## 1 Introduction  
The modernization of human life, characterized by advancements in smart cities, smart grids, smart transportation, etc., increasingly depends on wireless communication and sensor networks built upon the massive Internet of Things (IoT) [1, 2, 3]. The growing demand for high-bandwidth wireless networks capable of supporting high data transmission rates has driven the development of technologies beyond fifth-generation (B5G), sixth-generation (6G) [4], and optical (visible light) wireless communication networks (WCNs) [5]. Future WCNs leverage quasi-optic millimeter wave (mmWave) electromagnetic signals to achieve higher carrier frequencies, enabling high-speed, high-data-rate wireless communication [6]. Although mmWave signals offer higher bandwidth, they face significant propagation challenges due to rapid attenuation, fast fading, shadowing, as well as blockage and blind-spot issues [7]. To mitigate these limitations, the concept of massive multi-input multi-output (MIMO) communication has been introduced [8]. MIMO base stations (BSs) are equipped with arrays of mmWave antennas [9], enabling them to enhance signal strength through beamforming. This technique focuses electromagnetic waves to transmit steered signals in line-of-sight (LoS) directions toward user equipment (UE) [10]. However, the massive MIMO concept introduces complexity and reduces sustainability in future WCNs due to the intensive baseband signal processing and active beamforming requirements, which can lead to energy and spectral efficiency challenges [11]. Reconfigurable intelligent surfaces (RISs), which act as passive yet smart mmWave reflectors, offer a solution to enhance both spectral and energy efficiency in future WCNs [12]. Unlike active MIMO systems, RISs do not require radio frequency (RF) chains for signal processing and consume minimal energy for passive beamforming, making them a viable option for more sustainable and less complex network architectures [13]. The significant mobility of unmanned aerial vehicles (UAVs), commonly referred to as drones, has proven advantageous for enhancing WCNs by improving coverage [14], ubiquitousness [15], capacity [16], flexibility, throughput [17], and reliability. UAVs can be utilized for data collection in sensor networks [18] or as enablers for mobile edge computing (MEC) to support remote IoT applications [19]. Among UAV types, quadrotors, which are vertical take-off and landing (VTOL) vehicles [20], are particularly well-suited for deployment in dense urban environments. Their ability to maintain proximity for efficient data collection, from IoT sensors that transmit low power over short distances, makes them ideal for such tasks [21]. Additionally, quadrotors play an essential role in smart city applications, especially in intelligent transportation systems involving intelligent vehicles (IVs) [22]. Their flexibility allows them to support aerial wireless communication for the Internet of Vehicles (IoVs) [23; 24; 25], which function as dynamic IoT nodes. IoVs require high-speed, reliable, low-latency, and stable wireless links [26] to exchange perception data [27] critical for safe self-diving [28]. Device-to-device (D2D) communication is expected to be indispensable in future WCNs for enabling high-speed connectivity, offloading data traffic, and optimizing the capacity of large-scale networks [29]. IVs can leverage vehicle-to-vehicle (V2V) communication to establish vehicular networks [30] and enhance safety features such as collision avoidance for autonomous driving [31]. Additionally, IVs can utilize vehicle-to-everything (V2X) communication for applications related to commercial use, road safety, and traffic efficiency [32]. In this context, drones play a crucial role due to their high mobility and ability to disseminate urgent information swiftly among vehicles. They can also provide aerial LoS communication links for IoVs in obstructed, densely populated urban environments [33].