Andre Luiz Lins Aquino

This study aims to optimize vehicular user association to base stations in a mobile network. We propose an efficient heuristic solution that considers the base station average handover frequency, the channel quality indicator, and bandwidth capacity. We evaluate this solution using real-world base station locations from São Paulo, Brazil, and the SUMO mobility simulator. We compare our approach against a state of the art solution which uses route prediction, maintaining or surpassing the provided quality of service with the same number of handover operations. Additionally, the proposed solution reduces the execution time by more than 80\% compared to an exact method, while achieving optimal solutions.

The rapid urbanization growth has underscored the need for innovative solutions to enhance transportation efficiency and safety. Intelligent Transportation Systems (ITS) have emerged as a promising solution in this context. However, analyzing and processing the massive and intricate data generated by ITS presents significant challenges for traditional data processing systems. This work proposes an Edge-based Data Lake Architecture to integrate and analyze the complex data from ITS efficiently. The architecture offers scalability, fault tolerance, and performance, improving decision-making and enhancing innovative services for a more intelligent transportation ecosystem. We demonstrate the effectiveness of the architecture through an analysis of three different use cases: (i) Vehicular Sensor Network, (ii) Mobile Network, and (iii) Driver Identification applications.

Intelligent Transportation Systems (ITS) involve integrating information and communication technologies with traffic infrastructure and vehicles to support the development of more sustainable transportation systems. However, ITS face security, reliability, and efficiency challenges in storage and sharing real-time critical data. To address these issues, we propose an architecture based on edge computing and blockchain to enable secure data storage and sharing for ITS. By leveraging edge computing capabilities and blockchain's distributed ledger technology, our architecture enhances data security, ensures data integrity, and improves real-time data processing in ITS. We analyze a smart parking system application scenario, and the results demonstrate a significant reduction in average latency and storage usage, highlighting the positive impact of our solution on enhancing the overall performance and reliability of ITS.

This work proposes a centrality-based approach to identify data offloading points in a VSN. The solution presents a scheme to select vehicles used as aggregation points to collect and aggregate other vehicles’ data before uploading it to processing stations. We evaluate the proposed solution in a realistic simulation scenario derived from data traffic containing more than 700,000 individual car trips for 24 hours. We compare our approach with both a reservation-based algorithm and the optimal solution. Our results indicate an upload cost reduction of 30.92% using the centrality-based algorithm and improving the aggregation rate by up to 10.45% when considering the centralized scenario.

This research paper presents a Virtual Machine Placement algorithm designed to optimize the service allocation process, which is one of the most significant challenges in resource allocation and management. Our solution employs a ranking strategy based on global resource scarcity, where we consider all the information contained in datacenter hosts during the allocation process. We compare our approach with two different literature-based methods regarding task allocation time and the number of allocated resources. To evaluate our proposal, we consider scenarios in which requisitions arrive at different times or simultaneously. The first scenario represents a realistic situation where no knowledge is available regarding task arrival. Meanwhile, the second scenario represents a dense situation where many tasks arrive together. Our evaluations show that our solution yields better results in both cases, especially when requisitions arrive simultaneously. Our solution reduced the allocation time by around 25% on average and achieved better load distribution among hosts.

This work presents an interdisciplinary assessment examining air quality tracking in urban environments. This application is well suited to be approached with wireless sensor networks' paradigm in their overall variations. The proposed modeling application takes advantage of Vehicle Sensor Networks (VSN) by embedding sensor nodes in public transportation, addressing this study case with bus lines so that the mobiles spread the sampling activity through many different places visited during the route. Simultaneously, it alleviates power management restrictions, packaging dimensions (size and weight), and general maintenance issues. We perform environmental modeling based on real data considering temporal and spatial multivariate behavior on observed phenomena. We consider the city of São Paulo in our case study and parse the asserted data to create a multivariate map of samples, showing the behavior of five different air pollutants from fossil-fueled vehicles (CO, O3, PM10, NO2 and SO2) simultaneously while it also varies in time. Furthermore, the experiment considers a detailed description of roads, bus lines, vehicle itineraries, and general traffic information. The input data that has unformatted or missing information due to being sourced from real sensors is handled to create the map mentioned above. Our methodology addresses the following: 1) the mentioned environmental simulation, 2) the deployment of mobile sensor nodes and performing sensing process, 3) the implementation of network activity and delivery of collected data, 4) visualization of monitored environment based on gathered data using Voronoi Diagrams to fill blank data at non-reached areas. Finally, our VSN-based approach improved 126 times lower error and 11 times higher coverage compared to conventional monitoring with air quality stations.

TheMinimum Broadcast Time(MBT) is a well‐known data dissemination problem whose goal is to find a broadcast scheme that minimizes the number of steps needed to execute the broadcast operation. The problem has many applications in distributed systems and, in particular, the Industry 4.0 domain. Because Industry 4.0 applications rely primarily on the use of large‐scale machine to machine communications, they need data dissemination techniques that combine high reliability with low communication latency. This work proposes a Biased Random‐Key Genetic Algorithm and a matheuristic for the MBT. We carry out experiments with our algorithms on instances commonly used in the literature (hypercube, shuffle exchange, cube‐connected cycles, de Bruijn, Harary graphs), and also on massive synthetic instances (up to 1000 vertices), allowing to cover many possibilities of real industry topologies. Our proposal is also compared with state‐of‐the‐art exact methods and heuristics. Experimental results show that our algorithm is able to outperform the best‐known heuristics for the MBT, and also that it is a very good alternative for large instances that cannot be solved by current exact methods.