Localization systems play a major role in many applications for Vehicular Ad Hoc Networks (VANETs). One of the most interesting problems to be solved in vehicular networks is how to provide anywhere and anytime highly accurate and reliable localization information. Unique characteristics of VANETs such as mobility constraints, driver’s behavior and the high-speed displacement nature of vehicles cause rapid and constant changes in network topology, leading to dissemination of outdated localization information. To circumvent this problem, an alternative is the use of predicted future locations of vehicles. The main idea of this approach is to use the localization prediction as an extension of a data fusion localization system. In such an approach, a future position of a vehicle is predicted for a given future time and used to take advantage of a future time-space window of a vectorial trajectory rather than a static localization point. In this paper, we discuss this subject by studying and analyzing the use of localization prediction as natural way to improve VANET applications. We survey proposed approaches for localization, target tracking and time series prediction techniques that can be used to estimate the future position of a vehicle. We also highlight their advantages and disadvantages through an analytical discussion visualizing its potential application scenarios in VANETs. We present a set of experiments that show the results of such techniques when applied to a realistic VANET scenario.

Some Inherent Vehicular Ad Hoc Networks (VANETs) characteristics, such as intermittent connectivity, highly dynamic topology, and hard delay constraints, make data communication a challenging task in these networks. Due to such peculiarities, in this work we study the impact of using vehicles predicted locations as a metric for data communication in VANETs. In our proposal, called LPRV (Localization Prediction-based Routing for VANETs), packet forwarding is performed by nodes with predicted future localization closer to the delivery destination, without the need for exchanging additional control message. The proposed algorithm also explores the knowledge of a digital map to limit the scope of message exchanges in the shortest path for vehicles between source and destination. We compared our proposal to both classic Flooding and SIFT (Simple Forwarding over Trajectory) algorithms. Our results clearly demonstrate the efficiency of the proposed solution in different scenarios, especially in terms of delivery rate, number of hops and delay, with a reduced number of message transmissions.

In wireless sensor networks (WSNs), power consumption is an important aspect when designing routing protocols. When compared to other components of a sensor node, the power required by radio transmitters is responsible for most of the consumption. One way to optimize energy consumption is by using energy-aware protocols. Such protocols take into consideration the residual energy information (i.e., remaining battery power) when making decisions, providing energy efficiency through the careful management of energy consumption. In this work, we go further and propose a new routing protocol that uses not only the residual energy information, but also the available renewable energy information from renewable energy sources such as solar cells. We then present the Renewable Energy-Based Routing (REBORN) algorithm, an energy-aware geographic routing algorithm, capable of managing both the residual and the available energy. Our results clearly show the advantages and the efficiency achieved by our REBORN algorithm when compared to other proposed energy-aware approaches.

The occurrence of hole regions in Wireless Sensor Networks is a significant challenge when applying a greedy technique in a geographic routing approach. The local minimum phenomenon is commonly attributed to physical obstacles, energy depletion of the nodes, failures in communication between neighbors, or even the incorrect deployment of the nodes in the sensing field. To address the problem of hole regions, most approaches choose to abandon the traditional greedy forwarding mechanism to temporarily adopt the well-known perimeter routing scheme applied to nearby nodes or along the edge of a region of a hole. However, this mechanism does not satisfy the network load balance requirement, because it imposes too much traffic to the nodes in the hole’s edge, making them overloaded when compared to other network nodes more distant from holes. In this work, we propose a novel location-free geographic routing technique called PAtCH (Proactive Approach to Circumvent Holes in Wireless Sensor Network) to avoid routing holes in WSNs. Our solution can circumvent hole regions and create routing paths toward the destination. We consider that our sink has a higher communication range, and the Received Signal Strength Indicator (RSSI) is used to assist the construction of the routing paths. Our results show the efficiency achieved by our proposed solution in scenarios with hole regions, also maintaining all the benefits of a classic greedy forwarding technique.

O objetivo deste estudo foi avaliar a eficiência do curso on-line ABC da Inteligência Artificial, tendo adotado a Análise do Comportamento como teoria educacional. Participaram 244 pessoas, ao longo de quatro ofertas do curso, desde graduandos em ciências exatas até pós-graduados de outras áreas. Foi aplicada prova de 30 itens de V/F no início (pré) e ao final do curso (pós-teste), para avaliar aprendizagem e autoconfiança em relação às próprias respostas na prova. Avaliamos, ainda, a satisfação com o curso. Observamos aumento estatisticamente significativo de acertos e de autoconfiança entre pré e pós-teste, com tamanhos do efeito de moderado a alto: 0,48 ≥ d ≤ 0,87 (aprendizado) e 0,45 ≥ d ≤ 0,98 (autoconfiança). A satisfação com o curso foi elevada (M = 4,45; DP = 0,10). Esperamos que esse curso seja replicado e seus recursos, principalmente objetivos de aprendizagem e avaliações, possam ser aperfeiçoados, a partir do material gratuitamente disponibilizado na Internet.