Publications | Allan M. de Souza

2023

IEEE CCNC

Compressed Client Selection for Efficient Communication in Federated Learning

Aissa Hadj Mohamed, Nícolas R. G. Assumpçáo, Carlos A. Astudillo, Allan M. Souza, Luiz F. Bittencourt, and Leandro A. Villas

In 2023 IEEE 20th Consumer Communications & Networking Conference (CCNC), Jan 2023

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Federated learning (FL) is a distributed approach that enables collaborative training of a shared machine learning (ML) model for a given task. FL requires bandwidth-demanding communication between devices and a central server, which is a cause of many issues such as communication bottlenecks and scaling in the network. Therefore, we introduce the CCS (Compressed Client Selection) algorithm aimed at decreasing the overall communication costs for fitting a model in the FL environment. CCS employs a biased client selection strategy that reduces the number of devices training the ML model and the number of rounds required to reach convergence. In addition, the compression method Count Sketch is implemented to reduce the overhead in client-to-server communication. A use case on the Human Activity Recognition dataset is performed to evaluate CCS and compare it with other state-of-the-art approaches. Experimental evaluations show that CCS efficiently reduces the overall communication overhead for fitting a model and its convergence in a FL environment. In particular, CCS reduces up to 90% the communication overhead compared to literature approaches while providing good convergence even in scenarios where the data are not-independently and identically distributed among client devices.
Springer JISA

Service Provisioning in Edge-Cloud Continuum: Emerging Applications for Mobile Devices

Diego O. Rodrigues, Allan M. Souza, Torsten Braun, Guilherme Maia, Antonio A. F. Loureiro, and Leandro A. Villas

Journal of Internet Services and Applications, Jun 2023

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SBRC

Dispositivos, Eu Escolho Vocês: Seleção de Clientes Adaptativa para Comunicação Eficiente em Aprendizado Federado

Allan Souza, Luiz Bittencourt, Eduardo Cerqueira, Antonio Loureiro, and Leandro Villas

In Anais do XLI Simpósio Brasileiro de Redes de Computadores e Sistemas Distribuídos, Jun 2023

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O aprendizado federado (Federated Learning – FL) é uma abordagem distribuída para o treinamento colaborativo de modelos de aprendizado de máquina. O FL requer um alto nível de comunicação entre os dispositivos e um servidor central, assim gerando diversos desafios, incluindo gargalos de comunicação e escalabilidade na rede. Neste trabalho, introduzimos DEEV, uma solução para diminuir os custos gerais de comunicação e computação para treinar um modelo no ambiente FL. DEEV emprega uma estratégia de seleção de clientes que adapta dinamicamente o número de dispositivos que treinam o modelo e o número de rodadas necessárias para atingir a convergência. Um caso de uso no conjunto de dados de reconhecimento de atividades humanas é realizado para avaliar DEEV e compará-lo com outras abordagens do estado da arte. Avaliações experimentais mostram que DEEV reduz eficientemente a sobrecarga geral de comunicação e computação para treinar um modelo e promover sua convergência. Em particular, o DEEV reduz em até 60% a comunicação e em até 90% a sobrecarga de computação em comparação com as abordagens da literatura, ao mesmo tempo em que fornece boa convergência mesmo em cenários em que os dados são distribuídos de forma não independente e idêntica entre os dispositivos clientes.
IEEE T-ITS

Mobility and Deadline-Aware Task Scheduling Mechanism for Vehicular Edge Computing

Joahannes B. D. Costa, Allan M. Souza, Rodolfo I. Meneguette, Eduardo Cerqueira, Denis Rosário, Christoph Sommer, and Leandro Villas

IEEE Transactions on Intelligent Transportation Systems, Jun 2023

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Vehicular Edge Computing (VEC) is a promising paradigm that provides cloud computing services closer to vehicular users. In VEC, vehicles and communication infrastructures can form pools with computational resources to meet vehicular services with low-latency constraints. These resource pools are known as Vehicular Cloud (VC). The usage of VC resources requires a task scheduling process. In this case, depending on its complexity, a vehicular service can be divided into different tasks. An efficient task scheduling needs to orchestrate where and for how long such tasks will run, considering the available pools, the mobility of nodes, and the tasks deadline constraints. Thus, this article proposes an efficient VC task scheduler based on an approximation heuristic and resources prediction to select the best VC for each task, called MARINA. MARINA aims to analyze the behavior of vehicles that share their computational resources with the VC and make scheduling decisions based on the mobility (VC availability) of these vehicles. Simulation results under a realistic scenario demonstrate the efficiency of MARINA compared to existing state-of-the-art mechanisms in terms of the number of tasks scheduled, monetary cost, system latency, and Central Processing Unit (CPU) utilization.
WTG

NeuralMatch: Identificando a Similaridade de Clientes baseado em Modelos no Aprendizado Federado

Gabriel Talasso, Allan Souza, and Leandro Villas

In Anais Estendidos do XLI Simpósio Brasileiro de Redes de Computadores e Sistemas Distribuídos, Jun 2023

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O aprendizado federado é uma técnica de aprendizado de máquina distribuído que permite que vários dispositivos colaborem no treinamento de um modelo de dados comum, enquanto preserva a privacidade dos dados do usuário. No entanto, o aprendizado federado apresenta desafios relacionados aos dados não identicamente distribuídos e balanceados, o que pode resultar em modelos menos precisos. Dessa forma, foi proposto o NeuralMatch, um arcabouço para identificar similaridade de modelos para aprendizado federado, capaz de identificar a similaridade entre os clientes sem o compartilhamento de dados. O arcabouço proposto pode ajudar a desenvolver soluções mais eficientes de aprendizado federado para lidar com os problemas de dados não identicamente balanceados e distribuídos.
Ad Hoc networks

Mobility-aware Vehicular Cloud formation mechanism for Vehicular Edge Computing environments

Joahannes B.D. da Costa, Wellington Lobato, Allan M. de Souza, Eduardo Cerqueira, Denis Rosário, Christoph Sommer, and Leandro A. Villas

Ad Hoc Networks, Jun 2023

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Rapid advancements in vehicular technology and increased vehicle modernization have led to the emergence of intelligent and interconnected entities. As a result, the Vehicular Edge Computing (VEC) paradigm has gained prominence. This paradigm enables the provision of cloud computing services close to vehicular users by utilizing the idle computational resources of vehicles to execute tasks that require computing power beyond what is available locally. Aggregating these computational resources in the vehicular context is known as Vehicular Cloud (VCloud) formation. However, leveraging and aggregating these resources poses several challenges due to the dynamic nature of the vehicular environment. One of the main challenges is the efficient selection of vehicles to assume management roles in the distribution of computational power within the group, often referred to as leading vehicles. This research presents a mobility-aware mechanism called PREDATOR to enhance the VCloud formation process. In this mechanism, the Roadside Unit (RSU) provides vehicular mobility predictions, enabling the selection of the most stable vehicles within the RSU coverage area to assume leadership roles in the VCloud. In this context, vehicle stability is associated with a vehicle’s time within the RSU coverage area, known as dwell time. PREDATOR employs a microscopic perspective to select vehicles with the longest dwell time in the VCloud, allowing for efficient management of computational resource utilization. Simulation results have demonstrated that PREDATOR not only increases the VCloud lifetime but also minimizes leader changes, reduces network message exchange, mitigates packet collisions, and facilitates the effective utilization of aggregated vehicular resources compared to state-of-the-art approaches.
SN Compt. Sci.

