This work presents a V2V video transmission system to help on a car-overtaking decision using the IEEE 802.11p/WAVE communication technology. The system consists on a ffmpeg based video encoder that encapsulates the data received from a camera placed in a front vehicle into HTTP Post packets, and forwards the packet to the rear vehicle through the On Board Units (OBUs) installed on both vehicles. The rear vehicle presents the images to the driver using a visualization device. The proposed system was evaluated through real vehicular experiments in two distinct scenarios: urban and highway. Performance studies were focused on delay measurements, re- transmission rate, signal strength and bandwidth consumption related with the traveling speeds and video quality. Results show that the communication delay is higher in the highway scenario, mainly due to the distance between vehicles and the different speeds. However, promising results regarding the maximum delay and the average number of retransmissions foresee important inputs for future services of assisted-driving, in general, and car- overtaking assistance, in particular.

The advances in wireless technologies and the automotive industry lead to the appearance of Vehicular Ad-hoc NETwork (VANET). Consequently, new interests have emerged regarding the introduction of new applications and services for non-urgent content (e.g. videos, ads, touristic information) dissemination for vehicular network environments. The nodes of a VANET are equipped with On-Board Units (OBUs) to enable the communication with other vehicles and to the infrastructure through Road Side Units (RSUs). OBUs enable content dissemination by the mobile nodes while RSUs are static boards deployed along the road or/and strategic locations (e.g. intersections and high buildings). This paper studies and designs how data can be efficiently disseminated using real vehicular boards (RSUs and OBUs) equipped with Dedicated Short Range Communications (DSRC) interfaces in vehicular networks. It evaluates a content distribution strategy (Local Rarest Bundle First (LRBF)) through a laboratory testbed composed by real RSUs and OBUs equipped with IEEE 802.11p (WAVE) communication interfaces. The experimentation results, performed using real vehicular boards, show that the LRBF strategy provides a high delivery of content through the vehicles, with a limited amount of resources.

Content prefetching brings contents close to end users before their explicit requests to reduce the content retrieval time, which is crucial for mobile scenarios, such as vehicular ad-hoc networks (VANETs). In order to make intelligent prefetching decisions, three questions have to be answered: which content should be prefetched, when and where it should be prefetched. This paper answers these questions by proposing a vehicle mobility prediction-based over-the-top (OTT) content prefetching solution. We proposed a vehicle mobility prediction module to estimate the future connected roadside units (RSUs) using data traces collected from a real-world VANET testbed deployed in the city of Porto, Portugal. We designed a multi-tier caching mechanism with an OTT content popularity estimation scheme to forecast the content request distribution. We implemented a learning-based algorithm to proactively prefetch the user content to VANET edge caching at RSUs. We implemented a prototype using Raspberry Pi emulating RSU nodes to prove the system functionality. We also performed large-scale OpenStack experiments to validate the system scalability. Extensive experiment results prove that the system can bring benefits for both end-users and OTT service providers, which help them to optimize network resource utilization and reduce bandwidth consumption.

Low-Power Wide Area Networks (LPWANs) are prominent technologies in the field of the Internet of Things (IoT). Due to the long range capabilities and low energy consumption, Low-Power Wide Area Networks (LPWANs) are the ideal technologies to send small data occasionally. With their unique characteristics, LPWANs can be used in many applications and in different environments such as urban, rural and even indoor. The work in this paper presents a study on the LPWAN LoRa technology, by testing and evaluating its range, signal quality properties and its performance in delivering data. Three distinct scenarios are proposed and tested, one rural and two urban scenarios in Portugal. The maximum communication range achieved was 5660 meters for the rural scenario and around 2000 meters for the urban ones, and we concluded that there are three essential scenario characteristics that influence the achieved results: distance, elevation difference and obstacles in the signal path.

The Internet is increasingly considered an essential and integral part of people's lives. With its massification and the constant increase of the bandwidth offered to the consumers, services like Over-The-Top (OTT) have grown in recent years - e.g. YouTube and Netflix. OTT services refer to the delivery of audio, video and other data over the Internet without network operators' control. This trend leads to Quality-of-Experience (QoE) challenges in the presence of large delays or quality breaks in the visualization of a video stream. To overcome these issues, new approaches are needed to improve the content delivery reception to the users, and consequently, the services' QoE. This paper proposes a novel prefetching mechanism aiming to reduce the content delivery response, bringing the content as close as possible to consumers before being explicitly requested. This approach is based on the distribution of OTT contents in wireless networks, that can be integrated in network equipment: it is able to predict the type and quality of content that consumers connected to - or nearby - an access point may request, and cache it before being requested, thereby improving their quality perception of the service. Given the lack of information in the literature on management and control of proxy caches for embedded systems, the first step is to analyze and compare different proxy cache solutions - Nginx and Squid - and then evaluate two developed prefetching algorithms in mobile scenarios, given the characteristics of wireless networks. The results show that the proposed content-aware prefetching mechanism is capable of boosting the network's performance, from a technical perspective - higher hit-ratios, reduced data transfers and request latency - and from a user perspective, with significant improvements in the Mean Opinion Score (MOS) QoE metric.

