# Unmanned Vehicles' Placement Optimisation for Internet of Things and Internet of Unmanned Vehicles
## 2 Unmanned Aerial and Surface Vehicle Networks: Applications and Advancement in Their Optimisation
### UAV and USV Network Enhancements  
Park et al. [7] proposed an algorithm for UAV fleet control to create an ad hoc wireless network that services a Wireless Area Network connecting terrestrial devices. They succeeded in improving the network's throughput based on the control of the position of the drones. Their work aimed to reduce congestion by casting out UAVs. In addition, they evaluated the algorithm in ns-3 on a surface of 500 m \(\times\) 500 m, and they assumed that the terrain was flat. Instead, our algorithm considered the propagation environment and channels and could be applied on larger surfaces and different terrain profiles, because it considered the terrain elevation. In [44], a path planning concept based on environmental conditions (wind speed and direction) was proposed. It exploited the possibility to change the angle between wind force and UAV path. Nevertheless, there are other factors which influence UAV path planning, such as payload and speed. Our work, though, was based on communication parameters and not environmental ones. [PERSON] et al. [45] proposed an algorithm for reconstructing an air-to-ground channel and predicting the signal strength between a single UAV and several ground end users. Through the proposed algorithm, the authors in [45] targetted the optimal UAV-based relay position. Their work did not cover all air-related channels and only employed one UAV. Like our work, they aimed to find the optimum position of the UAV such that it acts as a relay. In comparison with their work, the current paper made use of UVs that can act, in their turn, as access points. In [46], already known air-to-air channel models were revisited and path loss-related simulations were only performed for two UAVs. The work was a precursor initiative of FANET algorithms development. However, most of the employed models (e.g., Egli, Okumura, SUI) have some limitations related to the height and distance ranges. As compared to [46], in our paper, we used UAV propagation models adapted to real-case scenarios. For example, we used the 3 GPP model to highlight the propagation losses regarding communication between ground users and UAVs and between UAVs themselves when they are not in the line-of-sight condition, under a height of 60 m. Moreover, the height and distance ranges were broader and closer to real environments. Several path planning or formation algorithms were developed for USV networks, but all were based on path optimisation or obstacle avoidance [47; 48; 49; 50; 51] in the context of autonomous guidance, ignoring the communication layer. In [52], a communication technology switching algorithm was proposed for shore-to-sea communication. The authors employed a USV equipped with the following technologies: broadband data, satellite communication, shortwave communication, and BeiDou Navigation Satellite System (BDS). Nevertheless, the proposed method was based on a resource allocation approach. Similar to [52], we proposed the switching of the communication technology based on the scenario, i.e., a long-range communication technology (e.g., LoRa/LoRaWAN) is used for minimum low-throughput connectivity between GW and UVs. In contrast, a short-range, high-throughput technology is used to quickly transfer data between a served WSN and UV or between UVs themselves. As opposed to [52], we used a path-loss-based approach and not a resource-allocation-based one. When referring to UAV-USV cooperation, it is worth mentioning again that the existing solutions consider the positioning of UAVs or USVs from different perspectives. One of them is related to the landing and transportation of a UAV by a USV. The positioning algorithm employs the distance data acquired from four ultrasonic sensors. Even though UVs are equipped with communication modules, communication-related data are not used to enhance UVs positioning [12].