Abstract—This paper presents a low-cost and energy-efficient system for environmental data collection in remote areas using Unmanned Aerial Vehicles (UAVs) and Internet of Things (IoT) sensor nodes based on ESP32 microcontrollers. The sensor nodes, powered by solar energy, collect environmental data and store it locally. A drone equipped with an ESP32 device periodically flies over the monitored area, retrieving data via the ESP-NOW protocol. Onboard edge computing is performed using the Typicality and Eccentricity Data Analytics (TEDA) algorithm to detect and filter outliers before the data is transmitted to the cloud. The system integrates with Amazon Web Services (AWS) IoT Core for secure data transmission, processing, and visualization through Grafana dashboards. Experimental results demonstrate up to 2.66 days of battery-only autonomy, extended by solar charging, and reliable wireless communication up to 100 m. The entire system was built for under USD 320, with each sensor node costing approximately USD 36. This architecture proves to be a practical and scalable solution for real-time environmental monitoring in remote or infrastructure-constrained environments.
 
Keywords—artificial intelligence, drone, Internet of Things (IoT), outlier


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