Automatic Extraction of Conversation Flows from Human Dialogues: Understanding Their Impact to Refine NLP Models

M. F. Sanches, J. M. C. Sá, R. R. Allan M. de Souza, and al.

Spriger Nature Computer Science, Jun 2023

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In recent years, extensive state-of-the-art research has been conducted on natural language processing (NLP) issues. This includes improved text generation and text comprehension models. These solutions are deeply data dependent, as models use high-quality data. The need for more data in a particular language severely restricts the number of available datasets. This investigation proposes methodology for creating conversational datasets (MCCD), designed to extract multi-turn and multi-user conversational datasets. MCCD can obtain data from existing sources and identify multiple answers to the same message to create conversation flows for the extracted datasets. MCCD creates larger datasets suited to question answering (Questions & Answers (QA)) of open-domain conversational agents. In addition, this article proposes a tool based on MCCD to assist future researchers and applications. Our software tool was applied to extract two human conversation datasets. The evaluation of our methodology and resulted datasets was conducted based on the training of a Portuguese NLP model. We explored the outcome models in a classification task, obtaining better results than a state-of-the-art models.
IEEE DCOSS

Resource Aware Client Selection for Federated Learning in IoT Scenarios

Filipe Maciel, Allan M. De Souza, Luiz F. Bittencourt, and Leandro A. Villas

In 2023 19th International Conference on Distributed Computing in Smart Systems and the Internet of Things (DCOSS-IoT), Jun 2023

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Machine learning optimizes performance in many embedded applications. A weak point of many learning solutions is the intensive use of data and computational resources required for training the model. By default, client devices send data to a solution developer’s server to execute the training process in a more computationally powerful environment. However, this approach can compromise the client’s privacy, as data is transmitted to third parties for processing. Federated learning solves this problem by training the model on the client devices, thus without sharing data. The trained models are then aggregated on the server to create a generalized version that can run on every client. The federated learning protocol involves selecting which clients will participate in each training round, with selection criteria focused on maximizing the number of clients per round, controlling fairness, lowering round discards, and managing resources. However, existing selection algorithms neglect the minimization of battery consumption, which is critical in scenarios where clients have limited resources. In this paper we propose a client selection mechanism for a federated learning protocol that considers energy, processing capacity, and network quality as determinant criteria for decision. Compared to a state-of-the-art selection technique, our algorithm saves resources while maintaining the model’s accuracy.
IEEE DCOSS

FedPredict: Combining Global and Local Parameters in the Prediction Step of Federated Learning

Cláudio G. S. Capanema, Allan M. Souza, Fabrício A. Silva, Leandro A. Villas, and Antonio A. F. Loureiro

In 2023 19th International Conference on Distributed Computing in Smart Systems and the Internet of Things (DCOSS-IoT), Jun 2023

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In traditional Federated Learning (FL), such as FedAvg, the main objective is to compute a generalized model applied to all clients. This approach is not effective in the non-IID scenario, where each client has a specific data distribution. As an alternative, personalized FL has proven to be an important research direction for dealing with clients’ particularities. However, part of these solutions must be reexamined when a new client (i.e., a few times trained or never trained) is added to the FL process. To address these problems, we propose FedPredict, a simple but effective federated learning approach that combines global and local (i.e., personalized) model parameters of neural networks, considering their evolution and update levels. This combination is essential because our method is a plugin that operates in the prediction/inference step on the FL client side, which means that there is no modification in the learning process, and it can be coupled with other techniques. Compared to state-of-the-art solutions, FedPredict converges faster while achieving greater accuracy in various scenarios, including when new clients are added.

2022

IEEE ITSM

Enhancing Sensing and Decision-Making of Automated Driving Systems With Multi-Access Edge Computing and Machine Learning

Allan M. Souza, Horacio F. Oliveira, Zhongliang Zhao, Torsten Braun, Antonio A.F. Loureiro, and Leandro A. Villas

IEEE Intelligent Transportation Systems Magazine, Jan 2022

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Emerging self-driving vehicles are now capable of sensing the environment and performing autonomous operations, paving the way to a more efficient, safer, and greener transportation system. On the other hand, emerging technologies such as vehicle-to-everything communications, 5G, and edge computing can expand even more the potential of automated driving vehicles, especially when combined with machine learning techniques. In this article, we explore how these emerging technologies can be used to enhance automated driving systems from different perspectives, such as driving safety and transportation efficiency. We conduct a case study using real-world data to show how these technologies can be used together to provide a more reliable path planning service considering predicted future urban dynamics.
SBRC

NEMESIS: Mecanismo para Formação de Nuvens Veiculares Baseado em Previsão de Mobilidade

Joahannes Costa, Wellington Lobato Junior, Allan Souza, Eduardo Cerqueira, Denis Rosário, and Leandro Villas

In Anais do XL Simpósio Brasileiro de Redes de Computadores e Sistemas Distribuídos, Jan 2022

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Levando em consideração a rápida modernização dos veículos, que os direciona para entidades cada vez mais inteligentes e conectadas, surge o paradigma de Computação de Borda Veicular. Esse paradigma fornece serviços de computação em nuvem mais próximos dos usuários veiculares através da agregação dos recursos de veículos, a qual é denominada formação de nuvens veiculares. No entanto, devido à alta dinamicidade do ambiente veicular, agregar e utilizar esses recursos impõe alguns desafios. O principal desafio é selecionar de forma eficiente quais veículos irão assumir papéis de gerenciamento na utilização do poder computacional, os chamados veículos líderes. Sendo assim, este trabalho apresenta o NEMESIS, um mecanismo baseado em previsão de mobilidade para formação de nuvens veiculares. O NEMESIS seleciona os veículos com maior tempo de permanência nas nuvens para gerenciar o processo de utilização dos recursos. Os resultados de simulação mostraram que o NEMESIS consegue aumentar o tempo de vida das nuvens, minimizar as mudanças de líderes, enviar menos mensagens na rede, o que colabora na diminuição da colisão de pacotes, e por fim, possibilita uma utilização eficiente dos recursos veiculares agregados.
SBRC

Escalonamento de Tarefas Ciente de Contexto para Computação de Borda Veicular

Joahannes Costa, Allan Souza, Rodolfo Meneguette, Eduardo Cerqueira, Denis Rosário, and Leandro Villas

In Anais do XL Simpósio Brasileiro de Redes de Computadores e Sistemas Distribuídos, Jan 2022

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A Computação de Borda Veicular é um paradigma promissor que fornece serviços de computação em nuvem próximo dos usuários veiculares. Veículos e infraestruturas de comunicação podem cooperativamente atender serviços veiculares com restrições de baixa latência através da formação de nuvens veiculares e utilização dos recursos computacionais dessas nuvens, sendo este último processo chamado de escalonamento de tarefas. Um escalonador de tarefas eficiente precisa decidir qual nuvem veicular executará as tarefas, levando em consideração aspectos como mobilidade veicular e requisitos das tarefas. Isso se faz importante para minimizar o tempo de processamento e o custo monetário na utilização do poder computacional. No entanto, as soluções da literatura não consideram esses aspectos contextuais conjuntamente, o que pode degradar a eficiência geral do sistema. Assim, apresenta-se o CARONTE, um escalonador de tarefas que considera aspectos contextuais em seu processo de decisão. Os resultados mostraram que o CARONTE é capaz de escalonar mais tarefas enquanto minimiza custo monetário, atraso de computação e tempo de fila, quando comparado com abordagens do estado da arte.
CTD CSBC

