of this study is to explore and validate the potential of using brain-computer interfaces (BCIs) and electroencephalography (EEG) signals to control external devices, specifically unmanned aerial vehicles (UAVs). The main problem of research is to integrate between human cognitive functions with Internet of things (IoT) devices, enabling hands-free control based on mental commands. Such an approach could transform fields where rapid, precise, and intuitive control is critical, including healthcare, search-and-rescue missions, and industrial automation. By capturing raw EEG data associated with simple motor commands, we demonstrate the feasibility of translating specific brainwave patterns into UAV instructions like "hover" and "land." The next phase will involve analog-to-digital processing and filtering to refine these signals, preparing them for machine learning algorithms to enhance command accuracy. Our findings highlight the promise of neural interfaces as a bridge between human thought and machine action, paving the way for advancements in IoT-based human-technology interaction.

Autonomous vehicles (AV) applications can extend to reach variable fields, some fields are more impacted with this interference than others, farming, wildfire detecting, search and rescue, weed control, and livestock management are critical and can be enhanced in large range using the integration between artificial intelligence (AI) and AV’s. Thus, in this research, we try to describe a process of making a singular multitask AV with sufficient processing power, and an accurate AI functionality to perform such tasks in real time. In order to achieve such target, multiple requirements are essential such as an accurate flight controller, a high-end processing unite, and aerodynamical design to operate in severe weather conditions, as well as precise and high-quality materials for 3D-printing and execution.