# Unmanned Vehicles' Placement Optimisation for Internet of Things and Internet of Unmanned Vehicles
## 9 Conclusions  
Through this paper, we emphasised the impact of UAV and USV networks in the Internet of Things ecosystem, and we explored the concept of Internet of Unmanned Vehicles as an extrapolation of the concept of Internet of Drones (or Internet of Unmanned Aerial Vehicles), which was narrower. In addition, we proposed an IoT architecture based on a hybrid network of WSNs, UAVs and USVs (UVs) that have two main roles: (1) to ensure communication between a served WSN and the gateway and (2) to fulfil tasks assigned by a Cloud Center (CC). For the UVs to achieve the first role, we presented the background for the propagation models employed to model the communication link between aerial, aquatic and terrestrial nodes. Based on the theoretical study, we implemented two algorithms that can run on a Cloud server hosted by the Cloud Center. The first one uses, for the first time according to our knowledge, the orthodrome (great circle path) concept to reduce the optimum location searching space and, thus, the complexity of the position optimisation algorithm, which is the second one proposed in the paper. The first algorithm successfully determines the closest UV nodes to specific virtual nodes on the orthodrome. The proposed algorithm gives very good results and it is not influenced by the distance between the gateway and the served WSN. Instead, its execution time is influenced by the number of real nodes (UVs). Nevertheless, for a hypothetical number of 100 UVs, the output of the algorithm is returned in around 0.116 s. After applying the great circle method and its corresponding algorithm, the placement optimisation algorithm determines the number and the optimum positions of UVs such that the communication between the served WSN and gateway is ensured. We proposed concepts such as communication graphs and metrics as the weight of the graph and a performance indicator, \(\sigma\). The latter reveals whether there are more UVs acting as access points than needed or not when the communication is ensured. The algorithm was implemented in MATLAB 2019b. The set of pseudorandom initial coordinates of UVs and the static coordinates of the gateway and of the WSNs correspond to a region in the Danube Delta, but the algorithm is not restricted to this area alone. The proposed optimisation algorithm uses an adaptive step for the distance, reducing its execution time. For example, in the case of a Wi-Fi-based technical solution, the execution time decreases from 9.154 s to 3.285 s when an adaptive distance coefficient \(\rho=0.2\) is used and the distance between the gateway and the served WSN is 2.393 km. When the distance increases to 7.2209 km, the usage of the same adaptive distance coefficient determines the decreasing of the execution time from 24.073 s to 8.982 s. In what concerns the performances of the placement optimisation algorithm when LoRa/LoRaWAN technology is used, on the one hand, we revealed that the algorithm is not necessary for the distances of 2.393 and 7.2209 km tested for Wi-Fi communication. On the other hand, when a technical solution having \(LB=106\) dB is used, the performance indicator of the algorithm decreases, while the execution time increases to 44 s. Even lower performances (high execution time, more UVs acting as relay than needed) are observed when using the technical solution with \(LB=115\). Nevertheless, the goal of the algorithm resides in providing the optimum positions of the UVs such that fast data transfer occurs when low \(LB\), high-throughput communication technology such as Wi-Fi is employed. Additionally, if needed, based on the positions computed and on the UVs' equipment (sensors, power supply etc. ), the Cloud Center can adaptively assign specific monitoring tasks to UVs. Afterwards, we highlighted the main use cases arising from the proposed IoT architecture and algorithm when LoRa/LoRaWAN communication technology alone is employed or when LoRa/LoRaWAN, Wi-Fi and cellular communications are used in the Danube Delta developing region.