Fifth-generation technology represents a transformative shift in telecommunications, offering enhanced speed, reliability, and ultralow latency. This paper addresses the challenge of load balancing in 5G networks, especially within multi-access edge computing architectures, by evaluating strategies that ensure quality of service and meet the stringent ultra-reliable low-latency communication requirements.Using Free5GC to emulate 5G core and UERANSIM to simulate user equipment and radio access network behavior, the proposed EdgeLB framework integrates the LoxiLB load balancer to evaluate algorithms such as Round Robin, Weighted Round Robin, Hash-based, and Least Connections. Through Netperf-based experiments, we assessed performance under varying numbers of concurrent connections and UEs, as well as network delays that emulate geographic distance.The results demonstrate that intelligent traffic distribution significantly improves network performance and scalability. All algorithms maintained submillisecond latency in ultra-reliable low-latency scenarios, and some exhibited strong jitter and throughput control. Furthermore, polynomial regression models were derived to approximate the degradation of performance under scaling conditions. These findings validate the EdgeLB architecture as a viable solution for latency-sensitive multi-access edge computing and AI inference applications in 5G environments.

Postpartum placental assessment is essential for clarifying adverse pregnancy outcomes and informing clinical decisions; however, anatomopathological examination is generally reserved for selected cases due to structural and operational constraints. This study presents a deep learning-based pipeline for segmenting placental structures in real images taken in the delivery room, aiming to support clinical triage. A proprietary dataset was built using a standardized photographic protocol and annotated by pathologists across nine morphological classes. Five architectures were evaluated: U-Net with ResNet34, ResNet50, EfficientNet-B0, and EfficientNet-B7 backbones, in addition to YOLOv11 for instance segmentation. ResNet34 achieved the best overall performance (Dice: 81.2%, IoU: 70.6%, Accuracy: 85.3%), while YOLOv11 reached a AP50 of 73.2% in detecting key anatomical components. Despite the limitation imposed by the small dataset—which may affect the models’ generalization capability—the results demonstrate the feasibility of using AI for photographic placental triage, with potential to assist clinical decisions and optimize resource use in obstetric settings.

The provisioning of Quality of Service (QoS) in 5G networks plays a crucial role in ensuring efficient performance in scenarios characterized by high demand and dynamism. In the data era, where the generation and consumption of information are growing exponentially, effective traffic management becomes indispensable. This study investigates the application of queueing methodologies — FIFO, PQ, and WFQ — in the context of SDN (Software-Defined Networking) for 5G networks, utilizing ONOS as the SDN controller. The research highlights the importance of traffic management in meeting the stringent requirements of 5G networks, such as ultra-low latency, high reliability, and broad transmission capacity, which are essential for applications like eMBB, URLLC, and mMTC. In addition to a comprehensive theoretical review of 5G, SDN, QoS, and queueing techniques, the study included a practical implementation in a simulated environment using the ONOS software, validating theoretical concepts through a comparative analysis of the impact of queueing on critical metrics such as latency, throughput, packet loss, and jitter. The results demonstrate that optimized queueing techniques are essential for efficient traffic management, reducing latency and increasing throughput across different network scenarios. Thus, this study reaffirms the relevance of integrating SDN and queueing techniques as adaptive and effective solutions for managing modern mobile networks. Furthermore, it highlights promising avenues for future research, including the use of adaptive algorithms in next-generation networks.

This systematic literature review investigates the state of the art in the application of artificial intelligence (AI), particularly convolutional neural networks (CNNs), in the diagnosis of pneumoconioses and asbestos-related diseases (ARDs). A total of 30 articles published between 2020 and 2025 were analyzed, selected from major scientific databases (IEEE Xplore, ScienceDirect, Springer Link, ACM Digital Library, Nature, Wiley Online Library). The analysis addressed the models used types of radiological images (chest X-rays and computed tomography), performance metrics, and limitations. A significant advancement was observed in the use of CNNs and 3D architectures, with an emphasis on automated screening and the interpretability of clinical patterns.

This paper proposes an architecture for managing and distributing context information from an AAL (Ambient Assisted Living) environment to the cloud. The architecture, with semantic features, includes the processing of the sensed data using QoC (Quality of Context) parameters and data transport using SDN (Software-defined Networking). The experiments showed that the semantic context processing allows accurate assessment of the QoC context and the SDN flexibility allows the context to be delivered in a way to provide differentiated quality data.

Wireless Sensor Networks (WSNs) arc susceptible to faults both in sensors and in communication. Information fusion techniques allow to extract precise information from a large amount of data. Detection, identification and treatment of outlier, in these techniques, is a key point. Outlier detection in WSNs is a challenge due to the low capacity of the nodes and low bandwidth of the network. This paper proposes a methodology that applies the clustering and lightweight statistics techniques for detection of outliers in WSNs. The assessment of the methodology involves a case study with temperature sensors in WSN nodes. The results show that this methodology is able to provide precise information, even in the presence of outliers.

