This article tackles the critical challenge of optimizing the buffer pool, a core component of Database Management Systems (DBMS) that caches frequently accessed data pages, where manual configuration often proves inadequate in dynamic, high-demand environments. To address this gap, we present an automated, data-driven methodology that combines advanced Machine Learning techniques with Bayesian optimization. Our approach follows a systematic three-phase process: (1) Exploratory Factor Analysis (EFA) coupled with K-means clustering to uncover latent factors and reduce the dimensionality of performance metrics; (2) LASSO regression to identify and rank the most influential configuration parameters; and (3) Bayesian optimization using Gaussian Process modeling with acquisition functions (Expected Improvement, Probability of Improvement, and Upper Confidence Bound) to fine-tune buffer pool settings. The main contributions of this work include a novel automated framework for DBMS tuning that simplifies configuration, enhances memory management, and boosts performance efficiency. We validated the proposed solution using real workloads collected from a large-scale financial system in Latin America, achieving up to a 45% reduction in maximum data access wait times, confirming improvements in performance and scalability.

Wireless Underground Sensor Networks (WUSNs) are complex systems comprised of subterranean sensors interconnected through wireless communication technologies. These networks fulfill a crucial role in monitoring subsurface environments. However, they grapple with a formidable challenge concerning their Network Lifetime (NL), which can be defined as the maximum duration over which the network remains operational and thus connected to a designated observation area. Given the paramount significance of prolonging NL to ensure comprehensive coverage of the observed region, the deployment of wireless power transfer stands out as a preeminent solution for augmenting NL. Nonetheless, the existing sleep-wakeup protocols have not been originally engineered to support this paradigm, which has subsequently resulted in suboptimal network performance. Therefore, we present a study to introduce a novel sleep-wakeup protocol explicitly tailored for wireless power transfer in WUSNs with the overarching aim of optimizing the network’s operational lifetime called Sleep-Wakeup Power-Transfer Media Access Control (SWPTMAC). The evaluation of SWPTMAC has been conducted through comprehensive simulations leveraging the Castália simulator. The empirical findings disclosed an average improvement of approximately 24% when contrasted against incumbent protocols in the domain.

Forest fires have the potential to cause enormous social, economic and, above all, environmental damage. Nowadays, the intelligent and early detection of forest fires is a fundamental technological tool for rescue and emergency agencies in mitigating damage. Among the possibilities, video surveillance technology in conjunction with artificial intelligence and computer vision techniques proves to be an interesting solution. This work proposes the SEMFOGO system, an intelligent system for monitoring and rapid detection of fires in the cerrado region, which currently in South America has an area of 2,045,000 km2between Brazil, Bolivia and Paraguay. The SEMFOGO solution uses the massive capture and processing of video streams through a distributed and scalable system and applies a deep learning model that performs a grid classification on parts of the image. This work also presents a new training dataset specific to the cerrado region with smoke contour annotations. The experimental results compared the metrics of several models and show the practical feasibility of the proposed solution for real time fire detection.

This work proposes to solve the maximal covering location problem of the Mobile Operations Coordination Center (CCOp Mv), which aims to support the operational command of the Brazilian Army. This problem consists of selecting, in a limited region and with poor communication infrastructure to the ground troops’s operating area, the positions of vehicles equipped with Base Transceiver Station (BTS), the amount needed, and theirs transmission power to be set that maximizes the coverage area and reduce the interference due to the overlap of signals. For this reason, analytical modeling based on the mixed-integer linear problem was proposed that guided two optimization solutions: (i) E-ALLOCATOR – Exact ALLOCATiOn seRvice; and (ii) M-ALLOCATOR – Metaheuristic ALLOCATiOn seRvice. The solutions were evaluated in a scenario that employs CCOp Mv to support a rescue operation based on the tragedy in January 2019 in Brumadinho-MG and compared with a heuristic. The performance evaluation results show evidence of efficiencies in terms of quality and resource savings of the proposed solutions. Furthermore, E-ALLOCATOR has been proven to be suitable for a low workload on the network. At the same time, M-ALLOCATOR is suitable for scenarios with a high workload providing almost optimal solutions within the adequate computational time for all problem instances.

