Optimizing UAV-Assisted Data Collection in IoT Sensor Networks Using Dual Cluster Head Strategy

The proliferation of the Internet of Things (IoT) has significantly impacted the integration of digital and physical realms, with Wireless Sensor Networks (WSNs) playing a crucial role. However, these sensor nodes often face challenges related to battery constraints and deployment in inaccessible terrains. The advent of Unmanned Aerial Vehicles (UAVs) presents a transformative solution, particularly for data collection from remote IoT devices. This work explores the application of UAVs to improve data collection in dense IoT sensor networks. We propose a novel approach called optimizing UAV-assisted data collection in IoT sensor networks using Dual Cluster Head (UAVDCH) that utilizes dual cluster heads within each cluster to optimize the UAV's energy consumption. The primary cluster head is responsible for collecting data within the cluster, while the secondary cluster head is tasked with transmitting the data to the UAV. Our objective is to maximize the available data the UAV collects with respect to its energy constraints. We develop a strategy for selecting appropriate secondary cluster heads, determining UAV's hovering points, and designing flight trajectories that maximize data collection. By adopting a multi-channel technique, we facilitate simultaneous data collection from multiple clusters, reducing hovering and transmission times. Experimental results demonstrate that our algorithm outperforms existing methods, offering a promising solution for energy-efficient data collection in IoT sensor networks.