## 2 Organization and Contribution of the Paper
Therefore, studies should incorporate UAV navigation considerations to enhance energy efficiency. Secondly, from a practical standpoint, generated paths and trajectories must be both dynamically and kinematically (kinodynamically) feasible. This means that UAV motion must adhere to kinematic constraints such as speed, acceleration, and nonholonomic limitations (e.g., rate of heading change). Moreover, in dense urban environments where paths are often obstructed and maintaining LoS links is crucial, the trajectory should also account for the UAV's rotational dynamics. This practical aspect is frequently overlooked in the current literature within the wireless communication field. 3. _Applications of UAVs and RISs in wireless networks_ are discussed in Section 5, where various services involving UAV deployment in communication networks, UAV design, and RIS technologies are reviewed. Each application, whether it involves UAVs as airborne BS, IoT sensor data collection, support for V2X communication in IoVs, maritime communication, etc., comes with its own unique environment and network characteristics. These differences necessitate distinct objectives and constraints in the optimization problems and significantly influence UAV navigation and control models. Additionally, depending on the application or available resources, the aerial package, comprising the UAV and communication module, can vary in design. For example, UAVs may be deployed as active aerial base stations (UAV-BS) or equipped with RIS (RISeUAV) to enhance energy efficiency and reduce system complexity. 4. _Solutions_ to the optimization problems are discussed in Section 6, covering convex programming, machine (deep) learning, and game theory approaches. Convex programming is a widely used solution for optimization problems. However, optimization challenges in UAV navigation, such as joint energy-efficient trajectory design and communication performance, are typically highly nonlinear and non-convex. As a result, alternating optimization (AO) techniques, including sequential convex programming (SCP) and successive convex approximation (SCA), are employed after applying convex relaxation methods. These techniques provide computationally tractable solutions, albeit at the cost of suboptimality [36]. Additionally, with recent advancements in artificial intelligence (AI), deep learning, and particularly deep reinforcement learning (DRL) agents, these methods have garnered significant attention. 5. Section 7 explores _integrated localization and communication_. The radio frequency sensor (radar) has been employed for object detection and tracking. In future WCNs,mmWave communication and beamforming are expected to enable higher angular resolution, leading to more accurate centimeter-level localization. As a result, mmWave localization technology has garnered significant attention for autonomous unmanned vehicles [37]. Additionally, RISs are increasingly valuable for creating smart radio environments, where the propagation and scattering of communication signals can be controlled, reducing multipath interference. Furthermore, localization-assisted communication is instrumental in transmitting and propagating directional communication signals, enhancing the efficiency and effectiveness of communication. The integration of sensing (localization) and communication (ISAC) serves as a powerful technique to optimize resource utilization, network capacity, and spectrum efficiency.