Full Paper in the Innovative Practice track - The pandemics caused by the spreading of the COVID 19 virus cornered the educational system worldwide, changing the classroom into remote class activities. This change in our social behavior has directly impacted the volume and shape of the Internet traffic data. A recent study shows 15% to 30% increases in Internet traffic caused, among other reasons, by educational video streaming traffic during few weeks in the 2020 lockdown period in Europe. To give some perspective, network providers usually work with a 30% data traffic increase per year. In 2021, it is expected that almost 82% of all Internet traffic will be video, according to CISCO annual forecast report. This scenario has a tremendous impact on the Internet bandwidth capacity, demanding optimized video streaming solutions, such as adaptive bitrate algorithms (ABR). On the other hand, considering the educational challenges in computer network courses, the core activities must be executed using specialized infrastructure to develop students' capabilities with networking equipment. As these types of equipment are costly to be obtained and forwarded to in-home students or simply e-students, a remote platform capable of reproducing an environment for networking applications is required. This is the scenario where PyDash was built. PyDash is a framework for the development of adaptive streaming video algorithms. It is a learning tool designed to abstract the networking communication details, allowing e-students to focus exclusively on developing and evaluating ABR protocols. This paper presents our practical experience developing and using PyDash as an educational tool for teaching ABR protocols at Computing Networking courses at the Department of Computer Science at the University of Brasilia, Brazil. Last semester, over 120 students, divided into four different undergraduate courses, had their first contact with PyDash. Even though this was their first contact with ABR concepts and the pyDash tool, they were able to perform the design, implementation, validation, and analysis of some state-of-the-art algorithms used by Netflix and Youtube.

Fifth Generation of Mobile Communication (5G) integrates the use of telecommunication and computer systems. As virtualisation eases the deployment of new functionalities demanded by many industrial and social use cases, also show many research challenges regarding performance and resource optimisation. In the 5G architecture, while mobile networks are already trying to implement a full virtualisation of hardware resources, the core itself lacks of an integrated performance evaluation proposal. In this paper we propose a performance evaluation framework, based on an evaluation function and an assortment of distributed observation functions acting as monitors for compute nodes in a virtualised. infrastructure. The framework, unlike previous proposals, is designed to be integrated into 5G as a native service. The framework was evaluated in a Ultra-Reliable and Low Latency Communications (URLLC) scenario and the results show success in monitoring and analysing a Network Function Virtualisation (NFV) environment with a standard 5G NFV implementation.

Static spectrum allocation policies allied with the increasing demand for higher data rates stimulated the pursuit of alternative spectrum allocation strategies. In this context, Opportunistic Spectrum Access (OSA) has been considered an alternative to allow licensed portions of the spectrum to be shared with unlicensed users. OSA requires unlicensed users to identify unused portions of the spectrum for opportunistic access that minimizes possible interference with the licensed users. Accurate mechanisms to avoid interference and improve the spectrum usage is highly desirable. This work investigates the performance of a traditional Hidden Markov Model (HMM) predictor where the licensed traffic follows a Poisson distribution. The results show that, under the evaluated settings, traditional HMM predictor can improve spectrum usage up to 15% at the expense of a high rate (≈ 50%) of inaccurate forecasts of idle periods. Based on these results, this paper proposes two techniques to optimize prediction performance and reduce inaccurate forecasts rate. Using the proposed enhancements inaccurate forecasts rate was reduced to only 6%. An additional benefit was observed when reducing collision with the licensed user, that is an improvement in the amount of slots effectively used by the unlicensed users from 15% to 23%.