Towards a Personalized Multi-objective Vehicular Traffic Re-routing System

Allan Souza, Leandro Villas, and Torsten Braun

In Anais do XXXV Concurso de Teses e Dissertações, Jan 2022

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Vehicular traffic re-routing is the key to provide better vehicular mobility. However, considering just traffic-related information to recommend better routes for each vehicle is far from achieving the desired requirements of a good Traffic Management System (TMS), which intends to improve mobility, driving experience, and safety of drivers and passengers. In this scenario, context-aware and multi-objective re-routing approaches will play an important role in traffic management, considering different urban aspects that might affect path planning decisions such as mobility, distance, fuel consumption, scenery, and safety. There are at least three issues that need to be handled to provide an efficient TMS, including: (i) scalability; (ii) re-routing efficiency; and (iii) reliability. In this way, this thesis contributes to efficient and reliable solutions to meet future TMSs. The proposed solutions were widely compared with other related works on different performance evaluation metrics. The evaluation results show that the proposed solutions are efficient, scalable, and cost-effective, pushing forward state-of-the-art traffic management systems
IEEE VTC

FLEXE: Investigating Federated Learning in Connected Autonomous Vehicle Simulations

Wellington Lobato, Joahannes B. D. Da Costa, Allan M. de Souza, Denis Rosário, Christoph Sommer, and Leandro A. Villas

In 2022 IEEE 96th Vehicular Technology Conference (VTC2022-Fall), Sep 2022

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Due to the increased computational capacity of Connected and Autonomous Vehicles (CAVs) and worries about transferring private information, it is becoming more and more appealing to store data locally and move network computing to the edge. This trend also extends to Machine Learning (ML) where Federated learning (FL) has emerged as an attractive solution for preserving privacy. Today, to evaluate the implemented vehicular FL mechanisms for ML training, researchers often disregard the impact of CAV mobility, network topology dynamics, or communication patterns, all of which have a large impact on the final system performance. To address this, this work presents FLEXE, an Open Source extension to Veins that offers researchers a simulation environment to run FL experiments in realistic scenarios. FLEXE combines the popular Veins framework with the OpenCV library. Using the example of traffic sign recognition, we demonstrate how FLEXE can support investigations of FL techniques in a vehicular environment.
IEEE ITSC

Efficient Pareto Optimality-based Task Scheduling for Vehicular Edge Computing

Joahannes B. D. Da Costa, Allan M. Souza, Denis Rosário, Christoph Sommer, and Leandro A. Villas

In 2022 IEEE 96th Vehicular Technology Conference (VTC2022-Fall), Sep 2022

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Vehicular Edge Computing is a promising paradigm that provides cloud computing services closer to vehicular users. Vehicles and communication infrastructure can cooperatively provide vehicular services with low latency constraints through vehicular cloud formation and using these computational resources via task scheduling. An efficient task scheduler must decide which cloud will run the tasks, considering vehicular mobility and task requirements. This is important to minimize processing time and, consequently, monetary cost. However, the literature solutions do not consider these contextual aspects together, degrading the overall system efficiency. This work presents EFESTO, a task scheduling mechanism that considers contextual aspects in its decision process. The results show that, compared to state-of-the-art solutions, EFESTO can schedule more tasks while minimizing monetary cost and system latency.
IEEE LATINCOM

A Novel Short-term Vehicle Location Prediction using Temporal Graph Neural Networks

Farimasadat Miri, Alireza A. Namanloo, Allan M. De Souza, and Richard W. Pazzi

In 2022 IEEE Latin-American Conference on Communications (LATINCOM), Nov 2022

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Location prediction is essential for location-based applications such as route recommendation systems, resource allocation optimization in congested areas, congestion avoidance, traffic management, just to mention a few. Knowing approximate vehicle locations can enhance our ability to better manage and prepare the existing mobile edge computing resources in a Vehicular ad hoc network. In this paper, we focus on a graph neural network model to estimate vehicle locations in a timely manner. We first model the vehicle locations based on events that happen between different regions of an area and the car itself. Leveraging our event-based model, we introduce Temporal Location Prediction (TLP) to capture essential features from each node, edges, and neighboring nodes to achieve timely location prediction. Afterwards, instead of using GPS coordinates as input, we demonstrate a new data structure to feed Bidirectional LSTM (BiLSTM) and LSTM for vehicle location prediction in different time intervals. Thus, our main contribution is a network model that utilizes a Temporal Graph neural network for dynamic location prediction. We explain the advantages and disadvantages of each model and how we can improve them. Our experiments on a real dataset show that our model (TLP) outperforms LSTM and BiLSTM in short-term prediction, considering the same scenario and conditions.
ICEIS

MCCD: Generating Human Natural Language Conversational Datasets

Matheus F. Sanches., Jader M. C. de Sá., Allan M. de Souza., Diego A. Silva., Rafael R. de Souza., Julio C. Reis., and Leandro A. Villas.

Nov 2022

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In recent years, state-of-the-art problems related to Natural Language Processing (NLP) have been extensively explored. This includes better models for text generation and text understanding. These solutions depend highly on data to training models, such as dialogues. The limitations imposed by the lack of data in a specific language significantly limit the available datasets. This becomes worse as intensive data is required to achieve specific solutions for a particular domain. This investigation proposes MCCD, a methodology to extract human conversational datasets based on several data sources. MCCD identifies different answers to the same message differentiating various conversation flows. This enables the resulting dataset to be used in more applications. Datasets generated by MCCD can train models for different purposes, such as Questions & Answers (QA) and open-domain conversational agents. We developed a complete software tool to implement and evaluate our proposal. We applied our solution to extract human conversations from two datasets in Portuguese language.

2021

IEEE WCNC

Reinforcement Learning-designed LSTM for Trajectory and Traffic Flow Prediction

Mostafa Karimzadeh, Ryan Aebi, Allan M. de Souza, Zhongliang Zhao, Torsten Braun, Susana Sargento, and Leandro Villas

In 2021 IEEE Wireless Communications and Networking Conference (WCNC), Mar 2021

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Trajectory and traffic flow prediction will play an essential role in Intelligent Transportation Systems (ITS) to enable a whole new set of applications ranging from traffic management to infotainment applications. In this scenario, deep learning approaches such as Recurrent Neural Networks (RNN) and its variant Long Short Term Memory (LSTM) are excellent alternatives due to their ability to learn spatiotemporal dependencies. However, these neural networks tend to be over-complex and hard to design due to the broad set of hyper-parameters. We propose an automated framework to predict future trajectories and traffic flows in urban areas without human interventions. We employ Reinforcement Learning (RL) and Transfer Learning (TL) to generate high-performance LSTM predictors, which is referred as RL-LSTM. In addition, we introduce HERITOR (High ordE r tR affI c convoluTiOn R 1-lstm), a novel deep learning algorithm for traffic flow prediction. Specifically, HERITOR attempts to capture pure spatiotemporal features of urban traffic. The extracted features are fed into the RL-LSTM to realize a high performance LSTM for traffic flow prediction. We examine the proposed trajectory and traffic flow predictors on two real-world, large-scale datasets and observe consistent improvements of 15% - 25% over the state-of-the-art.
Towards a Personalized Multi-objective Vehicular Traffic Re-routing System

