## 5 Related Works  
According to the initial visions of the International Telecommunication Union (ITU), the three fundamental \"wide\" scenarios of usage for 5 GS have been identified and further commonly followed by the industry: Enhanced Mobile Broadband (eMBB), Massive Machine-Type Communications (mMTC), and Ultra-Reliable Low-Latency Communication (URLLC) [90]. However, the AFBW sectors were not indicated among the example applications. The popular and dominating vision of communication services applications for the AFBW sectors, which drives scientific efforts and further start-ups, associates the needs with \"low-end\" Internet of Things (IoT) sensor networks, i.e., mMTC, and works on different approaches are uncommon. This is most likely due to the pragmatism associated with the current capabilities of the existing networks, which determines the prospects for rapid implementation. The framework for the radio network planning of nomadic 5G campus networks to automate and optimize the DL coverage of base stations is presented in [91] and targets the altitudes of the receivers relevant in scenarios for the use cases mentioned above (0.1-1.5-3.5 m). An ad hoc 5G network built on a platform of drones [92] provides agricultural IoT data collection from sensors in rural areas with bad coverage. At the same time, the platform's drones can perform remote crop inspections with onboard cameras and sensors. The optimization method for drone-based relay systems that support agricultural robots with URLLC services has been proposed [93]. The described iterative algorithm allows for the determination of the optimal location (especially altitude) of a drone and the beamwidth of the antenna, changing the power, and varying the block length allocations per robot inside the circular cell while aiming at the minimization of the average overall decoding error. In a 1000-cow dairy farm, the system used for image recognition for RT individual dairy-cow monitoring, behavior analysis, and feeding has been developed using drones supported by cameras and communication over a 5G network [60]. An electronic fence composed of a set of 5G-connected cameras enhanced with image recognition is presented in [59], which enables the RT monitoring and detection of unauthorized access to farms to reduce potential damage or thievery. A fire detection system based on 5G communication using hybrid edge-cloud computing, which applies AI for video-analytic mechanisms, has been presented in [94]. Another approach [95] exploits the tactile internet concept, which provides ultra-low latency with extremely high availability, reliability, and security via mobile edge computing and data transmission over a 5G network, to enable advanced AI-based video stream analysis algorithms for fire detection in uncertain environments with smoke, fog, and snow. A UAV-based platform for forest wildfire detection with onboard laser-ranging, non-contact temperature, and photoelectric sensors, as well as IR and visible light cameras, uses 5G transmission [96]. The algorithms of RT object detection in forests from images acquired by remote 4G/5G-connected cameras have been validated in terms of accuracy and latency [97] within the framework of the 5G Connected Forest project (see below). A cloud-based multi-modal data acquisition and analysis system for urban waterway monitoring is trialled in [98], in which the sensor data and video streams from the multicamera unit are transmitted via a 5G network. A prototype of a solar-powered floating platform with various water quality sensors and a 4G/5G communication module is developed for RT river environment monitoring in [99]. A trial solution for the dynamic monitoring of coastal water quality around islands using an unmanned surface vehicle with onboard sensors exploits a 5G network for C2 and the transmission of the collected data to the server [100]. A concept for an autonomous underwater robot for invasive weed monitoring and harvesting in lakes, which integrates AI, IoT, and 5G URLLC, is presented in [101].