In this paper, we focus on two fundamental aspects in the design of Bus-based Vehicular Networks: the building of bus mobility scenarios for validating solutions and the dissemination of messages on the network. We present a methodology for generating bus mobility scenarios based on official data and simulation tools. In this regard, we consider the city’s road map and additional traffic infrastructure elements. In addition, traffic demand for different days of the week is generated based on GTFS data provided by the city’s transport agency. We validate our scenarios with official data, and through a comparative analysis, we show the relevance of this work compared to existing alternatives in the literature. We present a novel routing protocol named BR3C, which aims to forward messages between bus lines for data dissemination. BR3C (Bus Routing protocol based on Community and Centrality Characteristics) considers social metrics extracted from the contacts between bus lines for decision-making. As a result, our protocol significantly reduces delivery latency while keeping delivery ratio values similar to state-of-the-art.

In recent years, we have witnessed the viability of applying cloud computing concepts to the domain of vehicular networks. A basic component of this infrastructure derived from the merge of cloud computing and vehicular networks is a Vehicular Micro Cloud (VMC), also known as vehicular cloudlets. A VMC is a cluster of connected vehicles that share computational resources. Despite being the focus of many studies in recent years, we still do not have a clear understanding of the characteristics of VMCs in large-scale urban scenarios. In this paper, we investigate some fundamental characteristics of stationary and mobile VMCs obtained from a realistic vehicular mobility trace. We characterize the dwell time and the inter-arrival time in stationary VMCs. Also, using statistical modeling, we identify theoretical distributions that best fit these metrics. For mobile VMCs, we reveal how they occur throughout the city along the day, discussing evolution and lifetime aspects.

In this paper, we address a fundamental problem in vehicular networks, which consists of sending messages from a source vehicle to a destination vehicle. This problem becomes even more complex in the absence of fixed infrastructure or any other controlling entity. Although there are some solutions in the literature to work around this problem, they can cause significant network overhead and generate an amount of redundant data. In this regard, we develop a routing protocol that considers individual vehicular mobility as a determining factor for routing decisions. Through simulations using realistic vehicular mobility trace, we have observed that our strategy considerably decreases network overhead and the number of hops between source and destination while maintaining similar values for delivery ratio and latency.

In addition to being one of the primary means of transport, with the advent of sensing and communication technologies, buses belonging to the public transport system have gained a new role in urban centers. They have been applied as a powerful vehicular network that covers an entire city, called BUS-VANET. For the design and validation of solutions for this type of network, the nodes' mobility information is essential. For instance, data from the buses' GPS trajectories can be used to understand the dynamics of encounters between them. This knowledge can be applied to design applications and services for different users, besides providing the necessary information to properly manage this important public transport solution. However, real-world trajectories have several imperfections. In particular, GPS trajectories are heterogeneous, asynchronous, and typically contain a low sample rate. These characteristics impose certain limitations on the use of this dataset in the design of solutions for a BUS-VANET. In this work, we propose a hybrid method of calibrating trajectories based on historical information of trajectories and a road network to overcome these problems. We showed that our method surpasses the state-of-the-art techniques in several perspectives through evaluation with realistic data.

Understanding the mobility of vehicles plays a fundamental role in the design of solutions for intelligent vehicular networks. Considering that different types of vehicles have different characteristics of mobility, we are interested in investigating how bus mobility impacts the formation of these networks. In this sense, we present clear understanding the bus mobility for vehicular networks. Our analysis is based on a real mobility trace of buses from several days of Dublin, Ireland. Particularly, our study reveals key features of a vehicular network obtained from a real bus mobility trace such as the network structure over the days and how the components of the networks are arranged in the space and time. Additionally, we investigate the potential of bus mobility for urban sensing. In summary, due to network fragmentation identified in our analysis, data dissemination mechanisms that use store-carry-and-forward and street-centric routing are more indicated for intelligent vehicular networks based on bus mobility. Moreover, we show how the use of buses as sensors can compose a powerful urban sensing infrastructure.