Allan Souza, Leandro A Villas, and Torsten Braun

Institut für Informatik, Universitat Bern, Mar 2021

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Vehicular traffic re-routing is the key to provide better vehicular mobility. However, considering just traffic-related information to recommend better routes for each vehicle is far from achieving the desired requirements of a good Traffic Management System (TMS), which intends to improve mobility, driving experience, and safety of drivers and passengers. In this scenario, context-aware and multi-objective re-routing approaches will play an important role in traffic management, considering different urban aspects that might affect path planning decisions such as mobility, distance, fuel consumption, scenery, and safety. There are at least three issues that need to be handled to provide an efficient TMS, including: (i) scalability; (ii) re-routing efficiency; and (iii) reliability. Scalability refers to the ability of the system to deliver the desired performance without carrying about the vehicles’ number or the scenario’s size. On the other hand, re-routing efficiency refers to how good is the traffic management of the solution. Finally, reliability determines how reliable the system computes the routes regarding future changes in the urban dynamics. In this way, this thesis contributes to efficient and reliable solutions to meet future TMSs. The first contribution lies in developing a scalable architecture for traffic management based on distributed and cooperative algorithms for sensing the urban environment, estimating urban aspects, and re-routing vehicles in real-time. The second contribution relies on enabling an efficient multi-objective re-routing based on each user’s preferences. Thus, each user can determine which urban aspects will be chosen to plan its route. Unlike other multi-objective approaches, our solution is non-deterministic, which decreases the chance of creating additional congestion spots since vehicles with similar origin and destination potentially will be re-routed through different routes. This thesis’s last contribution lies in improving the reliability of the routes computed by the TMSs using a route planning algorithm that considers the future changes in the urban dynamics is proposed. The significant advantage of this solution regarding literature solutions is that the system predicts future urban dynamics (i.e., future changes in traffic conditions, safety risks, etc.). Thus, the system knows beforehand when some changes will happen and how long they will last, consequently computing more reliable routes. The proposed solutions were widely compared with other related works on different performance evaluation metrics. The evaluation results show that the proposed solutions are efficient, scalable, and cost-effective, pushing forward state-of-the-art traffic management systems.
PhD Thesis

Towards a personalized multi-objective vehicular traffic re-routing systems= Sistema de roteamento de tráfego veicular multi-objetivo personalizado

Allan Mariano Souza

[sn], Mar 2021

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Vehicular traffic re-routing is the key to provide better vehicular mobility. However, considering just traffic-related information to recommend better routes for each vehicle is far from achieving the desired requirements of a good Traffic Management System (TMS), which intends to improve mobility, driving experience, and safety of drivers and passengers. In this scenario, context-aware and multi-objective re-routing approaches will play an important role in traffic management, considering different urban aspects that might affect path planning decisions such as mobility, distance, fuel consumption, scenery, and safety. There are at least three issues that need to be handled to provide an efficient TMS, including: (i) scalability; (ii) re-routing efficiency; and (iii) reliability. Scalability refers to the ability of the system to deliver the desired performance without carrying about the vehicles’ number or the scenario’s size. On the other hand, re-routing efficiency refers to how good is the traffic management of the solution. Finally, reliability determines how reliable the system computes the routes regarding future changes in the urban dynamics. In this way, this thesis contributes to efficient and reliable solutions to meet future TMSs. The first contribution lies in developing a scalable architecture for traffic management based on distributed and cooperative algorithms for sensing the urban environment, estimating urban aspects, and re-routing vehicles in real-time. The second contribution relies on enabling an efficient multi-objective re-routing based on each user’s preferences. Thus, each user can determine which urban aspects will be chosen to plan its route. Unlike other multi-objective approaches, our solution is non-deterministic, which decreases the chance of creating additional congestion spots since vehicles with similar origin and destination potentially will be re-routed through different routes. This thesis’s last contribution lies in improving the reliability of the routes computed by the TMSs using a route planning algorithm that considers the future changes in the urban dynamics is proposed. The significant advantage of this solution regarding literature solutions is that the system predicts future urban dynamics (i.e., future changes in traffic conditions, safety risks, etc.). Thus, the system knows beforehand when some changes will happen and how long they will last, consequently computing more reliable routes. The proposed solutions were widely compared with other related works on different performance evaluation metrics. The evaluation results show that the proposed solutions are efficient, scalable, and cost-effective, pushing forward state-of-the-art traffic management systems.

2020

IEEE T-ITS

Safe and Sound: Driver Safety-Aware Vehicle Re-Routing Based on Spatiotemporal Information

Allan M. Souza, Torsten Braun, Leonardo C. Botega, Leandro A. Villas, and Antonio A. F. Loureiro

IEEE Transactions on Intelligent Transportation Systems, Sep 2020

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Vehicular traffic re-routing is key to provide better vehicular mobility. However, considering just traffic-related information to recommend better routes for each vehicle is far from achieving the desired requirements of a good Traffic Management System, which intends to improve not only mobility but also driving experience and safety of drivers and passengers. Context-aware and multi-objective re-routing approaches will play an important role in traffic management. However, most of these approaches are deterministic and can not support the strict requirements of traffic management applications, since many vehicles potentially will take the same route, and, thus, degrade the overall traffic efficiency. In this work, we introduce Safe and Sound (SNS), a non-deterministic multi-objective re-routing approach for improving traffic efficiency and reduce public safety risks (based on criminal events) for drivers and passengers. SNS employs a hybrid architecture and a cooperative re-routing approach for improving system scalability and computation efforts. SNS uses a recurrent neural network to both predict future safety risks dynamics and enable a personalized re-routing in which each vehicle decides the risks it wants to avoid. Simulation results revealed that when compared to state-of-the-art approaches, SNS reduces the CPU time of the re-routing algorithm in approximately 99% and decreases the average safety risk for drivers and passengers in at least 30% while keeping efficient traffic mobility.
An adaptive and distributed traffic management system using vehicular ad-hoc networks

Thiago S Gomides, E Robson, Allan M Souza, Fernanda SH Souza, Leandro A Villas, and Daniel L Guidoni

Computer Communications, Sep 2020

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Traffic Management Systems become an important challenge for large cities due to the constant growth of vehicles. As the road mesh does not increase as well as the number of vehicles in the streets, technological solutions for the traffic congestion rise as alternative and easy-to-use applications. This work presents the ON-DEMAND: An adaptive and Distributed Traffic Management System using VANETS. The proposed solution is based on V2V communication and the local view of traffic congestion. During its displacement in a road, the vehicle monitors its traveled distance and the expected one considering a free-flow traffic condition. The difference between these measurements is used to classify a contention factor, i.e., the vehicle perception on the road traffic condition. Each vehicle uses the contention factor to classify the overall congestion level and this information is proactively disseminated to its vicinity considering an adaptive approach. In the case a vehicle does not have the necessary traffic information to estimate alternative routes, it executes a reactive traffic information knowledge discovery. The proposed solution is compared with three literature solutions, named DIVERT, PANDORA and s-NRR. Our results showed that ON-DEMAND presents better results regarding network and traffic congestion metrics.
IEEE ISCC

GIN: Better going safe with personalized routes

Lucas Zanco Ladeira, Allan Mariano Souza, Heitor S. Ramos, and Leandro Aparecido Villas

In 2020 IEEE Symposium on Computers and Communications (ISCC), Jul 2020

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Contextual data characterize distinct regions of the city, allowing them to differentiate them according to security, entertainment, services, among others. Using contextual data to suggest routes helps to understand new aspects of a city that can change users’ perceptions of different routes. The impact of each type of contextual data may vary according to the user’s profile, which is not taken into account in most of the systems proposed by the literature. Besides, it is necessary to consider the behavior of contextual data, which changes according to the type of data. To tackle the problems mentioned above, we propose a route suggestion system with space-time risk, called GIN. The system consists of three modules, namely: identification of contextual windows, context mapping, and route personalization. Moreover, we propose a strategy to decrease the number of route requests to improve system scalability. The results show that the system adapts to sensitive changes in user’s profiles. We obtained promising by using the behavior of contextual data to avoid unnecessary requests. This strategy allowed a reduction of up to 50% of requests made to the system.
SBRC

