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The Minimum 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.

This work proposes a data sampling algorithm for smart cities applications based on sensor network infrastructure. Our algorithm identifies the sensor data behavior through the Causality Complexity-Entropy Plane and performs data reduction by removing redundant data without losing the system’s properties. For this, we recognize the systems’ dynamic changes in real-time through a delimiter, named Maximum Complexity Point (MCP). Thus, we determine when to update the sampling period to maximize the system’s information content, i.e., the statistical complexity quantifier. To confirm the sampling adaptability, we apply our method in three different chaotic attractors: Rossler, Lorenz, and B7 . We compared our solution with two other sampling algorithms: (i) random histogram-based sampling and the L algorithm. We use the K-S test, the average Data Error, and the Causality Complexity-Entropy Plane to compare the results. Using our sampling approach, we observed K-S test distances less than 3% for chaotic maps and 1% for natural environments data. The best results were in the Data Error, showing an average error rate up to 13.4% lower when evaluating chaotic data and 15.7% lower when evaluating natural environments. Regarding the dispersion of points in the Causality Complexity-Entropy Plane, the sampled time-series reached regions of higher statistical complexity, indicating that they preserved information content, hence the original data’s dynamics.

Several wireless sensor networks (WSNs) simulations run in parallel computer architectures to improve their scalability. The main problem with this strategy is guaranteeing the reproducibility transparently to simulation users. We present a reproducible model for WSNs parallel simulations. The model uses a chunk partition strategy, in which all simulation elements are wrapped into chunks and simulated sequentially inside each chunk. We can simulate multiple chunks in parallel as often as possible by adding seed and a pseudo-random number generator in each of them, thus ensuring the same results. This model was integrated into the JSensor simulator and validated accordingly. An important aspect is that the new reproducibility model does not alter the simulation’s semantics, and it is transparent to the developer. The results demonstrated that our model could guarantee the reproducibility of stochastic WSNs parallel simulations performed in different computer systems with different threads.

Understanding the structure and the dynamics of networks is of paramount importance for many scientific fields that rely on network science. Complex network theory provides a variety of features that help in the evaluation of network behavior. However, such analysis can be confusing and misleading as there are many intrinsic properties for each network metric. Alternatively, Information Theory methods have gained the spotlight because of their ability to create a quantitative and robust characterization of such networks. In this work, we use two Information Theory quantifiers, namely Network Entropy and Network Fisher Information Measure, to analyzing those networks. Our approach detects non-trivial characteristics of complex networks such as the transition present in the Watts-Strogatz model from k-ring to random graphs; the phase transition from a disconnected to an almost surely connected network when we increase the linking probability of Erdős-Rényi model; distinct phases of scale-free networks when considering a non-linear preferential attachment, fitness, and aging features alongside the configuration model with a pure power-law degree distribution. Finally, we analyze the numerical results for real networks, contrasting our findings with traditional complex network methods. In conclusion, we present an efficient method that ignites the debate on network characterization.

This paper presents a data sample algorithm applied to wireless sensor network applications with disruptive connections. Additionally, it defines a model for delay-tolerant sensor network where drop strategies are applied to improve the phenomenon coverage in an application that monitors the forest temperature incidence for wildlife observation. The environmental application model comprises: i) Phenomenon generation based on a Gaussian random field along with the Matern covariance model; ii) Sensing nodes deployment based on simple sequential inhibition process with a mobile sink node following a random walk process; iii) Data collection and processing based on a data-aware drop strategy; and iv) Phenomenon reconstruction based on simple kriging interpolation. This research employed the data-aware drop strategy and compared it with the others, reported in the literature. Besides the satisfactory application of this model, the results show that the performance of data-aware drop strategy is twice better than conventional ones in all evaluated scenarios.

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Terceiro Lugar do CTIC – SBC (Concurso de Trabalhos de Iniciação Científica 2022):

Desagregação de Energia Baseada em Deep Learning e Transformação de Wavelet

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Selecionada pelo SENAI/AL como uma das empresas alagoanas mais promissoras na prestação de serviços à indústria.

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Primeiro Lugar do IX Workshop de Computação Aplicada em Governo Eletrônico (2021):

CALT: Uma Ferramenta Automática para Cobrança do ICMS em Operações Interestaduais

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Primeiro Lugar do CTIC – SBC (Concurso de Trabalhos de Iniciação Científica 2019):

Técnicas de Redução de Dados em Redes de Sensores sem Fio tolerantes a atrasos

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Primeiro Lugar do XXXVII Simpósio Brasileiro de Redes de Computadores (2019):

Uma Estratégia de Leilões Combinatórios para Provisionamento de Serviços Virtualizados

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Segundo Lugar do Prêmio Beatriz Neves – SBMAC (2017):

Monitoramento Ambiental utilizando Redes de Sensores sem Fio tolerantes a atrasos

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