Abstract
To properly validate wireless networking solutions we depend on experimentation. Simulation very often produces less accurate results due to the use of models that are simplifications of the real phenomena they try to model. Networking experimentation may offer limited repeatability and reproducibility. Being influenced by external random phenomena such as noise, interference, and multipath, real experiments are hardly repeatable. In addition, they are difficult to reproduce due to testbed operational constraints and availability. Without repeatability and reproducibility, the validation of the networking solution under evaluation is questionable. In this paper, we show how the Trace-based Simulation (TS) approach can be used to accurately repeat and reproduce real experiments and, consequently, introduce a paradigm shift when it comes to the evaluation of wireless networking solutions. We present an extensive evaluation of the TS approach using the Fed4FIRE+ w-iLab.2 testbed. The results show that it is possible to repeat and reproduce real experiments using Network Simulator 3 (ns-3) trace-based simulations with more accuracy than in pure simulation, with average accuracy gains above

Abstract
In wireless networking R&D we typically depend on simulation and experimentation to evaluate and validate new networking solutions. While simulations allow full control over the scenario conditions, real-world experiments are influenced by external random phenomena and may produce hardly repeatable and reproducible results, impacting the validation of the solution under evaluation. 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, the TS approach is not compatible with scenarios where the radio spectrum is shared with concurrent networks, as it does not reproduce their channel occupancy. In this paper, we introduce the InterferencePropagationLossModel and a modified MacLow to allow reproducing the channel occupancy observed in past experiments using Wi-Fi. To validate the proposed models, the network throughput was measured in different experiments performed in the w-iLab.t testbed, controlling the channel occupancy introduced by concurrent networks. The experimental results were then compared with the network throughput achieved using the improved TS approach, the legacy TS approach, and pure simulation, validating the new proposed models and confirming their relevance to reproduce experiments previously executed in real environments. © 2020 ACM.

Abstract
In the past years, INESC TEC has been working on using ns-3 to reduce the gap between Simulation and Experimentation. Two major contributions resulted from our work: 1) the Fast Prototyping development process, where the same ns-3 protocol model is used in a real experiment; 2) the Trace-based Simulation (TS) approach, where traces of radio link qualities and position of nodes from past experiments are injected into ns-3 to achieve repeatable and reproducible experiments. In this paper we present ns-3 NEXT, our vision for ns-3 to enable simulation and experimentation using the same platform. We envision ns-3 as the platform that can automatically learn from past experiments and improve its accuracy to a point where simulated resources can seamlessly replace real resources. At that point, ns-3 can either replace a real testbed accurately (Offline Experimentation) or add functionality and scale to testbeds (Augmented Experimentation). Towards this vision, we discuss the current limitations and propose a plan to overcome them collectively within the ns-3 community. © 2019 ACM.

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.