Allan M. de Souza

2023

1. 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, 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.

2022

1. 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.

2020

1. 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.

2017

1. 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, Sep 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.

2016

1. 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.