Internet of Things (IoT) is one of the key enabling technologies for an always-connected world and also a main enabler for generating information of interest in various application domains. A growing problem in recent years in this technology is security, as power-constrained devices that are typical of IoT applications may not always provide these implementations properly. These conditions can compromise entire environments and allow malicious agents to take control and perform malicious activities. In this article, we provide a summary of the principal vulnerabilities reported for IoT devices based on the OWASP Internet of Things Project, classified by test routine groups. Using models based on standard architectures to define and detail reproducible verification routines for each test, a selection of independent analyzes of each identified category was performed to ensure more comprehensive and accurate testing. Finally, the proposed routines are performed in a test environment to exemplify and ensure their operation, thus contributing to meeting the demand in the area for more accurate information and to assist in understanding the most common vulnerabilities.

Wireless networking enhanced with multiple transmitting channels has been considered to improve the utilization of the electromagnetic spectrum and to provide ways to accommodation the growing demand for connectivity. In this scenario, the use of a common control channel to coordinate the stations and networking resources is usually employed. This work evaluates the control channel capacity and its impact on the overall system throughput when multiple channels are available. The CSMA/CA mechanism is considered as it is the most used access control mechanism in the wireless local area network. To this end, an analytical model is proposed to evaluate the saturation conditions of the control channel. Empirical results are also provided and compared with the analytical model. The results show that the empirical results are consistent with the analytical model. In particular, it is shown that the ratio of eight data channels per control channel attains the best results.

The radio spectrum is a finite resource and the current spectrum licensing model has led to a fenomenon known as spectrum scarcity. Several approaches have been presented in order to improve the effectiveness of spectrum utilization. This works main contribution is is to present an approach to opportunistic spectrum sensing and allocation focused on maximizing spectrum usage while minimizing communication time and interference on primary communications. Compared an alternative, our approach presents significant improvements in the spectrum access and utilization, improving network throughput up to 16 times.

The growing demand for mobile wireless access has stimulated the emergence of new communication technologies. Opportunistic Spectrum Access (OSA) is viewed as a promising alternative to overcome the problems caused by static spectrum assignment. Opportunistic access allows dynamic mapping of the transmission needs and communication opportunities. However, performing this task efficiently is not trivial. Indeed, it has been shown to be NP-complete. In this context, this paper presents an efficient heuristic for solving the channel allocation and data assignment problem, according to the opportunities and channels available. The proposed heuristic is optimal in terms of energy consumption, being close to the optimum, about 5% above, in terms of transmission time.

Multiple sequence alignment (MSA) is an important problem in Bioinformatics since it is often used to identify evolutionary relationships and predict secondary/tertiary structure, among others. MSAs are usually scored with the Sum-of-Pairs (SP) function and the exact SP MSA is known to be NP-Hard. Therefore, heuristic methods are used to solve this problem. In this paper, we propose and evaluate a parallel island injection genetic algorithm to solve the MSA problem. Unlike the other strategies, our parallel solution uses two types of interconnected archipelagoes, each with distinct types of individuals. Our results with real protein data sets show that our strategy is able to obtain better results, when compared to the traditional island model. Also, we were able to reduce considerably the execution time, when compared to the sequential version.

The main contribution of this article is to propose a collaborative and distributed cache mechanism tailored for ad hoc networks. Each node's cache is partitioned into a shared area and a private area. The proposed collaborative cache is formed by each node's shared area, which is used to store contents relevant to the group, while the private area is used to store information relevant to its owner. Empirical results have shown that our collaborative cache mechanism can significantly reduce the amount of network traffic, latency and energy consumption as well as the server's load.

Access networks based on cooper cables are costly to build and maintain. For this reason, last-mile access networks based on wireless technologies are gaining considerable attention. Among the wireless technologies being employed, the IEEE802.11 is common place. In such scenarios it is important to have well defined mechanisms to better evaluate traffic characteristics and overall system performance. Such understanding can help network designers to better estimate the resources needed to provide basic services with a reasonable level of quality (QoS). This task, however, has been shown to be non-trivial. The main contribution of this work is to present techniques than can be applied to estimate the throughput and the access pattern for basic services in the context of the IEEE802.11 based networks.