The integration of sixth-generation (6G) networks with the Industrial Internet of Things (IIoT) promises unprecedented connectivity and intelligence for industrial applications. However, the massive scale of device deployment and dynamic spectrum requirements in 6G-IIoT systems pose significant challenges for reliable and trustworthy resource allocation. This paper proposes a multi-objective optimization approach that leverages cognitive radio technology and energy harvesting capabilities to ensure trustworthy resource allocation while maximizing data transmission and minimizing energy consumption. The framework incorporates imperfect spectrum sensing and interference constraints to guarantee reliable coexistence between primary and secondary networks, aligning with the reliability requirements of 6G-IIoT applications. This paper proposes a constraint repair strategy-based multi-objective artificial hummingbird algorithm (CPS-MOAHA) that effectively handles the complex constraints and provides Pareto optimal solutions. Simulation results demonstrate the superiority of the proposed algorithm in achieving better trade-offs between data transmission and energy efficiency compared to existing approaches.

With the rapid expansion of the Internet of Things (IoT) and the increasing unmanned devices, data transmission and sharing between unmanned agents in the Internet of Unmanned Agents (IUA) face significant challenges. Mobile Edge Computing (MEC), which extends computing power from the cloud to the network edge, has become a key technology for enabling efficient, low-latency communication services. However, with the surge in the number of terminal devices and the diversification of service requirements, traditional MEC deployment methods face challenges such as inflexible resource allocation and limited service coverage. This paper mainly researches an unmanned aerial vehicle(UAV)-assisted ground-to-air communication and computing system, constructs a multi-UAV network communication model and a computing model, and proposes a joint optimization problem of system task processing delay and energy consumption based on the total system delay and the energy consumption of the UAV. For the path planning and resource allocation problems of the multi-UAV network, an improved double-delay deep deterministic policy gradient algorithm is proposed to jointly optimize the trajectory planning, user association and task offloading strategies of the multi-agent UAV. Finally, the performance of the proposed algorithm is verified and analyzed through simulation experiments.

In complex electromagnetic environments, targets that need to be interfered with often possess high levels of concealment and anti-interference capabilities. Additionally, due to the dynamic characteristics of these targets, interference tasks must be conducted within strict time constraints to ensure interference effect. In this article, we adopt a reconnaissance-first approach for concealed targets. After detecting the accurate location of the target, we deploy autonomous aerial vehicles (AAVs) to interfere with the targets. First, we established a AAV swarm task scheduling optimization model after considering constraints, such as target threat range, priority of reconnaissance and interference tasks, interference task time, and AAV energy consumption. Meanwhile, we model the anti-interference capability of the target as a threat range. Second, we propose a nondominated sorting genetic algorithm based on distance in the solution space and a dynamic parent selection strategy (DPSNSGA-II) to solve AAV resource scheduling optimization problem. The diversity of the population is increased and the situation of falling into local optima is reduced by improving the parent individual selection strategy, mutation strategy, and elite solution retention mechanism. Finally, we construct two data sets of varying sizes to evaluate the quality of solution sets and the convergence performance of the proposed algorithm. The simulation results indicate that the proposed DPSNSGA-II algorithm has better result for population diversity and convergence compared to state-of-the-art algorithms.

Unmanned aerial vehicle (UAV) swarm shows great potential as an effective tool for target tracking through completing complex tasks by collaboration of heterogeneous UAVs. However, UAV swarm scheduling faces challenges with poor quality communication and obstacles, especially in communication denial environment with multiple obstacles. To overcome these challenges, first, this paper proposes a scheduling slot model which divides the scheduling process into multiple time slots, allowing UAVs to communicate in communication slots while predicting instead of communication in communication denial slots. In communication denial slots, this model utilizes route fitting and two-stage Kalman filtering for UAV location prediction and optimizes UAV scheduling to align with predicted positions. In enabled slots, this model corrects position deviations to obtain precise UAV locations manually. Then, we propose an obstacle avoidance strategy to facilitate swarm scheduling for target searching under communication constraints. The obstacle avoidance strategy simplifies obstacles as regular hexagons and facilitates the determination of UAV avoidance routes by introducing intermediary points. Finally, to optimize UAV scheduling strategy, we propose a region co-evolution algorithm (RCEA), which emphasizes the collaboration among diverse individuals or populations. RCEA adopts area evaluation and Pareto strategy to enhance scheduling efficiency with following three steps. RCEA divides the overall scheduling region into multiple sub-regions, generates the foundational solution pool through the implementation of the area evaluation or Pareto strategy, and then proceeds to execute the region cooperation process base on the foundational solution pool. Simulation experiments are conducted to validate the performance of human-UAV interaction scheduling model with proposed scheduling methods and obstacle avoidance strategy. The simulation results demonstrate that RCEA outperforms other scheduling algorithms for UAV swarm in communication denial environment with multiple obstacles. Note to Practitioners—This paper addresses challenges inherent in real-world application scenarios, and the proposed algorithm has the potential to bring many benefits to practitioners. Firstly, the scheduling slot model can be applied not only to UAV swarm for target searching but can also be extended to other swarm devices for complex tasks with collaboration relying on communication support while facing poor quality communication or obstacles. Secondly, the proposed RCEA focuses on collaboration and region partitioning, the algorithm demonstrates remarkable scalability, effectively tackling challenges across diverse scales and complexities. Thirdly, the experimental scenarios can serve as a validation dataset for other peer researchers, and although the simulation experiment is based on a 2D movement model, this study still offers theoretical support applicable to a 3D movement model.

Automatic Modulation Open Set Recognition (AMOSR) is a crucial technological approach for cognitive radio communications, wireless spectrum management, and interference monitoring within wireless networks. Numerous studies have shown that AMR is highly susceptible to minimal perturbations carefully designed by malicious attackers, leading to misclassification of signals. However, the adversarial security issue of AMOSR has not yet been explored. This paper adopts the perspective of attackers and proposes an Open Set Adversarial Attack (OSAttack), aiming at investigating the adversarial vulnerabilities of various AMOSR methods. Initially, an adversarial threat model for AMOSR scenarios is established. Subsequently, by analyzing the decision criteria of both discriminative and generative open set recognition, OSFGSM and OSPGD are proposed to reduce the performance of AMOSR. Finally, the influence of OSAttack on AMOSR is evaluated utilizing a range of qualitative and quantitative indicators. The results indicate that despite the increased resistance of AMOSR models to conventional interference signals, they remain vulnerable to attacks by adversarial examples.