Vem Tranquilo: Rotas Eficientes baseado na Dinâmica Urbana Futura com Deep Learning e Computação de Borda

Allan Souza, and Leandro Villas

In Anais do XXXVIII Simpósio Brasileiro de Redes de Computadores e Sistemas Distribuídos, Jul 2020

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SBRC

CERVA: Roteamento Contextual para Veículos com Risco Espaço-temporal

Lucas Ladeira, Allan Souza, Thiago Silva, Geraldo Rocha Filho, Maycon Maciel Peixoto, and Leandro Villas

In Anais do XXXVIII Simpósio Brasileiro de Redes de Computadores e Sistemas Distribuídos, Jul 2020

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IEEE CLOUD

PONCHE: Personalized and Context-Aware Vehicle Rerouting Service

Lucas Z. Ladeira, Allan M. Souza, Thiago H. Silva, Richard W. Pazzi, and Leandro A. Villas

In 2020 IEEE 13th International Conference on Cloud Computing (CLOUD), Oct 2020

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The use of contextual data to suggest distinct types of routes helps to understand new aspects of a city that may change the perception of drivers about routes. The impact of these aspects may differ from driver to driver requiring a way to change the suggestion according to the driver’s point of view. Therefore, this paper presents an approach that identifies distinct situations in multiple types of contextual data and proposes a personalized and context-aware vehicle rerouting service called PONCHE. It considers common characteristics found in every dataset of spatiotemporal data to overcome the necessity of processing specific aspects of distinct data types. Regarding personalized service, each driver’s profile is reflected into contextual data type weights considered by the system, i.e., the intensity he/she wants to avoid a contextual region. With that, a driver’s profile may ignore a determined contextual data type. Performance evaluation results show that PONCHE identifies the best routes according to the weights given by a driver. It also improves the quality of contextual information obtained according to traffic, crime, and vehicle crashes. This study takes into consideration contextual data from Austin and Chicago in the USA, enabling comparison with two distinct cities.
IEEE VTC

A Cache Strategy for Intelligent Transportation System to Connected Autonomous Vehicles

Wellington Lobato, Allan M. Souza, Maycon L. M. Peixoto, Denis Rosário, and Leandro Villas

In 2020 IEEE 92nd Vehicular Technology Conference (VTC2020-Fall), Nov 2020

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Traffic congestion is a major problem in metropolitan areas, which inevitably leads to substantial social and economic impacts. In the Connected Autonomous Vehicles (CAVs) context, Intelligent Transportation System (ITS) addresses routing techniques for building an efficient transportation system in an urban environment. In order to improve traffic management, CAVs use real-time traffic data to disseminate faster routes for vehicles. Meanwhile, Cloud Computing is used to manage the traffic congestion situation, but it is not a suitable option for low-latency requirements of autonomous vehicles. Fog-based approaches dealing with traffic congestion found in the literature do not consider the use of caching for a routing scheme. Therefore, we propose a reliable caching mechanism for autonomous vehicle path planning based on Fog Computing, which is called ReCall. ReCall caches real-time traffic information from different regions to dynamically perform route recommendations. The results have shown that ReCall is able to reduce travel time and emissions.

2019

Elsevier Ad Hoc Networks

Vehicular software-defined networking and fog computing: Integration and design principles

Jéferson Campos Nobre, Allan M. de Souza, Denis Rosário, Cristiano Both, Leandro A. Villas, Eduardo Cerqueira, Torsten Braun, and Mario Gerla

Ad Hoc Networks, Nov 2019

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Vehicular Networks (VN) enable the collaboration among vehicles and infrastructure to deliver network services, where usually value-added services are provided by cloud computing. In this context, fog computing can be deployed closer to the users to meet their needs with minimum help from the Internet infrastructure. Software Defined Networking (SDN) might support the use of large-scale fog-enabled VN services. However, the current management of each wireless network that composes the VN has restricted the exploration of fog-enabled VN services. Therefore, the design principles for a VN architecture is still an open issue, mainly because it is necessary to address the diversity of VN fog applications. In this article, we investigate the design principles for fog-enabled Vehicular Software Defined Networking (VSDN) focusing on the perspectives of the systems, networking, and services. We evaluated these design principles in a use case of a traffic management system for a fast traffic accident rescue, using real traffic accident data. Finally, potential research challenges and opportunities for integrated use fog-enabled VSDN are discussed.
Sensors

An Interest-Based Approach for Reducing Network Contentions in Vehicular Transportation Systems

Allan M. Souza, Guilherme Maia, Torsten Braun, and Leandro A. Villas

Sensors, Nov 2019

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Traffic management systems (TMS) are the key for dealing with mobility issues. Moreover, 5G and vehicular networking are expected to play an important role in supporting TMSs for providing a smarter, safer and faster transportation. In this way, several infrastructure-based TMSs have been proposed to improve vehicular traffic mobility. However, in massively connected and multi-service smart city scenarios, infrastructure-based systems can experience low delivery ratios and high latency due to packet congestion in backhaul links on ultra-dense cells with high data traffic demand. In this sense, we propose I am not interested in it (IAN3I), an interest-based approach for reducing network contention and even avoid infrastructure dependence in TMS. IAN3I enables a fully-distributed traffic management and an opportunistic content sharing approach in which vehicles are responsible for storing and delivering traffic information only to vehicles interested in it. Simulation results under a realistic scenario have shown that, when compared to state-of-the-art approaches, IAN3I decreases the number of transmitted messages, packet collisions and latency in up to 95 % , 98 % and 55 % respectively while dealing with traffic efficiency properly, not affecting traffic management performance at all.
Springer JISA

Efficient data dissemination protocol based on complex networks’ metrics for urban vehicular networks

Joahannes B Costa, Allan M Souza, Denis Rosário, Eduardo Cerqueira, and Leandro A Villas

Journal of Internet Services and Applications, Nov 2019

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SBRC

Serviço de Sugestão de Rotas Seguras para Veículos

Lucas Ladeira, Allan Souza, Geraldo Rocha Filho, Thiago Henrique Silva, and Leandro Villas

In Anais do XXXVII Simpósio Brasileiro de Redes de Computadores e Sistemas Distribuídos, Nov 2019

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Springer JISA

Better safe than sorry: a vehicular traffic re-routing based on traffic conditions and public safety issues

Allan M Souza, Torsten Braun, Leonardo C Botega, Raquel Cabral, Islene C Garcia, and Leandro A Villas

Journal of Internet Services and Applications, Nov 2019

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IEEE LATINCOM

Optimizing Infrastructure Placement with Genetic Algorithm: A Traffic Management Use Case

Matheus Ferraroni Sanches, Allan M. Souza, Wellington Lobato, and Leandro A. Villas

In 2019 IEEE Latin-American Conference on Communications (LATINCOM), Nov 2019

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Infrastructure-based approaches are the key to achieve the strict requirements of traffic optimization services. In this scenario, the communication between vehicles and roadside units (RSUs) is essential to empowering the efficiency of traffic management solutions. However, most of vehicular re-routing solutions do not consider the effects of the signal degradation caused by urban obstacles in their system, which may not lead to the desired performance. In this scenario, this work proposes ORC (Optimizing RSU Coverage) an RSU placement optimization based on genetic algorithm considering signal degradation of urban obstacles for traffic management systems. Simulation results have shown that the proposed optimization improves the coverage and the services delivered by the system. In particular, ORC increases the coverage area in at least 15% and improves the vehicular traffic mobility in at least 20% when compared to literature solutions.
IEEE LATINCOM

