Energy-Efficient Trajectory and Resource Optimization for Cognitive IoT-Enabled AAV Aerial Computing in Smart Healthcare Systems

The rapid proliferation of Internet of Things (IoT) devices in healthcare has posed considerable hurdles in data processing, energy efficiency, and real-time response demands. This study presents a novel framework for cognitive energy-Efficient healthcare-enabled aerial computing and trajectory optimization (CEHEAT) to solve these difficulties. CEHEAT enhances energy efficiency by merging cognitive IoT (CIoT) capabilities with autonomous aerial vehicles (AAV) aerial computing to address the varied needs of healthcare applications. The system guarantees efficient data collecting and processing using cognitive skills, such as adaptive sensing and dynamic resource allocation while ensuring trustworthy healthcare monitoring. Simulation results demonstrate that CEHEAT significantly outperforms existing approaches across multiple performance metrics. The framework achieves 25%–35% higher energy efficiency, 3x faster convergence, and improved scalability compared to baseline methods. Moreover, CEHEAT’s adaptive sensing and speed control mechanisms contribute to overall energy savings while maintaining effective coverage of the healthcare IoT network. The findings indicate CEHEAT’s ability to improve the efficiency and effectiveness of smart healthcare systems, especially in situations that demand real-time monitoring, faster response as well as large quantities of data processing. This work gives useful guidelines for deploying the AAV-assisted aerial computing system in health facilities that extend the scope of healthcare delivery systems.