Energy-Efficient Solar-Powered UAV-Assisted Reliable Data Collection Considering IoT Elevation

Unmanned Aerial Vehicles (UAVs) have become essential in Internet of Things (IoT) networks due to their flexibility, ability to access hard-to-reach areas, capability to gather real-time information, and seamless integration with IoT systems. Many researchers addressed data collection in UAV-assisted IoT networks, however, considering a limited number of devices while lacking the consideration of solar-powered UAVs and elevation of IoT devices. In our work, we propose an energy-efficient approach for UAV-assisted data collection where the elevation and 3D location of devices and solar energy produced are considered. We first formulate the joint IoT clustering, cluster head (CH) selection, UAV deployment, 1oT-to-CH, and 1oT-to-UAV association problems as a multi-objective optimization problem. Then, we introduce sub-optimal real-time solutions using multi-layer clustering based on DBSCAN clustering and then using Gap statistics and K-means to deploy the minimum number of solar-powered UAVs while collecting data from the largest number of IoT devices and minimizing energy consumption subject to latency and reliability constraints.