Navegando por Autor "Gomides, Thiago da Silva"
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Item An urban traffic management system based on vehicle cooperation.(2023) Gomides, Thiago da Silva; Grande, Robson Eduardo de; Pereira, Rickson Simioni; Meneguette, Rodolfo Ipolito; Souza, Fernanda Sumika Hojo de; Guidoni, Daniel LudovicoThe next generation of smart cities will rely on Intelligent Transport Systems (ITSs) due to the increased complexity and dynamism of traffic caused by continuous urbanization and population growth. The traditional techniques to deal with these challenges are expensive and have a great impact on people’s lives and, in this scenario, the introduction of computational and technological solutions is necessary. In order to minimize the problems caused by congestion in urban centers, we present Let Me Know!, a traffic management system inspired by the communication among vehicles that allows the request and availability of information related to vehicular traffic. The vehicles request information in order to update a distributed database containing momentary analyzes of road traffic. Through extensive performance analysis, we show our system’s ability to reduce traffic congestion with a low impact on the network.Item Predictive congestion control based on collaborative information sharing for vehicular ad hoc networks.(2022) Gomides, Thiago da Silva; Grande, Robson Eduardo de; Meneguette, Rodolfo Ipolito; Souza, Fernanda Sumika Hojo de; Guidoni, Daniel LudovicoTraffic jams are an essential and continuous challenge in our cities, responsible for socioeconomic and environmental concerns and an ambitious traffic jams management agenda is urgent. The distributed solutions in the literature for Traffic Management Systems (TMS) are heavily based on beacon messages or proactive communication protocols to share vehicular traffic information among vehicles. Thus, these solutions are not scalable when the number of vehicles increases in the network — when there are traffic jams. To overcome these problems, we propose a new VANET-based traffic management system named CoNeCT: Predictive Congestion Control based on Collaborative Information Sharing for Vehicular Ad hoc Networks. CoNeCT’s primary goal is to support vehicles’ collaboration in analyzing, predicting, and managing congestion. The proposed system was designed to decrease the number of messages by using a novel road segment load assessment that improves traffic flow classification. Vehicles aware of traffic conditions share it with their neighbors, and they can also request traffic views whenever necessary. Additionally, vehicles can detect significant traffic variations and predict future traffic conditions to improve roads’ overall traffic conditions, mitigating the congestion before it arises. Results obtained from an extensive performance analysis show CoNeCT’s ability to reduce traffic congestion with a low impact on the wireless communication medium, outperforming the state-of-art systems.Item A robust traffic information management system against data poisoning in vehicular networks.(2022) Pedroso, Carlos Marcelo; Gomides, Thiago da Silva; Guidoni, Daniel Ludovico; Lima, Michele Nogueira; Santos, Aldri Luiz dosAttacks against systems supported by vehicular networks, such as Traffic Information Systems, are more frequent and critical because of the real-time demand and high volume of data. Attacks that decrease data reliability, as data poisoning – DaP, are the most damaging because they severely risk data use. However, in general, vehicular network systems do not implement these features. Hence, this work presents MOVE, an efficient, secure, and VANET-based traffic management system against DaP attacks. MOVE relies on watchdog monitoring and relational consensus for attack detection, achieving efficient data authenticity and high availability. The performance evaluation of MOVE on OMNET++ has reached a detection rate of 90%, false- negative and false-positive rates of 4% and 10%, respectively. MOVE decreases vehicle travel time by up to 40%, and average time on traffic jams by 35%. It increases the average speed by 12% compared to ON-DEMAND.