Geographic routing has been widely studied over the years as an effective solution for Vehicular Ad Hoc Networks (VANETs), especially because of the availability of wireless devices and global positioning system services. Given the unpredictable behavior of VANETs, selecting the next relay node has been proved a very challenging task. Therefore, in order to maintain acceptable network performance, the routing algorithm needs to be carefully designed to adapt to the fast network changes. The Geographic Perimeter Stateless Routing (GPSR) protocol is a widely adopted position-based routing protocol for VANETs, which makes it a good benchmark candidate. In this paper, we analyze the shortcomings of GPSR and propose a new strategy named Path Aware GPSR (PA-GPSR), which includes additional extension tables in the Neighbors' Table to select the best path and bypass the nodes that have delivered such previous packets in recovery mode. Moreover, our proposed algorithm can eliminate packet routing loops avoiding the delivery of the same packet to the same neighbor node. These PA-GPSR features can, for instance, help to overcome link-breakage due to the unavoidable reasons, such as road accidents or dead-end roads. We used the Simulation of Urban MObility (SUMO) and Network Simulator-version 3 (NS-3) platform to compare our proposed algorithm to the traditional GPSR and Maxduration-Minangle GPSR (MM-GPSR) in scenarios varying the number of nodes as well as the number of source-destination pairs. Our results show that the proposed PA-GPSR strategy performed better than the traditional GPSR and MM-GPSR when packet loss rate, end-to-end delay, and network yield are considered as performance metrics.

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Vehicles on cooperative inter-vehicular applications establish a mutual awareness of their presence by periodically broadcasting beacon messages. However, high vehicle density and poorly controlled beaconing lead to congested channel and degradation of system performance. Periodic beaconing may also lower the delivery rate of beacons and other types of messages. In this paper, we describe a beaconing rate control approach considering the density of nodes during beacon forwarding and adjusting the successive beacon delay to mitigate the congestion and maximize the delivery efficiency of beaconing. Our strategy can be adopted for any beacon-based algorithms. Therefore, we selected the widely adopted position-based routing protocol for VANETs known as Geographic Perimeter Stateless Routing (GPSR) to apply the proposed algorithm and evaluate the impact in the performance metrics. Our proposed algorithm shows performance improvement over standard GPSR related to the number of drops caused by collision and beacon load reduction, which keeps the information accuracy.

Several forwarding strategies (FS) were already proposed to leverage packet delivery in information-centric networking (ICN). However, less attention has been given to FS potential to improve quality of service (QoS) in ICN. This work addresses the impact of FS on the performance of voice application, video streaming and file transfer protocol (FTP). Particularly, we investigate the performance gain when distinct FS are tailored to these traffic categories using named data networking (NDN), a prominent ICN architecture. This work also proposes a new forwarding strategy, called inverse pending interests (IPI), which can be used for low-priority traffic. Given that distinct FS are simultaneously applied, we also evaluate their interoperability using three scenarios and observing QoS metrics for the applications mentioned above with different priorities for each traffic. The work was conducted within a multiobjective optimization framework, which allows defining dominant sets of solutions and enriches the analysis when compared to aiming at a single optimal solution. The simulations were performed using the ndnSIM simulator and the results indicate, for example, that multicast and best-route are the most suitable FS for voice application, while the request forwarding algorithm could better serve video streaming. The proposed IPI algorithm is beneficial when low-priority (FTP) traffic is present, and its adoption does not significantly impair other FS.

Software defined radio (SDR) is an important tool for communication research nowadays. It allows for better spectrum utilization and leads to smarter communication systems. Besides, due to its flexibility, research groups have been using SDR hardware platforms for quicker implementation of new algorithms and development of products. Therefore, the availability of flexible and relatively low cost platforms has been increasing and can also be explored in the teaching environment. This work describes the GNU Radio platform and presents some GNU Radio scripts for public use in teaching basic and intermediate level aspects of communications systems. The goal of this initiative is to contribute to the telecommunications community with easyto-follow educational material that is made freely available.

Unmanned Aerial Vehicles (UAVs) became very popular in a vast number of applications in recent years, especially drones with computer vision functions enabled by on-board cameras and embedded systems. Many of them apply object detection using data collected by the integrated camera. However, several applications of real-time object detection rely on Convolutional Neural Networks (CNNs) which are computationally expensive and processing CNNs on a UAV platform is challenging (due to its limited battery life and limited processing power). To understand the effects of these issues, in this paper we evaluate the constraints and benefits of processing the whole data in the UAV versus in an edge computing device. We apply Convolutional Pose Machines (CPMs) known as OpenPose for the task of articulated pose estimation. We used this information to detect human gestures that are used as input to send commands to control the UAV. The experimental results using a real UAV indicate that the edge processing is more efficient and faster (w.r.t battery consumption and the delay in recognizing the human pose and the command given to the drone) than UAV processing and then could be more suitable for CNNs based applications.

The automatic classification (or identification) of modulation schemes and radio access technologies (RAT) find several applications in military and cognitive radio systems. As in other domains, deep learning has been applied to classification problems in telecommunications. As other machine learning approaches, assessing deep learning depends on the available datasets. However, the evaluation of previous work in modulation classification was done only with simulated signals, which may not properly represent realistic scenarios. In this paper, we revisit modulation classification schemes and also conduct experiments in RAT classification. One of the contributions is a new public dataset of digitized signals with LTE and GSM signals, both simulated and digitized. We then compare deep learning with other classifiers and observe that with a more comprehensive set of features than used in recent works, deep convolutional networks do not significanytly outperform other classifiers under the tested conditions. The results also allow to draw conclusions regarding the performance of classifiers under mismatched training and test sets, such as training only with simulated signals and testing with digitized waveforms obtained from commercial mobile networks.