Abstract
The use of Autonomous Underwater Vehicles (AUVs) is increasingly seen as a cost-effective way to carry out underwater missions. Due to their long endurance and set of sensors onboard, AUVs may collect large amounts of data, in the order of Gbytes, which need to be transferred to shore. State of the art wireless technologies suffer either from low bitrates or limited range. Since surfacing may be unpractical, especially for deep sea operations, long-range underwater data transfer is limited to the use of low bitrate acoustic communications, precluding the timely transmission of large amounts of data. The use of data mules combined with short-range, high bitrate RF or optical communications has been proposed as a solution to overcome the problem. In this paper we describe the implementation and validation of UDMSim, a simulation platform for underwater data muling oriented systems that combines an AUV simulator and the Network Simulator 3 (ns-3). The results presented in this paper show a good match between UDMSim, a theoretical model, and the experimental results obtained by using an underwater testbed when no localization errors exist. When these errors are present, the simulator is able to reproduce the navigation of AUVs that act as data mules, adjust the throughput, and simulate the signal and connection losses that the theoretical model can not predict, but that will occur in reality. UDMSim is made available to the community to support easy and faster evaluation of data muling oriented underwater communications solutions, and enable offline replication of real world experiments.

Abstract
Aerial networks, composed of Unmanned Aerial Vehicles (UAVs) acting as Wi-Fi access points or cellular base stations, are emerging as an interesting solution to provide on-demand wireless connectivity to users, when there is no network infrastructure available, or to enhance the network capacity. This article proposes a traffic aware topology control solution for aerial networks that holistically combines the placement of UAVs with a predictive and centralized routing protocol. The synergy created by the combination of the UAV placement and routing solutions allows the aerial network to seamlessly update its topology according to the users' traffic demand, whilst minimizing the disruption caused by the movement of the UAVs. As a result, the Quality of Service (QoS) provided to the users is improved. The components of the proposed solution are described and evaluated in this article by means of simulation and an experimental testbed. The results show that the QoS provided to the users is significantly improved when compared to the corresponding baseline solutions.

Abstract
Today, wireless networks are operating in increasingly complex environments, impacting the evaluation and validation of new networking solutions. Simulation, although fully controllable and easily reproducible, depends on simplified physical layer and channel models, which often produce optimistic results. Experimentation is also influenced by external random phenomena and limited testbed scale and availability, resulting in hardly repeatable and reproducible results. Previously, we have proposed the Trace-based Simulation (TS) approach to address the problem. TS uses traces of radio link quality and position of nodes to accurately reproduce past experiments in ns-3. Yet, in its current version, TS is not compatible with scenarios where Multiple-In-Multiple-Out (MIMO) is used. This is especially relevant since ns-3 assumes perfectly independent MIMO radio streams. In this paper, we introduce the Trace-based Wi-Fi Station Manager Model, which is capable of reproducing the Rate Adaptation of past Wi-Fi experiments, including the number of effective radio streams used. To validate the proposed model, the network throughput was measured in different experiments performed in the w-iLab.t testbed, considering Single-In-Single-Out (SISO) and MIMO operation using IEEE 802.11a/n/ac standards. The experimental results were then compared with the network throughput achieved using the improved TS and Pure Simulation (PS) approaches, validating the new proposed model and confirming its relevance to reproduce experiments previously executed in real environments. © 2021 ACM.

Abstract
The ability to operate virtually anywhere and carry payload makes Unmanned Aerial Vehicles (UAVs) perfect platforms to carry communications nodes, including Wi-Fi Access Points (APs) and cellular Base Stations (BSs). This is paving the way to the deployment of flying networks that enable communications to ground users on demand. Still, flying networks impose significant challenges in order to meet the Quality of Experience expectations. State of the art works addressed these challenges, but have been focused on routing and the placement of the UAVs as APs and BSs serving the ground users, overlooking the backhaul network design. The main contribution of this paper is a centralized traffic-aware Gateway UAV Placement (GWP) algorithm for flying networks with controlled topology. GWP takes advantage of the knowledge of the offered traffic and the future topologies of the flying network to enable backhaul communications paths with high enough capacity. The performance achieved using the GWP algorithm is evaluated using ns-3 simulations. The obtained results demonstrate significant gains regarding aggregate throughput and delay.

Abstract
The ability to operate anywhere, anytime, as well as their capability to hover and carry cargo on board make Unmanned Aerial Vehicles (UAVs) suitable platforms to act as Flying Gateways (FGWs) to the Internet. The problem is the optimal placement of the FGWs within the flying network, such that the Quality of Service (QoS) offered is maximized. The literature has been focused on optimizing the placement of the Flying Access Points (FAPs), which establish high-capacity small cells to serve the users on the ground, overlooking the backhaul network design, including the FGW placement. The FGW placement problem is exacerbated in highly dynamic flying networks, where the dynamic traffic demand and the movements of the users may induce frequent changes in the placement of the FAPs. The main contribution of this paper is a fast gateway placement (F-GWP) algorithm for flying networks that determines the optimal position of a FGW. With F-GWP, backhaul communications paths with high enough capacity are established between the FAPs and the FGW, in order to accommodate the traffic demand of the users on the ground. Simulation and experimental results show F-GWP is two orders of magnitude faster than its state of the art counterpart, while ensuring the same flying network performance.

Abstract
Autonomous Underwater Vehicles (AUVs) are seen as a safe and cost-effective platforms for performing a myriad of underwater missions. These vehicles are equipped with multiple sensors which, combined with their long endurance, can produce large amounts of data, especially when used for video capturing. These data need to be transferred to the surface to be processed and analyzed. When considering deep sea operations, where surfacing before the end of the mission may be unpractical, the communication is limited to low bitrate acoustic communications, which make unfeasible the timely transmission of large amounts of data unfeasible. The usage of AUVs as data mules is an alternative communications solution. Data mules can be used to establish a broadband data link by combining short-range, high bitrate communications (e.g., RF and wireless optical) with a Delay Tolerant Network approach. This paper presents an enhanced version of UDMSim, a novel simulation platform for data muling communications. UDMSim is built upon a new realistic AUV Motion and Localization (AML) simulator and Network Simulator 3 (ns-3). It can simulate the position of the data mules, including localization errors, realistic position control adjustments, the received signal, the realistic throughput adjustments, and connection losses due to the fast SNR change observed underwater. The enhanced version includes a more realistic AML simulator and the antenna radiation patterns to help evaluating the design and relative placement of underwater antennas. The results obtained using UDMSim show a good match with the experimental results achieved using an underwater testbed. UDMSim is made available to the community to support easy and faster evaluation of underwater data muling oriented communications solutions and to enable offline replication of real world experiments.