Understanding mobility is a fundamental task in the design of mobile networking solutions. The adoption of mobility traces is extremely relevant both to obtain a meaningful understanding of mobility and to create realistic simulation scenarios. However, those traces may have different features that lead to conclusions inconsistent with reality and, consequently, impact the performance of the proposed solutions. In this work, we propose a methodology to evaluate mobility traces considering their spatial and temporal aspects. Furthermore, we review real, publicly available, and widely adopted mobility traces and discuss the application them to vehicular networks. The results show that the use of mobility data for vehicular networks is extremely timely, but that they must undergo a process of quality improvement.

In the near future, a significant increase of new services is estimated due to the advent of several heterogeneous devices connected to the Internet, with the capacity to exchange information, collect data and interact with the environment. Therefore, robust network infrastructures will be required to make these services available. The adoption of the Software Defined Networking (SDN) paradigm in Wireless Mesh Networks (WMN) fits in this context, opening up space for new features that optimize the network data flow, such as traffic engineering, flow-based packet forwarding, and interoperability. In this work, a performance analysis of OpenFlow is performed in a multi-interface WMN testbed. Among the experiments, we highlight the impact of heterogeneous mesh routers on the OpenFlow performance in single-channel and multi-channel scenarios, as well as the comparison of OpenFlow with three traditional WMN routing protocols: B.A.T.M.A.N., AODV, and HWMP. The results show a better performance of the OpenFlow in scenarios with homogeneous multi-channel routers in terms of throughput, delivery rate, jitter, and number of lost packets. In addition, the paper introduces problems related to the high overhead of OpenFlow in scenarios with multiple flows, as well as some directions to solve them.

A significant increase in the Internet traffic is expected for future scenarios of smart cities due to the emergence of billions of heterogeneous Internet of Things (IoT) objects with the ability to collect and exchange information. Thus, understanding the appropriate Access Network infrastructures to support such scenarios is indispensable. In this paper, we study the performance of Wireless Mesh Networks (WMN) as an Access Network solution for smart cities. The results describe the behavior of WMN with IoT devices generating different packet sizes and loads in several topologies. The results and discussions provide guidelines for WMN planning in smart cities of the future.

Vehicular ad hoc network (VANET) is a fundamental building block in the design of an Intelligent Transportation System (ITS). Considering the various applications in ITS, a VANET must provide communication solutions in different situations. In particular, we are interested in dealing with situations where the unicast communication problem occurs in sparse network scenarios. In this paper, we shed light on the need for mechanisms that take into account the vehicles' trajectories. We present a characterization that shows the spatiotemporal regularity of the vehicle movement. We propose a new methodology for identifying the spatiotemporal relationship between vehicle trajectories. We create a novel method named ROSTER for unicast routing in sparse VANETs. Simulations results show that the proposed solution considerably reduces message overhead in the network by maintaining compatible levels of delivery rate in comparison with other protocols.

Vehicular networks are seen as the key communication solution for intelligent transportation systems. An essential task for the development of solutions for vehicular networks is to understand aspects related to their communication topology along the time, mainly because it is directly impacted by vehicular mobility. In this sense, a natural question that arises is how can we model the communication topology in order to have a real representation of network connectivity? Particularly, this question becomes even more complex when we consider the dynamic behavior of mobility over time. In the literature, there are some efforts that aim to model the topology of a vehicular network to better understand its dynamics. However, we note that current approaches have limitations in the temporal perspective leading to the loss of important information. In this work, we show the strengths and weaknesses of current approaches in the characterization and analysis of vehicular network topology. In addition, we present how a model derived from the temporal network theory can be applied to capture the dynamics of a large-scale realistic vehicular mobility trace.

