Abstract
The growth of Internet of Things (IoT) technologies has triggered the development of low-cost solutions characterised by low energy consumption and low complexity. To interconnect these devices, some wireless communications technologies including IEEE 802.11 and IEEE 802.15.4 have been used due to their deployment and management simplicity and high scalability. However, in scenarios where the devices are physically distant or there is a massive number of devices in a reduced area, cellular technologies such as 3rd Generation Partnership Project (3GPP) Narrowband-Internet of Things (NB-IoT) are seen as the solution. This paper proposes a network planning theoretical model for NB-IoT, named NB-IoT Deterministic Link Adaptation Model (NB-DLAM), which can be used to estimate Quality of Service (QoS) metrics such as Packet Delivery Ratio (PDR), transmission time, and throughput. NB-DLAM estimations were compared with simulation results, which show the accuracy of the proposed model.

Abstract
Unmanned Aerial Vehicles (UAVs) acting as aerial Wi-Fi Access Points or cellular Base Stations are being considered to deploy on-demand network capacity in order to serve traffic demand surges or replace Base Stations. The ability to estimate the Quality of Service (QoS) for a given network setup may help in solving UAV placement problems. This paper proposes a Machine Learning (ML) based QoS estimator, based on convolutional neural networks, which estimates the QoS for a given network by considering the UAV positions, the user positions and their offered traffic. The ML-based QoS estimator represents a novel paradigm for estimating the QoS in aerial wireless networks. It provides fast and accurate estimations with reduced computational complexity. We demonstrate the usefulness and applicability of the proposed QoS estimator using the ideal UAV placement algorithm. Simulation results show the QoS estimator has an average prediction error lower than 5%.

Abstract
The growing demand for broadband communications anytime, anywhere has paved the way to the usage of Unmanned Aerial Vehicles (UAVs) for providing Internet access in areas without network infrastructure and enhancing the performance of existing networks. However, the usage of Flying Multi-hop Networks (FMNs) in such scenarios brings up significant challenges concerning network routing, in order to permanently provide the Quality of Service expected by the users. The problem is exacerbated in crowded events, where the FMN may be formed by many UAVs to address the traffic demand, causing interflow interference within the FMN. Typically, estimating inter-flow interference is not straightforward and requires the exchange of probe packets, thus increasing network overhead. The main contribution of this paper is an inter-flow interference-aware routing metric, named I2R, designed for centralized routing in FMNs with controllable topology. I2R does not require any control packets and enables the configuration of paths with minimal Euclidean distance formed by UAVs with the lowest number of neighbors in carrier-sense range, thus minimizing inter-flow interference in the FMN. Simulation results show the I2R superior performance, with significant gains in terms of throughput and end-to-end delay, when compared with state of the art routing metrics.

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
Autonomous Underwater Vehicles (AUVs) are widely used as a cost-effective mean to carry out underwater missions. During long-term missions, AUVs may collect large amounts of data that usually needs to be sent to shore. An AUV may have to travel several kilometers before reaching an area of interest near the seafloor, thus surfacing is unpractical for most cases. Long-range underwater communications rely mostly on acoustic communications, which are characterized by very low bitrates, thus making the transfer of large amounts of data too slow. GROW is a novel solution for long-range, high bitrate underwater wireless communications between a survey unit (e.g., deep sea lander, AUV) and a central station at surface. GROW combines AUVs as data mules, short-range high bitrate wireless RF or optical communications, and long-range low bitrate acoustic communications for control. In this paper we present the Underwater Data Muling Protocol (UDMP), a communications protocol that enables the control and the scheduling of the Data Mule Units within the GROW framework. Experimental results obtained using an underwater testbed show that the use of UDMP and data mules can outperform acoustic communications, achieving equivalent throughput up to 150 times higher within the typical range of operation of the latter.

Abstract
The Blue Economy has been growing in sectors such as offshore renewable energy, aquaculture, marine biotechnology, and deep sea mining. However, suitable wireless and mobile communications are lacking offshore. On the one hand, there is no coverage from terrestrial networks; on the other hand, satellite communications are still narrowband and expensive. Recently, the use of multi-hop airborne communications has been proposed to extend the coverage from terrestrial networks offshore but the communications range of these solutions is highly dependent on the height of the communications nodes. In this paper, we study the RF signal propagation in the maritime environment when the height of the receiver is changed and propose a position control approach for airborne multi-hop networks that maximizes the network capacity by taking full advantage of the signal reflections on the sea surface. The results obtained show that the proposed approach can provide lower propagation losses and higher network throughputs than random or fixed height approaches.