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.

Current multiple access technologies, Orthogonal Multiple Access (OMA) and Non-Orthogonal Multiple Access (NOMA) alone cannot satisfy 6G requirements and provide the connectivity to future networks. Hence, in this work, we propose an Adaptive NOMA-OMA (A-NOMA) that can benefit from interchanging multiple access technologies enhancing the Spectral Efficiency (SE). To turn feasible the interchange of transmission techniques, we model a spectrum sharing problem to exploiting the OMA-NOMA trade-off. We propose an architecture to the total spectral efficiency. Simulations resulted on an enhanced SE and provided more than 20% compared to NOMA SE and over 50% with OMA SE.

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.

Abnormal behavior in mobile cellular networks can cause network faults and consequent cell outages, a major reason for operational cost increase and revenue loss for operators. Nonetheless, network faults and cell outages can be avoided by monitoring abnormal situations in the network and acting accordingly. Thus, anomaly detection is an important component of self-healing control and network management. Network operators may use the detected abnormal behavior to quantify numerically their intensity. The quantification of abnormal behavior assists the characterization of potential regions for infrastructure updates and to support the creation of public policies for local connectivity enhancements. We propose an unsupervised learning solution for anomaly detection in mobile networks using Call Detail Records (CDR) data. We evaluate our solution using a real CDR data set provided by an Italian operator and compare it against other state-of-the-art solutions, showing a performance improvement of around 35%. We also demonstrate the relevance of considering the distinct traffic patterns of diverging geographic areas for anomaly detection in mobile networks, an aspect often ignored in the literature.

Network Intrusion Detection Systems (NIDS) play an important role as tools for identifying potential network threats. In the context of ever-increasing traffic volume on computer networks, flow-based NIDS arise as good solutions for real-time traffic classification. In recent years, different flow-based classifiers have been proposed using Machine Learning (ML) algorithms. Nevertheless, classical ML-based classifiers have some limitations. For instance, they require large amounts of labeled data for training, which might be difficult to obtain. Additionally, most ML-based classifiers are not capable of domain adaptation, i.e., after being trained on an specific data distribution, they are not general enough to be applied to other related data distributions. And, finally, many of the models inferred by these algorithms are black boxes, which do not provide explainable results. To overcome these limitations, we propose a new algorithm, called Energy-based Flow Classifier (EFC). This anomaly-based classifier uses inverse statistics to infer a statistical model based on labeled benign examples. We show that EFC is capable of accurately performing binary flow classification and is more adaptable to different data distributions than classical ML-based classifiers. Given the positive results obtained on three different datasets (CIDDS-001, CICIDS17 and CICDDoS19), we consider EFC to be a promising algorithm to perform robust flow-based traffic classification.

In this work, we investigate opportunities for network operators to reduce their expenditures through optimal allocation of virtual Base-Band Units (vBBUs) in Fog Radio Access Networks (F-RANs). The optimal allocation can generate additional revenue opportunities by leasing idle processing resources to Application Service Providers (ASPs). In particular, we address the challenge of improving vBBU allocation in terms of optimal assignment of the workloads of Remote Radio Heads (RRHs) to Micro Data Centers (MDCs) for cost minimisation, considering the trade-off between MDC and RRH distance and processing power consumption. Thus, we propose an optimisation model to decide the assignments between MDCs to RRHs. The optimal solution is obtained through Binary Integer Linear Programming (BILP). We evaluate our solution by applying a real Call Detail Record (CDR) data set, simulating different regions from Milan. K-means clustering was used to identify the Internet traffic behaviour of different regions in Milan. This work results' highlight opportunities for network operators to exploit their infrastructure usage and increase their gains.

Cognitive radio devices are able to sense the spectrum of frequencies and share access to vacant channels. These devices usually have a candidate channels list that must be sensed to find a vacant channel. In this paper, we propose a novel system called ChiMaS, which is able to manage the candidate channels list implementing three tasks: Analysis, Creation, and Sort. Analysis applies reinforcement learning algorithms to evaluate the channels quality based on their historical occupancy and their conditions; Creation is responsible for creating the Candidate Channels List; and Sort ranks the channels to obtain an Ordered Channels List in terms of quality. Results show that ChiMaS manages the candidate channels list following the IEEE 802.22 definition, while it finds the best channel in terms of availability and quality faster than Q-Noise+ algorithm, which was implemented for comparison purpose.

Cognitive radio make use of spectrum sensing techniques to detect licensed users transmissions and avoid causing interference. The major drawback in current spectrum sensing techniques is the use of static decision thresholds to detect such transmissions, which may be infeasible in public safety radio channels. More precisely, the cognitive radio may find different noise or interference levels when switching among these channels. This can lead to a wrong picture of the channel occupancy status, which in turn can increase the interference caused to licensed users. In this paper we propose an Adaptive Threshold Architecture, which uses machine learning algorithms to dynamically adapt the decision threshold, enabling the detection of licensed users transmissions in public safety radio channels. Results showed that the proposed architecture increased the sensing accuracy up to 2 times, providing results up to 6 times faster when compared to other solutions of the literature.

Software defined radio enables the improvement of the radio-frequency spectrum utilization through the design of cognitive radio devices. The implementation of these devices must be based on spectrum sensing function searching for vacant channels and, opportunistically, transmit over these channels in a cognitive radio network. Therefore, the configuration, monitoring and visualization of the spectrum sensing function are fundamentals to the continuous learning process of the network administrator. In this paper we propose Kitsune, a management system based on a hierarchical model allowing to manage summarized information about the spectrum sensing function in a cognitive radio networks. Moreover, a Kitsune prototype was developed and evaluated through a real IEEE 802.22 scenario using TV channels to Internet access. Results shown that Kitsune allows network administrator to achieve a higher knowledge about behavior of the users and improve the average throughput for each channel.

The management of smart objects (SObjs) is an important task because they are huge in number and applications. Such huge number of SObjs may lead the Internet of Things (IoT) to face severe network conditions, in terms of network congestion and large delays. Thus, the management of SObjs is fundamental to avoid future IoT network problems. In such a management, network boxes, also called gateways, have been configured to manage SObjs with software updates or reconfiguration followed by a warm start. However, gateways configuration become soon outdated because SObjs join and leave the network quite frequently. Therefore, we propose an approach called MbDSAS to reconfigure gateways without the need of a software updating or patching to manage and detect SObjs and deal with the dynamicity of the IoT network. An evaluation of MbDSAS was performed through an airport modeled scenario. In addition, MbDSAS was experimentally tested to be qualified as a management solution to IoT scenarios and to determine the best performance combination of technologies to implement MbDSAS.

Internet of Things devices being used now expose limitations that prevent their proper use in healthcare systems. Interoperability and security are especially impacted by such limitations. In this paper, we discuss today's issues, including benefits and difficulties, as well as approaches to circumvent the problems of employing and integrating Internet of Things devices in healthcare systems. We present this discussion in the context of the REMOA project, which targets a solution for home care/telemonitoring for patients with chronic illnesses.