Abstract
Flexible movement and rapid deployment capabilities of unmanned aerial vehicles (UAVs) or drones have enabled them to be ideal for fresh and real-time data collection in the Internet of Drones (IoD) network. With the rising demand for IoD applications, optimizing the Age of Information (AoI), and energy efficiency of drones has become a challenging problem. The existing literature works are either limited by considering single-drone data collection from 2D space or by not prioritizing data from diverse IoT devices. In this paper, we have developed an optimization framework for multi-drone-assisted data collection in 3D space, which brings a trade-off between minimizing drone energy consumption and AoI, exploiting the Mixed Integer Linear Programming (MILP) problem. However, due to the NP-hardness of the developed optimization framework for large networks, we have devised a Multi-Agent Deep Deterministic Policy Gradient (MADDPG) algorithm, supported and enhanced by a Multi-Head Attention (MHA) mechanism for multi-drone-assisted data collection to minimize drone energy consumption and AoI jointly, namely MECAO. The MHA in the MECAO system helps prioritize IoT data sources and ensures the timely collection of important data. This system enables the agents to coordinate effectively among themselves and provides innovative solutions to complex network issues. Our findings demonstrate substantial advancements in real-time data collection and drone performance, offering a practical and efficient solution for modern IoD applications. The developed MECAO system is implemented in the OpenAI Gym simulator platform, and the simulation trace file content depicts the improvement in AoI by up to 56% while the energy consumption is reduced by as high as 38.5%, respectively, compared to the state-of-the-art works.