MARTINI: Towards a mobile and variable time window identification for spatio-temporal data

Lucas Zanco Ladeira, Allan Mariano Souza, Geraldo Pereira Rocha Filho, Thiago Henrique Silva, Matheus Ferraroni Sanches, and Leandro Aparecido Villas

In 2019 IEEE Latin-American Conference on Communications (LATINCOM), Nov 2019

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Understand the time interval that an event is contained is key for different decision making services. For instance, a secure route suggestion needs crime data to identify crime hotspots inside a time window and select safe routes. Time windows help to separate distinct situations and focus the analysis within a time interval. Also, the result may provide an insight into the changes that occur during the day. With this in mind, this paper presents an approach to identify mobile and variable time windows with the goal of discovering hotspots, named MARTINI. The hotspots may be used by different types of services that want the granularity applied in this paper. The data is fragmented with the objective to identify the situation according to each day of the week, data type, and more. MARTINI utilizes a Gaussian Distribution Function to describe the event density of different data types and time intervals. In addition, it uses this representation to find out the changes that occur during the day. The results obtained show that MARTINI requires less time to recognize changes in the situation with a 10 minute sensitivity. In addition, it outperforms the smaller time window even with a 2 hour interval.

2018

SBRC

Protocolo para Disseminação de Dados em VANETs baseado em Métricas de Redes Complexas: Um Estudo de Caso com Sistema de Gerenciamento de Trânsito

Joahannes Costa, Denis Rosário, Allan Souza, Leandro A. Villas, and Eduardo Cerqueira

In Anais do XXXVI Simpósio Brasileiro de Redes de Computadores e Sistemas Distribuídos, Nov 2018

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SBRC

Por Aqui é Mais Seguro: Melhorando a Mobilidade e a Segurança nas Vias Urbanas

Allan Souza, Leonardo C. Botega, Islene Garcia, and Leandro A. Villas

In Anais do XXXVI Simpósio Brasileiro de Redes de Computadores e Sistemas Distribuídos, Nov 2018

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IEEE VTC

Itssafe: An Intelligent Transportation System for Improving Safety and Traffic Efficiency

Allan M. Souza, Lehilton L. C. Pedrosa, Leonardo C. Botega, and Leandro Villas

In 2018 IEEE 87th Vehicular Technology Conference (VTC Spring), Jun 2018

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Recently, many cities are facing challenging mobility and safety issues. The former is commonly related to traffic congestion, as a consequence of uncontrolled population growth and accelerated urbanization. The latter regards to elevated number of city-wide criminal incidents. Several Intelligent Transportation Systems (ITS) were proposed to overcome mobility issues; meanwhile, some safety- based systems were proposed to guide pedestrians and drivers toward safest paths. However, most of these systems tackle only one of the issues. Hence, an ITS can guide vehicles toward risky areas, in order to avoid traffic congestion, while a safety-based system can guide them toward congested roads, focusing on the safety of drivers and passengers. This paper introduces itsSAFE (Intelligent Transportation Systems for improving SAfety and traFfic Efficiency), an ITS which employs accurate knowledge about traffic conditions and unsafety levels on roads for improving the safety of drivers and passengers at the same time it deals with traffic congestion. Simulation results under a realistic scenario have shown that itsSAFE outperformed state-of-the-art approaches that deal with mobility or safety issues, by effectively dealing with traffic efficiency and safety.
IEEE ITSC

Efficient Context-Aware Vehicular Traffic Re-Routing Based on Pareto-Optimality: A Safe-Fast Use Case

Allan M. De Souza, Torsten Braun, and Leandro Villas

In 2018 21st International Conference on Intelligent Transportation Systems (ITSC), Nov 2018

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Vehicular traffic re-routing is the key to provide better traffic mobility. However, considering just traffic-related information to recommend better routes for each vehicle is far from achieving the desired requirements of a good Traffic Management System, which intends to improve not only mobility, but also driving experience and safety of drivers and passengers. Context-aware and multi-objective re-routing approaches will play an important role in traffic management. Yet, most approaches are deterministic and can not support the strict requirements of traffic management applications, since many vehicles potentially will take the same route, consequently degrading overall traffic efficiency. In this way, this work introduces an efficient algorithm based on Pareto-optimality for dealing with such problem. In addition, we focus on the improvement of traffic mobility and public safety during the route recommendation. Thus, methods for building pieces of knowledge about ongoing traffic conditions and risky areas based on city-wide criminal activities are presented. Simulation results have shown that our proposal provides a better trade-off between mobility and safety than state-of-the-art approaches and also avoids the problem of potentially creating different congestion spots.
IEEE DCOSS

FnS: Enhancing Traffic Mobility and Public Safety based on a Hybrid Transportation System

Allan M. Souza, Leonardo C. Botega, and Leandro A. Villas

In 2018 14th International Conference on Distributed Computing in Sensor Systems (DCOSS), Jun 2018

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Recently, many cities are facing mobility and safety issues, commonly related to traffic congestion and the high number of city-wide criminal incidents. Several Intelligent Transportation Systems (ITS) were proposed to overcome mobility issues. Meanwhile, some safety-based systems were proposed to guide pedestrians and drivers toward safest paths. However, most of these systems address only a single issue. Hence, in order to avoid traffic congestion, an ITS may guide vehicles toward risky areas, while a safety-based system may guide them toward congested ones, focusing only on the safety of drivers and passengers. This paper introduces FnS (Faster and Safer), a hybrid ITS which employs accurate knowledge about traffic conditions and unsafety levels on roads for improving the safety of drivers and passengers at the same time it deals with traffic congestion. Simulation results under a realistic scenario have shown that FnS outperformed state-of-the-art approaches that deal with mobility or safety issues.
IEEE ISCC

Data Dissemination Based on Complex Networks’ Metrics for Distributed Traffic Management Systems

Joahannes Costa, Denis Rosário, Allan M. Souza, Leandro A. Villas, and Eduardo Cerqueira

In 2018 IEEE Symposium on Computers and Communications (ISCC), Jun 2018

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With the growth of large urban centers, some problems arise and solutions must be sought to contain them. In this context, traffic congestion is one such problem, where road infrastructure does not follow the high growth in the number of vehicles. Traffic Management Systems (TMS) arise to mitigate traffic-related problems, with automatic detection of slow roadways and vehicle rerouting to avoid such routes. Such applications are supported by the Vehicular Ad hoc NETworks (VANETs), where traffic information are disseminated between the vehicles or central server, and thus the better decisions about traffic management can be made. However, the data dissemination in VANETs is a challenging task, due to the short-range communication and high node mobility. Thus, this paper introduces a protocol for Data Dissemination based on Metrics of Complex Networks, called CRONOS. It provides data dissemination of traffic data with low overhead and high coverage. Simulation results show that CRONOS reduced the number of transmissions by 95%, the congestion time by 48.95%, and travel time by 18.11% for a TMS application.