This paper proposes an eHealth context management and distribution architecture with semantic features and designed for the Future Internet. A formal basis was used for managing both QoC (Quality of Context) and QoS (Quality of Service). Our approach was assessed in an experimental setting by using biomedical and environment sensors. The experiments showed that the context semantic processing detects anomalies or inconsistencies, generating consistent alerts. We observed that the SDN (Software-Defined Network) allows the context to be transported and delivered in accordance with the QoS context requirements. Therefore, our approach provides knowledge with fine granularity to assist health caregivers in making decisions.

The use of wireless sensor networks (WSN) to support critical monitoring applications is becoming a relevant topic of interest. These networks allow a highly flexible approach to data monitoring and, consequently, a major breakthrough for several application domains, from industrial control applications to large building domotics and health care applications. One of the major impairments of using wireless networks to support critical monitoring applications is the electromagnetic noise, which may increase the packet loss ratio to unacceptable values. In this paper, we assess different techniques of cooperative communication and network coding that can be useful to mitigate the aforementioned problem. These techniques may be implemented in WSN nodes in conformance with the IEEE 802.15.4 standard, to reduce the impact of electromagnetic interferences upon the packet loss ratio. In this paper, we report an experimental assessment of the network coding and cooperative diversity techniques, where the network is subjected to a controlled electromagnetic interference inside of an anechoic chamber. The experimental results show that, by using these techniques, it is possible to increase the success rate of communication in typical electromagnetic noisy environments.

In several monitoring applications, such as those that can be found in industrial factory floor, it may be necessary to obtain an uniform sensing coverage, providing as much as possible the same coverage degree for the entire network area. The IEEE 802.15.4 has become an important standard in wireless sensor networks. However, the use of cluster-tree topology in these networks hampers a fair and uniform coverage in the sensing area. Clusters more distant from the base station are eventually adversely affected, with messages from its sensors delayed and discarded. In this paper, we propose a framework, entitled GLHOVE, whose goal is to make a trade-off between a minimum and uniform coverage area and the energy consumption of the network. The simulation results show an increase up to 20% of the fairness metric (w.r.t. messages received by base station).

In some Wireless Sensor Network (WSN) applications, it may be necessary to keep a large number of nodes sensing and transmitting data to a base station in order to have unbiased measurement values. Therefore, in addition to the traditional energy consumption issues, the spatial diversity of the monitored area is another relevant metric to evaluate the performance of a WSN. Nevertheless there is a clear trade-off between these two parameters, as keeping the sensors active all the time will deplete the nodes batteries and therefore will shorten the network lifetime. Fortunately, for the case of some applications it is possible to specify as a Quality of Service (QoS) parameter the number of periodically expected messages in the base station. Therefore, it will be possible to balance QoS against energy consumption. This paper proposes an approach called (m,k)-Gur Game that aims a trade-off between the expected QoS and the spatial coverage diversity. Simulation results show the effectiveness of the proposed approach.

The increase in the demand for computing resources with scalable infrastructure and easily managed has stimulated the emergence of IaaS - Infrastructure-as-a-Service - providers in public clouds, as well as the creation of open source IaaS solutions for institutions that prefer to implant their own private cloud. Aiming at expanding the cost-benefit, it is often necessary to integrate public and private cloud services. However, the wide variety of interfaces for communication with the services provided by each solution may impair the management of these clouds by the current environment management tool. In this context, is proposed a management tool for private and hybrid clouds, referred to as GBD IaaS Manager, which offers as original contribution, support for migration of virtual elements to these environments, allowing for a better utilization of computing resources and the load balancing among private and hybrid clouds.

Wireless Sensor Networks (WSNs) can be used to monitor hazardous and inaccessible areas. The WSN is composed of several nodes each provided with its separated power supply, e.g. battery. Working in hardly accessible places it is preferable to assure the adoption of the minimum transmission power in order to prolong as much as possible the WSN’’s lifetime. Though, we have to keep in mind that the reliability of the data transmitted represents a crucial requirement. Therefore, power optimization and reliability have become the most important concerns when dealing with modern systems based on WSN. In this context, we propose to evaluate the effectiveness of a Transmission Power Self-Optimization (TPSO) technique for WSNs in an Electromagnetic Interference (EMI) Environment. The TPSO technique consists of an algorithm able to guarantee an equally high Quality of Service (QoS), concentrating on the WSN’’s Efficiency (Ef), while optimizing the transmission power necessary for data communication. Thus, the main idea behind our approach is to reach a trade-off between Ef and energy consumption in an environment with inherent noise.

Wireless sensor networks (WSN) are being targeted for use in applications like security, resources monitoring and factory automation. However, the reduced available resources raise a lot of technical challenges. Self-organization in WSN is a desirable characteristic that can be achieved by means of data fusion techniques when delivering reliable data to users. In this paper it is proposed a genetic machine learning algorithm (GMLA) approach that makes a trade-off between quality of information and communication efficiency. GMLA is based on genetic algorithms and it can adapt itself dynamically to environment modifications. The main target of the proposed approach is to achieve self-organization in a WSN application with data fusion. Simulations demonstrate that the proposed approach can optimize communication efficiency in a dense WSN.