Low-Power Wide Area Networks (LPWANs) are a set of technologies that are growing in the field of the Internet of Things (IoT). Due to their long range capabilities and low energy consumption, LPWAN are the ideal technologies to send small data occasionally. With their unique characteristics, LPWANs can be used in many applications and in different environments such as urban, rural and even indoor. The integration of one of the LPWAN technologies, Long Range (LoRa), in a multi-technology data gathering platform is the key objective of this work. This objective is addressed with the following proposed approaches: (1) an organization based in clusters of sensor nodes; (2) a Medium Access Control (MAC) protocol that provides reliable communications through LoRa technology; and finally, (3) a connection manager that selects which wireless technologies should be used simultaneously to forward the sensing data, according to its classification. The evaluation experiments aim to understand which type of parameters can influence the performance of the overall proposed solution, as well as the advantages of a multi-technology approach in a data gathering platform. The results in a real scenario show that the proposed approach is able to achieve simultaneous multi-technology communication while delivering different types of data with reliability and low latency.

Navy communications between mariners, boats and ships can be significantly demanding in terms of quality. Moreover, due to their intermittent connectivity in dynamic scenarios, they require the support of store and forward mechanisms to opportunistically use the available communications and transmit the information related to their operations. However, besides the fact that the communication can be intermittent, it needs to assure a minimum quality whenever communication is possible. This paper proposes an approach for delay tolerant routing based on the quality of the wireless links in navy communications. It measures the quality of the available connections and it uses this information to develop a quality function and a quality-based transitive rule. We propose a delay-tolerant quality-based routing protocol - Quality PRoPHET (Q-PRoPHET), that uses the quality function and transitive rule to determine the best routing decisions. This approach is tested in real indoor and outdoor scenarios that emulate the navy ones. Q-PRoPHET results in better performance than the original PRoPHET in terms of delivery rates, end-to-end delay and packets transmission, which are critical parameters for the communication in navy operations.

The migration of popular Catch-up TV services to modern Over-The-Top (OTT) multimedia delivery infrastructures creates a wide set of scalability challenges which are commonly addressed using Content Delivery Networks (CDNs) relying on caching nodes close to users. The use of general-purpose caching nodes, tailored for generic web content, is far from optimal as it does not consider the particularities of Catch-up TV content, namely its dynamic popularity behavior, superstar effects, and relevance decay, as shown in existing scientific literature. Since caches are limited in size and are relatively small when compared to the whole catalog of available Catch-up TV content, which may contain tens of thousands of TV programs, it is crucial to make the most out of the available resources. To address these issues, this paper proposes a novel content-aware cache replacement algorithm, Most Popularly Used (MPU), capable of taking advantage of content demand forecasts built using machine learning models, to significantly outperform traditional cache replacement policies, such as Least Recently Used (LRU), Least Frequently Used (LFU), and First-In-First-Out (FIFO), and approach the optimal theoretical hit-ratio limits. MPU leverages millions of Catch-up TV request logs to validate its results under realistic conditions.

In decision approaches applied to network control, it is common to use a function that encompasses a set of parameters weighted through empirically defined values. However, these weights are usually not optimized, and the error from the optimal value will be propagated to the decision function. This paper presents a method to determine the best input weight parameters according to a predefined payoff function using a genetic algorithm (GA). We have extended the GA with respect to its key elements, e.g. chromosomes coding scheme and fitness function. The method is quite general and suits any simulation-based optimization problem with multiple discrete input parameters, without requiring the whole system behavior to be expressed into a mathematical formula. The optimization method is tested in our approach for social-aware nodes' association in mobile networks, and the results show that it autonomously searches for the input weight values that lead to the best solution for the association decision, improving the results when compared to empirically defined weights.

Autonomic and distributed management requires the cooperation between the network nodes and the periodical exchange of network information between them. To increase scalability of the information exchange, network nodes need to be associated and grouped through an efficient set of rules, to minimize the amount of control messages flowing in the network. This paper proposes a community-based association between nodes, where nodes in a community are connected through social-based metrics. The proposed social-based metrics are able to join the network nodes through their characteristics and probable interactions, reducing the disconnection between nodes and optimizing the information exchange between them. The obtained results show that the social-based metrics increase stability of the links and provide better resource aggregation, decreasing the required number of messages exchanged between nodes in a distributed management approach.

Wireless mesh networks constitute an emerging technology that is quickly gaining popularity due to its countless advantages in terms of coverage area and low implementation cost. Wireless mesh networks are also capable of self- organization. The eminent advantage of a self-organized network is its ability to perform network control and management, which reduces both the developmental complexity and the need for maintenance of these networks. In this paper, we propose a self-organizing approach for the optimized link state routing protocol (OLSR) in wireless mesh networks, based on the agent technology. Based on the results we obtained, we argue that our architectural self-organization model increases throughput and improves the delay and packet delivery of the overall network, when compared to the original OLSR protocol.

Self-organization in wireless mesh networks is an emergent research area, which is becoming important due to the growing amount of nodes in a network. Consequently, arise the specific problem of manual configuration of individual nodes, as being either impossible or very expensive. So it is interesting the nodes be configured by themselves. In this paper, we propose an efficient architecture for self-organization for wireless mesh networks based on the Optimized Link State Routing Protocol (OLSR) and Ad hoc on demand distance vector (AODV) utilizing the technology of software agents. As relevant results, we argue that applying the self-configuration module proposed, we can increase the throughput of network, improve the delay, network load, decrease the HELLO messages traffic and the metric MPR count, according to the scalability of the network. We conclude by simulations that using the self-configuration mechanism a significantly improved performance of OLSR and AODV protocols in comparison to the original approach.