2017

CTD CSCB

Controle de Congestionamento de Veículos Utilizando Sistemas de Transporte Inteligentes

Allan Souza, and Leandro Villas

In Anais do XXX Concurso de Teses e Dissertações, Jun 2017

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DSN

Traffic management systems: A classification, review, challenges, and future perspectives

Allan M Souza, Celso ARL Brennand, Roberto S Yokoyama, Erick A Donato, Edmundo RM Madeira, and Leandro A Villas

International Journal of Distributed Sensor Networks, Jun 2017

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In cities, where the number of vehicles continuously increases faster than the available traffic infrastructure to support them, congestion is a difficult issue to deal with and it becomes even worse in case of car accidents. This problem affects many aspects of the modern society, including economic development, traffic accidents, increase in greenhouse emissions, time spent, and health damages. In this context, modern societies can rely on traffic management system to minimize traffic congestion and its negative effects. Traffic management systems are composed of a set of application and management tools to improve the overall traffic efficiency and safety of the transportation systems. Furthermore, to overcome such issue, traffic management system gathers information from heterogeneous sources, exploits such information to identify hazards that may potentially degrade the traffic efficiency, and then provides services to control them. With this question in mind, this article presents a classification, review, challenges, and future perspectives to implement a traffic management system.
IEEE ICC

A fully-distributed advanced traffic management system based on opportunistic content sharing

Allan M. Souza, Nelson L. S. Fonseca, and Leandro Villas

In 2017 IEEE International Conference on Communications (ICC), May 2017

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Urban mobility has become one of the most challenging issue in urban centers. As a consequence, traffic congestion has become a daily problem. Several Advanced Traffic Management Systems (ATMS) have been proposed to improve overall traffic efficiency. However, these systems inefficiently exchange traffic information, which can lead to network overload. In order to overcome the mobility problem and improve the efficiency in dealing with vehicle traffic, this paper introduces a fully-distributed advanced traffic management system based on opportunistic content sharing, named PANDORA. Simulation results indicate that PANDORA outperforms the assessed solutions in various scenarios, considering different key requirements of ATMS.
SBRC

GTE: Um Sistema para Gerenciamento de Trânsito Escalável baseado em Compartilhamento Oportunista

Allan M. Souza, Leonardo C. Botega, and Leandro A. Villas

In Anais do XXXV Simpósio Brasileiro de Redes de Computadores e Sistemas Distribuídos, May 2017

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IEEE NCA

Centrality-based data dissemination protocol for vehicular ad hoc networks

Joahannes Costa, Wellington Lobato, Allan M. Souza, Denis Rosário, Leandro A. Villas, and Eduardo Cerqueira

In 2017 IEEE 16th International Symposium on Network Computing and Applications (NCA), Oct 2017

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Vehicular Ad-hoc NETworks (VANETs) are composed of moving vehicles with the ability to process, store, and communicate via wireless medium. VANETs promise a wide scope of services, such as, safety and security, traffic efficiency, and others. For instance, a VANET application can detect, control and reduce traffic congestion based on data that describes traffic patterns. However, disseminating data in VANET is a challenging task, due to its particular characteristics, i.e., heterogeneous density, short-range communication, and node mobility. Since, existing protocols for data dissemination do not effectively address the high overhead, in this paper, we proposed a Data Dissemination protocol Based on Centrality (DDBC) for urban scenarios. The simulation results show that DDBC protocol offers good efficiency in terms of delays and overhead, while achieve network coverage around 90%.

2016

IEEE ISCC

Real-time path planning to prevent traffic jam through an intelligent transportation system

Allan M. Souza, Roberto S. Yokoyama, Guilherme Maia, Antonio Loureiro, and Leandro Villas

In 2016 IEEE Symposium on Computers and Communication (ISCC), Jun 2016

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Congestion is a major problem in large cities. One of the main causes of congestion is the sudden increase of vehicle traffic during peak hours. Current solutions are based on perceiving road traffic conditions and re-routing vehicles to avoid the congested area. However, they do not consider the impact of these changes on near future traffic patterns. Hence, these approaches are unable to provide a long-term solution to the congestion problem, since when suggesting alternative routes they create new bottlenecks at roads closer to the congested one, thus just transferring the problem from one point to another. With this issue in mind, we propose an intelligent traffic system called CHIMERA, which improves the overall spatial utilization of a road network and also reduces the average vehicle travel costs by avoiding vehicles from getting stuck in traffic. Simulation results show that our proposal is more efficient in forecasting congestion and is able to re-route vehicles appropriately, performing a proper load balance of vehicular traffic.
ACM MSWiM

A Fully-Distributed Traffic Management System to Improve the Overall Traffic Efficiency

Allan Mariano Souza, and Leandro Aparecido Villas

In Proceedings of the 19th ACM International Conference on Modeling, Analysis and Simulation of Wireless and Mobile Systems, Jun 2016

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In recent years, the number of vehicles has increased faster than the available infrastructure. Consequently, traffic congestion has become a daily problem affecting several aspects of modern society, including regional economic development. In this way, Traffic Management System (TMS) have been proposed to improve the traffic efficient and minimize traffic congestion problems. These systems rely on gather traffic-related data in a central entity to identify congestion and suggest alternative routes. However such approach adds load in communication channel depending on the traffic density. In this way, this paper introduces FASTER, a fully-distributed TMS to improve the overall vehicle traffic efficiency that does not overloads the communication channel, providing a suitable distributed solution. Simulation results indicate that our FASTER outperforms the assessed solutions in different scenarios and in different key requirements of TMS.
IEEE VTC

SPARTAN: A Solution to Prevent Traffic Jam with Real-Time Alert and Re-Routing for Smart City

Allan M. Souza, Azzedine Boukerche, Guilherme Maia, Eduardo Cerqueira, Antonio A. F. Loureiro, and Leandro A. Villas

In 2016 IEEE 84th Vehicular Technology Conference (VTC-Fall), Sep 2016

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As an important component of Smart Cities, transportation system plays a critical role to address the sustainability and mobility of the society. One key concern is that the number of vehicles continuously increases faster than the available infrastructure, as well as the traffic congestion is a difficult issue to deal with. Several solutions to Intelligent Transportation Systems (ITS) have been proposed to identify congestion and re-route the vehicles afterwards. In this direction, this work introduces SPARTAN, a fully distributed ITS solution, which notifies drivers about congested areas through Vehicle-to-Vehicle communication and employs a real-time decision making mechanism used to reroute vehicles to avoid the congested areas. Simulation results show the effectiveness of SPARTAN in calculating new routes and disseminating them to vehicles that approaching a congestion area. As a consequence, SPARTAN reduces the travel time and the congestion time in urban scenarios when compared to existing approaches.
Elsevier COMNET

ICARUS: Improvement of traffic Condition through an Alerting and Re-routing System

Allan M. de Souza, R.S. Yokoyama, Azzedine Boukerche, Guilherme Maia, Eduardo Cerqueira, Antonio A.F. Loureiro, and Leandro Aparecido Villas

Computer Networks, Sep 2016

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In cities, where the number of vehicles continuously increase faster than the available infrastructure to contain them, traffic congestion is a difficult issue to deal with. This problem becomes even worse in case of accidents and affects many aspects of the modern society, including economic development, accidents, CO (Carbon monoxide) emission, trip time, and health. Several solutions for Traffic Management System (TMS) have been proposed to identify congestions and re-route the vehicles afterward. To this end, they exchange messages periodically between vehicles and central server, what can cause an overhead in the communication channel. In this scenario, it is important to identify the source of the problem and inform the drivers of new routes before the congestion takes place with, considering the limitations of vehicular communication. This work introduces ICARUS, a distributed and pro-active Traffic Management System, which receives notifications about a traffic events then it can calculates new routes, and, then, notifies drivers to follow new paths pro-actively by using inter-vehicle communications. Simulation results show the effectiveness of ICARUS in calculating new routes and disseminating them to vehicles approaching a congested area. Hence, ICARUS reduces the travel time, fuel consumption, and CO emissions of vehicles in urban environments when compared to existing approaches. In addition, ICARUS reduces the broadcast storm problem and maximizes the data dissemination capabilities with short delays and low overhead.

