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
The automotive industry is currently focusing on automation in their vehicles, and perceiving the surroundings of an automobile requires the ability to detect and identify objects, events and persons, not only from the outside of the vehicle but also from the inside of the cabin. This constitutes relevant information for defining intelligent responses to events happening on both environments. This work presents a new method for in-vehicle monitoring of passengers, specifically the task of real-time face detection in thermal images, by applying transfer learning with YOLOv3. Using this kind of imagery for this purpose brings some advantages, such as the possibility of detecting faces during the day and in the dark without being affected by illumination conditions, and also because it's a completely passive sensing solution. Due to the lack of suitable datasets for this type of application, a database of in-vehicle images was created, containing images from 38 subjects performing different head poses and at varying ambient temperatures. The tests in our database show an AP50 of 99.7% and an AP of 78.5%.

Abstract
The present demographic trends point to an increase in aged population and chronic diseases which symptoms can be alleviated through rehabilitation. The applicability of passive 3D reconstruction for motion tracking in a rehabilitation context was explored using a stereo camera. The camera was used to acquire depth and color information from which the 3D position of predefined joints was recovered based on: kinematic relationships, anthropometrically feasible lengths and temporal consistency. Finally, a set of quantitative measures were extracted to evaluate the performed rehabilitation exercises. Validation study using data provided by a marker based as ground-truth revealed that our proposal achieved errors within the range of state-of-the-art active markerless systems and visual evaluations done by physical therapists. The obtained results are promising and demonstrate that the developed methodology allows the analysis of human motion for a rehabilitation purpose.

Abstract
Wi-Fi networks are becoming more and more ubiquitous and represent a substantial source of energy consumption around the globe, mainly when it comes to Access Points (APs). There has been some work done on the characterization of the power consumption of Wi-Fi APs and network interface cards (NICs), and the power usage of these devices under different configurations and standards but mostly using legacy standards. A detailed AP power consumption analysis, exploring the whole set of degrees of freedom and capabilities of these devices is lacking in the state of the art. In this paper, we present a thorough power consumption analysis, covering the configuration options available in enterprise Wi-Fi APs from the three major vendors on the market. The goal is to understand how the power consumption of an AP varies with the different configurations, and provide insights on the parameters that significantly affect the AP power consumption. The obtained experimental results confirm previous state-of-the-art conclusions but contradict some of the studies and results found in the literature, while updating results and conclusions taken in the past to the most recent standards, configurations, and data rates available today. The analysis provided herein is a valuable source of information for deriving new AP power consumption models and designing energy-efficient Wi-Fi networks.

Abstract
Wi-Fi networks lack energy consumption management mechanisms. In particular, during nighttime periods, the energy waste may be significant, since all Access Points (APs) are kept switched on even though there is minimum or null traffic demand. The fact that more than 80% of all wireless traffic is originated or terminated indoor, and served by WiFi, has led the scientific community to look into energy saving mechanisms forWi-Fi networks. State of the art solutions address the problem by switching APs on and off based on manually inserted schedules or by analyzing real-time traffic demand. The first are vendor specific; the second may induce frequent station (STA) handoffs, which has an impact on network performance. The lack of implementability of solutions is also a shortcoming in most works. We propose an algorithm, named Energy Consumption Management Algorithm (ECMA), that learns the daytime and nighttime periods of the Wi-Fi network. ECMA was designed having in mind its implementability over legacyWi-Fi equipment. At daytime, the radio interfaces of the AP (2.4 GHz and 5 GHz) are switched on and off automatically, according to the traffic demand. At nighttime, clusters of APs, covering the same area, are formed, leaving one AP always switched on for basic coverage and the redundant APs swichted off to maximize energy savings, while avoiding coverage and performance hampering. Simulation results show energy savings of up to 50% are possible using the ECMA algorithm.