# Unmanned Vehicles' Placement Optimisation for Internet of Things and Internet of Unmanned Vehicles
###### Abstract  
Currently, the use of unmanned vehicles, such as drones, boats and ships, in monitoring tasks where human presence is difficult or even impossible raises several issues. Continuous efforts to improve the autonomy of such vehicles have not solved all aspects of this issue. In an Internet of Unmanned Vehicles (IoU) environment, the idea of replacing the static wireless infrastructure and reusing the mobile monitoring nodes in different conditions would converge to a dynamic solution to assure data collection in areas where there is no infrastructure that ensures Internet access. The current paper fills a significant gap, proposing an algorithm that optimises the positions of unmanned vehicles such that an ad hoc network is deployed to serve specific wireless sensor networks that have no other Internet connectivity (hilly/mountainous areas, Danube Delta) and must be connected to an Internet of Things (IoT) ecosystem. The algorithm determines the optimum positions of UV nodes that decrease the path losses below the link budget threshold with minimum UV node displacement compared to their initial coordinates. The algorithm was tested in a rural scenario and 3 rd Generation Partnership Project (3 GPP), free space and two-ray propagation models. The paper proposes another type of network, a Flying and Surface Ad Hoc Network (FSANET), a concept which implies collaboration and coexistence between unmanned aerial vehicles (UAVs) and unmanned surface vehicles (USVs) and several use cases that motivate the need for such a network. algorithm; Internet of Unmanned Vehicles; link budget; LoRa/LoRaWAN; optimisation; path loss; Wireless Sensors Networks; UAV; USV; Wi-Fi +
Footnote †: journal: Sensors  
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network services but also for task/objective fulfilment or mission accomplishment, as they are capable of performing these tasks [1,8]. Such networks bring several benefits: mobility, on-site image processing, tri-dimensional (3D) mapping, etc. In addition, on-demand deployment is possible, thus making new and expensive infrastructure unnecessary if the network is only to be used for a short time [9]. Currently, FANET represents a component of the Vehicular Ad Hoc Network (VANET) which, in turn, is included in the Mobile Ad Hoc Network (MANET). Unlike MANET and VANET, in FANET, the node density is significantly lower [10]. UAV networks' capabilities can be improved by using USVs (Unmanned Surface Vehicles) and surface ad hoc networks (SANET) [11]. Moreover, one important challenge related to UAVs' reliability refers to their limited battery life. Consequently, when a Ground Control Station (GCS) [7] or a Ground Recharging Station (GRS) are not available due to distance or other factors (weather, for instance), USVs are good candidates to assure efficient, on-time battery charging, acting as mobile recharging stations or as landing points [12]. Additionally, UAV battery recharge scheduling algorithms were proposed [13]. UAVs can also integrate these algorithms such that the charging schedule is optimised in terms of energy and distance to the USV charging station. UAVs, in exchange, can assist USVs' navigation by utilising the possibility to gather environmental data from a 3D space [14]. In addition, UAVs can extend the range of USVs' communication. In addition to energy extension cooperation, in large-scale IoT architectures, UAVs and USVs can act both as data and image collectors and as network relays [15] and aggregators, as unmanned vehicles have the great advantage of mobility in comparison to the usual network equipment. Numerous researchers provided algorithms that control UAVs' formation in order to increase the networks' performances [7, 16, 17, 18, 19], but currently, no algorithm that envisions the formation of a UAV through UAV-USV cooperation is available. For data collection, Wireless Sensors Networks (WSNs) can be deployed in this UAV-USV cooperation to monitor and collect environmental data.