2015

IEEE ISCC

An intelligent transportation system for detection and control of congested roads in urban centers

Celso A. R. L. Brennand, Allan M. Souza, Guilherme Maia, Azzedine Boukerche, Heitor Ramos, Antonio A.F. Loureiro, and Leandro A. Villas

In 2015 IEEE Symposium on Computers and Communication (ISCC), Sep 2015

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Traffic jams frustrate drivers and cost billions per year in time and fuel consumption. In order to avoid such problems, this paper presents an intelligent transportation system that collects real-time traffic information and is able to detect and manage traffic congestion based on this information. Simulation results show that the proposed protocol can reduce the average travel time, CO2 emission and fuel consumption. In particular, the average travel time was reduced in approximately 23%, the average fuel consumption in 9% and average CO2 emission in 10%.
IEEE CIT

Scorpion: A Solution Using Cooperative Rerouting to Prevent Congestion and Improve Traffic Condition

Allan Mariano Souza, Roberto Sadao Yokoyama, Leonardo Castro Botega, Rodolfo Ipolito Meneguette, and Leandro Aparecido Villas

In 2015 IEEE International Conference on Computer and Information Technology; Ubiquitous Computing and Communications; Dependable, Autonomic and Secure Computing; Pervasive Intelligence and Computing, Oct 2015

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Most large cities suffer with congestion problem, one of the main causes of congestion is the sudden increase of vehicle traffic during peak hours, mainly in areas with bottlenecks. Current solutions in the literature are based on perceiving road traffic conditions and re-routing vehicles to avoid the congested area. However, they do not consider the impact of thesechanges on near future traffic patterns. Hence, these approachesare unable to provide a long-term solution to the congestion problem, since when suggesting alternative routes, they create new bottlenecks at roads closer to the congested one, thus just transferring the problem from one point to another one. With this issue in mind, we propose an intelligent traffic cooperative routing application called SCORPION, which improves the overall spatial utilization of a road network and also reduces the average vehicletravel costs by avoiding vehicles from getting stuck in traffic. Simulation results show that our proposal is able to forecasting congestion and re-route vehicles properly, performing a load balance of vehicular traffic.
IEEE CIT

GARUDA: A New Geographical Accident Aware Solution to Reduce Urban Congestion

Allan Mariano Souza, Roberto Sadao Yokoyama, Nelson Luis Saldanha Fonseca, Rodolfo Ipolito Meneguette, and Leandro Aparecido Villas

In 2015 IEEE International Conference on Computer and Information Technology; Ubiquitous Computing and Communications; Dependable, Autonomic and Secure Computing; Pervasive Intelligence and Computing, Oct 2015

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In cities where the number of vehicles continuously increases faster than the available infrastructure, traffic congestion is a difficult issue to deal with. This problem becomes even worse in case of accidents that affects many aspects of the modern society, including economic development and CO2 emission. Several solutions for Intelligent Transportation Systems have been proposed to identify congestions and re-route the vehicles afterwards. This work introduces GARUDA, a fully distributed and pro-active intelligent traffic information system, which avoid congestions, calculates new routes, and notifies drivers to follow new paths proactively. Simulation results show the effectiveness of GARUDA. When compared to original vehicular mobility trace, GARUDA reduces the average trip time in approximately 53%, the overall CO 2 emission in 26% and the fuel consumption in 28%.
IEEE

A new Solution based on Inter-Vehicle Communication to Reduce Traffic jam in Highway Environment

Allan Souza, and Leandro Aparecido Villas

IEEE Latin America Transactions, Mar 2015

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Traffic congestion is an urban mobility problem, which generates stress to drivers and economic losses. In 2012, greenhouse gas emissions from transportation accounted for about 28% of total U.S. greenhouse gas emissions. Intelligent transportation systems can assist in the identification and reduction of vehicular traffic congestion. In this context, this work proposes an intelligent traffic information system based on inter-vehicle communication to avoid vehicle traffic congestion. The main goal of the proposed solution is to decrease CO2 emissions, the average trip time and fuel consumption by avoiding congested roads. Simulation results show that our proposed solution can reduce the average trip time, and the overall CO2 emission and fuel consumption. In particular, the trip time was decreased approximately 86%, the fuel consumption 40% and the CO2 emission 55%. This shows the potential of the proposed solution.
IEEE NTMS

Minimizing traffic jams in urban Centers using vehicular ad hoc networks

Allan M. Souza, R. S. Yokoyama, Guilherme Maia, Antonio A. F. Loureiro, and Leandro A. Villas

In 2015 7th International Conference on New Technologies, Mobility and Security (NTMS), Jul 2015

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Traffic jam in urban centers is a mobility problem responsible for significant economic losses and the degradation of the environment. However, a wide range of technologies, in particular, intelligent transportation systems, may be used to predict, identify and reduce vehicular traffic congestion. In this context, this work presents an Intelligent Traffic Information System (ITIS) which relies on inter-vehicle communication in order to avoid traffic jams in urban centers. The main goal of the proposed system is to reduce the average trip time, the fuel consumption and CO2 emissions, thus increasing vehicular traffic efficiency, reducing economic losses and making cities more environmental friendly. Simulation results for a synthetic scenario have shown that our proposed system is able to accomplish this goal. In particular, the average trip time was decreased ≈47%, the fuel consumption ≈22% and the CO2 emission ≈24%.
SBRC

Co-op: Uma solução para a detecção, classificação e minimização de congestionamentos de veı́culos utilizando roteamento cooperativo

AM Souza, Daniel Guidoni, Leonardo C Botega, and Leandro A Villas

Simpósio Brasileiro de Redes de Computadores e Sistemas Distribuı́dos-(SBRC), Jul 2015

2014

IEEE NCA

ADD: A Data Dissemination Solution for Highly Dynamic Highway Environments

Allan M. Souza, Guilerme Maia, and Leandro A. Villas

In 2014 IEEE 13th International Symposium on Network Computing and Applications, Jul 2014

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ACM MobiWac

Decreasing Greenhouse Emissions through an Intelligent Traffic Information System Based on Inter-Vehicle Communication

Allan Mariano de Souza, Azzedine Boukerche, Guilherme Maia, Rodolfo Ipolito Meneguette, Antonio A.F. Loureiro, and Leandro Aparecido Villas

In Proceedings of the 12th ACM International Symposium on Mobility Management and Wireless Access, Jul 2014

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Traffic congestion is an urban mobility problem, which generates stress to drivers and economic losses. In 2012, greenhouse gas emissions from transportation accounted for about 28% of total U.S. greenhouse gas emissions. Intelligent transportation systems can assist in the identification and reduction of vehicular traffic congestion. In this context, this work proposes an intelligent traffic information system based on inter-vehicle communication to avoid vehicle traffic congestion. The main goal of the proposed solution is to decrease CO2 emissions, the average trip time and fuel consumption by avoiding congested roads. Simulation results show that our proposed solution can reduce the average trip time, and the overall CO2 emission and fuel consumption. In particular, the trip time was decreased approximately 86%, the fuel consumption 40% and the CO2 emission 55%. This shows the potential of the proposed solution.