Abstract
In IEEE 802.11 based wireless networks interference increases as more access points are added. A metric helping to quantize this interference seems to be of high interest. In this paper we study the relationship between the improved attacking case metric, which captures interference, and throughput for IEEE 802.11 based network using directional antenna. The y(1/3) = a + b (ln x)(3) model was found to best represent the relationship between the interference metric and the network throughput. We use this model to predict the performance of similar networks and decide the best configuration a network operator could use for planning his network.

Abstract
A common problem in networking research and development is the duplicate effort of writing simulation and implementation code of routing protocols. This can be avoided by reusing simulation code in real prototyping and in production environments. In ns-3, emulation mode can be used to run simulation models of routing and Software Defined Networking (SDN) protocols on top of real L2 interfaces such as Ethernet and Wi-Fi. Although this feature is already available, the additional packet processing involved degrades the performance of the nodes and limits the amount of network traffic that can be processed. Our proposal to overcome this performance bottleneck consists in moving the data plane processing operations to outside of the ns-3 process, running such operations natively in the host Operating System (OS). Two approaches are proposed: (a) running the data plane in user space (DPU); (b) running the data plane in kernel space (DPK). Both approaches support the emulation of one or multiple nodes per emulation host machine. The experimental results show that the DPU and DPK approaches significantly improve the throughput by respectively 4.9 and 19 times when compared against traditional ns-3 emulation of a single node. For multiple nodes, the DPK approach further improves the throughput by as much as 23 times. The amount of code reuse is high - e.g., for the routing protocols used in this paper, only 1.4% and 11% of extra code is required to benefit from the performance improvements achieved respectively by the DPK and DPU approaches.

Abstract
The high flexibility of the wireless mesh networks (WMNs) physical infrastructure can be exploited to provide communication with different technologies and support for a variety of different services and scenarios. Context information may trigger the need to build different logical networks on top of physical networks, where users can be grouped according to similarity of their context, and can be assigned to the logical networks matching their context. When building logical networks, network virtualization can be a very useful technique allowing a flexible utilization of a physical network infrastructure. Moreover, dynamic resource management using multiple channels and interfaces, directional antennas and power control, is able to provide a higher degree of flexibility in terms of resource allocation among the available virtual networks, to enable isolated and non-interfering communications while maximizing the network efficiency. In this paper we propose a resource management approach that uses transmit power control algorithm with both omnidirectional and directional antennas, to determine the resources of each virtual network while minimizing interference between virtual networks, considering the support of multiple services and users. Each virtual network can be extended to include the nodes of the WMN required by new users. The results of the proposed approach show that the support of multiple virtual networks for multiple services highly improves the network performance when compared to the support of the services in only one virtual network, with no interference minimization nor dynamic resource control.

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
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.