In this work we propose, implement, and evaluate Group Regularity Model (GRM), a novel mobility model that accounts for the role of group meetings regularity in human mobility. We show that existing mobility models for humans do not capture the regularity of human group meetings present in real mobility traces. We characterize the statistical properties of such group meetings in real mobility traces and design GRM accordingly. We show that GRM maintains the typical pairwise contact properties of real traces, such as contact duration and inter-contact time distributions. In addition, GRM accounts for the role of group mobility, presenting group meetings regularity and social communities' structure. Finally, we evaluate state-of-art social-aware protocols for opportunistic routing and show that their performance in synthetic traces generated by GRM is similar to their performance in real-world traces.

Simulation is the most frequently adopted approach for evaluating protocols and algorithms for Vehicular Ad hoc Networks (VANETs) and Delay-Tolerant Networks (DTNs). Usually, simulation tools use mobility traces to build the network topology based on the existing contacts between mobile nodes. However, quality of the traces, in terms of spatial and temporal granularity of each entry in the logfile, is a key factor that impacts the network topology directly. Therefore, the reliability of the results depends strongly on the accurate representation of the real network topology by the vehicular mobility model. We show that five widely adopted existing real vehicular mobility traces present gaps, leading to fallible outcomes. In this work, we propose a solution to fill those gaps, leading to more fine-grained traces, which lead to more trustworthy simulation results. We propose and evaluate a data-based solution using clustering algorithms to fill the gaps of real-world traces. In addition, we also present the evaluation results that compare the communication graph of the original and the calibrated traces using network metrics. The results reveal that the gaps do indeed induce network topologies differing from reality, decreasing the quality of the evaluation results. To contribute to the research community, we have made the calibrated traces publicly available, so that other researchers may adopt them to improve their evaluation results.

Vehicular network applications are emerging to bring many benefits to the users during their journey. However, the design and deployment of such applications require studies that provide insightful information about the network formed by vehicles. To this end, in the last years researchers have been characterizing real mobility traces collected from taxis equipped with GPS devices. However, these traces present temporal and spatial gaps, as it was demonstrated in other studies. In this work, we compare the real original traces with their calibrated version (i.e., with no gaps) to show that the existing gaps expressively affect key metrics such as contact duration, inter-contact time, and network capacity. As result, we contribute to the research community by presenting important analysis of real vehicular network topology and by showing the importance of calibrating the traces before evaluating vehicular network applications.

Simulation is the approach most adopted to evaluate Vehicular Ad hoc Network (VANET) and Delay-Tolerant Network (DTN) solutions. Furthermore, the results' reliability depends fundamentally on mobility models used to represent the real network topology with high fidelity. Usually, simulation tools use mobility traces to build the corresponding network topology based on existing contacts established between mobile nodes. However, the traces' quality, in terms of spatial and temporal granularity, is a key factor that affects directly the network topology and, consequently, the evaluation results. In this work, we show that highly adopted existing real vehicular mobility traces present gaps, and propose a solution to fill those gaps, leading to more fine-grained traces. We propose and evaluate a cluster-based solution using clustering algorithms to fill the gaps. We apply our solution to calibrate three existing, widely adopted taxi traces. The results reveal that indeed the gaps lead to network topologies that differ from reality, affecting directly the performance of the evaluation results. To contribute to the research community, the calibrated traces are publicly available to other researchers that can adopt them to improve their evaluation results.

Vehicular Ad hoc NETworks (VANETs) have been widely used to provide data exchange services in Intelligent Transport Systems (ITS), which should facilitate user's routines. At the same time that user's routines are improved, they can also be used as an important source of data for helping the decision-making process of VANET services. These routines can be acquired from vehicle's trajectories through the use of Global Positioning Systems (GPS). In this context, the combination of location-based data and data forwarding methods brings interesting challenges since these topics are not directly related. Therefore, the key idea of this work is to provide a geocast routing mechanism in VANETs based on daily movements of users in their vehicles. The main contribution is related to the delivery rate, which increases in partitioned and sparsely connected networks. The results show the efficiency of our new method to perform opportunistic routing based on location data, which discovers the best routes to forward packets through the network.