authors propose an approach for optimizing user scheduling, transmission power, UAV jamming power, RIS phase shift, and UAV trajectory to maximize the worst-case secrecy rate. In this study, despite the central problem's non-convexity, they used alternating optimization (AO), slack variable techniques, successive convex approximation (SCA), and semi-definite relaxation (SDR) to develop an effective suboptimal solution. Simulation results show that the algorithm significantly improves the secrecy rate compared to benchmark methods.  
In the third contribution, [PERSON] and [PERSON] propose a location-allocation model for environmental emergency response using a drone network. In the proposed approach, drones verify emergencies and gather data to aid response efforts. The model, operated by first responders, seeks to minimize costs while meeting operational goals. Through conducting a case study in Ji-Parana in the Brazilian Amazon, the authors used data from a whistleblower app and satellite imagery to test the model's effectiveness under various demand and capacity scenarios. A set of experiments were performed to evaluate the effectiveness and feasibility of the proposed model.  
In the fourth contribution, [PERSON] et al. propose a dynamic checkpoint interval decision algorithm for drone recovery, enhancing reliability and stability in large-scale monitoring. Drones often face mission interruptions due to network errors, low battery capacities, or physical defects. Checkpointing periodically saves the system's state, enabling mission restoration. The proposed algorithm minimizes energy consumption while increasing checkpoint operations, consuming only 3.51% more energy but performing 25.97% more checkpoints than the Fixed Interval Checkpointing (FIC) scheme. This approach boosts checkpoint availability and quickly resumes missions, improving the overall efficiency of drone-based services.  
In the fifth contribution, [PERSON] et al. [5] show how with the rise of IoT in remote and complex areas, UAV-based edge computing addresses challenges where base stations are impractical. Their paper proposes a multi-UAV-assisted MEC system to optimize energy consumption and resource allocation. A joint deployment and task scheduling algorithm is developed, showing improved energy efficiency and task handling compared to benchmark algorithms.  
In the sixth contribution, [PERSON] et al. discuss the use of UAVs to assist in data transmission from sensor nodes (SNs) in WSNs or IoT networks to base stations (BSs) in order to overcome the limitations of sensor devices. UAVs are effective in time-sensitive applications like disaster response tasks but face challenges in timely data delivery due to flight control and scheduling issues. The Age of Information (AoI) metric, measuring data freshness, has driven research in optimizing UAV-assisted data gathering. This paper reviews the literature on AoI minimization, identifying key design aspects such as energy management, flight trajectory, and UAV/SN scheduling. It also explores related issues and future considerations, like traffic prioritization and system optimization.  
Finally, in the seventh and last contribution, [PERSON] et al. analyze the role of UAVs in opportunistic wireless networks, especially in areas without infrastructure or with overloaded networks. However, their short battery life and need for frequent charging limit their utility. This paper addresses optimal charging station placement in UAV-aided networks using K-means, DBSCAN, and random clustering. These approaches were tested with routing protocols, namely Epidemic, Spray and Wait, and State-Based Campus Routing (SCR). Simulations showed that K-means clustering with three clusters results in a reduction in message delay and higher success rates compared to random or DBSCAN placement, regardless of the routing strategy adopted.