# Trajectory Optimization for UAV-Aided IoT Secure Communication Against Multiple Eavesdroppers +
## 1 Introduction
One study demonstrated that co-designing UAV jammer trajectories and transmit power can maximize the average achievable secrecy rate [26]. Energy efficiency is another critical consideration. A UAV-assisted amplify and forward relay network has been proposed, combining wireless energy harvesting with cooperative friendly jamming [27]. Here, a cluster of hovering UAVs not only relays information from ground sources to distant destinations, but also actively transmits jamming signals to eavesdroppers. Additionally, UAVs can form virtual antenna arrays to enable secure communication with ground terminals via beamforming, thereby improving secrecy rates [28]. In summary, PLS technology, when combined with UAVs' flexible mobility and cost-effective deployment, offers robust support for securing large-scale IoT wireless networks. It also presents novel solutions to overcome the limitations of traditional encryption in resource-constrained environments. However, research is still scarce for scenarios with multiple eavesdroppers in IoT systems, with insufficient exploration of effective countermeasures to address the security challenges presented by such multi-eavesdropper configurations. Most existing studies focus exclusively on single-eavesdropper scenarios, whereas real-world IoT deployments often involve multiple eavesdroppers distributed across different locations, significantly increasing the complexity of ensuring secure communication. Moreover, coordinated eavesdropping among multiple malicious nodes can substantially increase the risk of information leakage. To tackle these critical challenges, we expand upon our earlier work presented at the 2022 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery [29]. In this paper, we introduce an innovative and efficient trajectory optimization algorithm tailored for multi-eavesdropper scenarios, aimed at strengthening UAV communication security within IoT networks. The main contributions of this study are summarized as follows:  
* A secure communication framework for IoT systems is introduced, incorporating UAVs alongside multiple eavesdropping entities. Through the optimization of UAV flight trajectories, we improve the worst-case secrecy rate, thereby augmenting physical layer security in environments with multiple eavesdropping nodes. * To mitigate the computational complexity of continuous trajectory optimization, we discretize the UAV trajectory into a limited collection of coordinate points and develop an optimization model based on these discrete coordinate variables. * To address the challenge of maximizing secrecy rates under the worst-case scenario, where the optimization problem is inherently non-convex and non-differentiable, we utilize SCA techniques and hypograph theory. By implementing a novel set of trajectory increment variables to replace the original UAV trajectory coordinates, we reformulate the initial non-convex problem into a set of convex subproblems. Moreover, by utilizing hypograph theory, we equivalently convert the non-differentiable subproblems into standard convex optimization formats, facilitating rapid resolution. * Simulation results indicate that under varied parameter conditions, the proposed method can efficiently evade multiple eavesdroppers with a low computational complexity while significantly improving the system's physical layer security (PLS) performance. This validates the substantial impact of UAV trajectory optimization on improving PLS effectiveness. The remainder of this article is organized as follows. Section 2 presents the system model and problem formulation. Section 3 introduces a novel and efficient trajectory optimization algorithm. Section 4 presents and analyzes the simulation results. Section 5 provides the conclusion. Finally, Section 6 discusses the future work.