This work presents the development of a low-cost monitoring system for assessing the operating condition of wastewater and rainwater pumping systems using motor current signature analysis. The proposed system leverages open-source hardware and firmware, ensuring accessibility, interoperability, and replicability across diverse urban water infrastructures. It has been successfully deployed and validated in real sewage and stormwater sump environments, demonstrating robustness under harsh and variable field conditions. The architecture is designed with flexibility in mind, supporting both local and cloud-based deployments, thus enabling integration with advanced data analytics platforms. A remote diagnostics and alerting mechanism is incorporated to provide real-time food detection capabilities, improving emergency response during extreme weather events. By relying solely on non-invasive current measurements, the proposed approach effectively identifies anomalies associated with clogging in submersible pumps, providing a scalable and economically viable solution for condition monitoring within urban water systems.

The number, sophistication, and cost of cyber-attacks targeting both public and private organisations have increased markedly in recent years. A significant proportion of these attacks — such as eavesdropping, port scanning, and lateral movement — exploit readily accessible vulnerabilities at the network level. Although various solutions are available to strengthen network environments, these are often overlooked or improperly implemented by network, system, or security administrators. The goal of this study is twofold: to assess the state of organisational network security and to identify common vulnerabilities and misconfigurations that network administrators should prioritise when securing their infrastructures. To this end, a comprehensive network security assessment was carried out across 61 Spanish organisations. Link-layer, network-layer, and higher-layer vulnerabilities were examined using a combination of passive and active techniques. Security findings were grouped and aggregated to provide a consolidated view of network security practices across organisations. The results indicate that more than 93% of the organisations are vulnerable to link-layer, network-layer, or higher-layer attacks such as Man-In-The-Middle (MITM), traffic interception, Denial-of-Service (DoS), and identity theft. These findings highlight that several security risks in organisational networks stem from persistent misconfigurations and the absence of systematic hardening practices, often exacerbated by administrators’ unawareness of the risks posed by default protocols and hardware configurations. The findings also provide empirical data that can help administrators prioritise and implement targeted security improvements.

This paper presents the design and implementation of a web tool offering an innovative method for detecting, diagnosing and classifying bearing faults in rotating machinery under limited data conditions, providing explainability and interpretability of the results obtained. The tool uses a machine learning model to detect and diagnose bearing faults. A monotonic smoothed stacked autoencoder builds a health indicator without requiring feature extraction, making the tool useful without the need for specialized staff. The tool generates explainability and interpretability reports with a correlation analysis between the health indicator and well-known engineering features and easily interpretable details on the diagnosed faults. The tool includes the option to use preloaded state-of-the-art datasets, while also allowing users to upload their own datasets to analyze vibration data from real industrial equipment.

Predicting the remaining useful lifetime (RUL) of bearings in electric motors is crucial to reduce repair costs in industrial maintenance. With the technological advances of Industry 4.0, physical models for prognostics and RUL prediction have been replaced by data-driven models that require no expert feature extraction. Instead, the model itself learns which features are important. However, these models are normally trained and tested on the same dataset, i.e. under the same operating conditions. This limits the application of a model to other operating conditions unless the model is fine-tuned with data corresponding to those conditions. This paper proposes a novel robust health prognostics technique that detects inner-race bearing failures and predicts the RUL of electric motor bearings under various motor conditions without model retraining or fine-tuning. The model combines time and frequency-domain vibration signal analyses to extract features, a stacked variational denoising autoencoder (SVDAE) to fuse these features and build a Health Indicator and a bidirectional long short-term memory (BiLSTM) neural network to predict the remaining useful lifetime of the bearings. The proposed model is trained with a dataset, validated with another dataset and finally tested with seven additional datasets corresponding to vibrations gathered from different motors and operating conditions. The results are more robust and accurate than those of the literature, the robustness of the prediction with different motor and operating conditions is proven, and there is no need to retrain or fine-tune the model, making the proposed model suitable for recently installed equipment.

Employee time tracking is sometimes an issue for many companies for two reasons. First, the lack of convenient devices. Second, the reluctance of employees. This is particularly noticeable in situations where employees do not work within corporate facilities, making it difficult to track work hours. Recent regulations by governments have emphasized the need for accurate time tracking, creating uncertainty in business environments. This paper proposes a simple and cost-effective time tracking system using the Asterisk PBX that runs on commodity hardware. This allows employees to track their working time without additional devices and regardless their locations. This solution uses an interactive voice menu, so employees may record actions related to their daily activities (start and end of the workday, etc.). The employee calls the Asterisk PBX and utters a passphrase before recording the action. Then, a biometric module analyzes the employee's voice to verify their identity, preventing impersonation. The verification process is independent of the text (IT), so the passphrases given to employees have no constraints. The results of the tests performed on the system indicate an accuracy of 99.7%, demonstrating the reliability of the biometric module. High values of accuracy (99.89) and recall (99.8%) are also obtained. Thus, the identification of legitimate employees and fraudulent identification attempts are very accurate.

Rotating machinery plays an essential role in various industrial processes such as manufacturing, power generation, and transportation. These machines, which include turbines, pumps, motors, compressors, and many others, are the heartbeats of numerous industries. The seamless operation of these machines is critical for the efficiency and productivity of these sectors. However, over time, these machines degrade and can suffer faults. One of the most critical components are bearings, which can suffer different types of faults. This paper presents a novel approach for bearing fault classification and diagnosis under limited data. A Monotonic Smoothed Stacked AutoEncoder (MS2AE) is used to infer a smoothed monotonic health index from raw bearing acceleration data. The MS2AE is trained using only healthy data, so this approach can also be used with recently comisioned equipment that has not failed yet. Then, using the evolution of the health index, a first faulty point is computed, so two stages are identified in the lifespan of the rotating machinery: healthy and faulty. Correlation matrices are computed to show the relationship of the health index with time-domain and frequency-domain features in order to provide explainability and validate the health index construction process. When the health index is classified as faulty, Dynamic Time Warping is applied between healthy samples and faulty samples to extract differences. Finally, based on a 1/3-binary tree 3 level kurtogram, these differences are filtered using a bandpass filter and converted to the frequency domain, where characteristic harmonics are used to identify the type of bearing fault. The explainability provided in the health index construction process makes the system useful in certain industries where black-box AI models cannot be trusted due to strict regulations. The classification and diagnosis system achieves robustness in fault classification under different working conditions by utilizing multiple bearing fault datsets. Its ability to be trained using only healthy data and the interpretability offered, makes it suitable for recently installed rotating machinery in real industrial facilities, without requiring qualified staff.

Programming is a key subject in many engineering programs. Students often perceive it as a difficult skill to master. There is extensive literature on helping students learn and improve to program, most of which focuses on K-12 education. However, due to the current demand for workers with programming skills, more research must be conducted on techniques for learning programming at the higher education level. In this work, an analysis and evaluation of the usefulness of an Asterisk Private Branch Exchange (PBX) as an educational tool to improve the programming skills of students in higher education is presented. The study worked with undergraduate students in telecommunications engineering, with little work experience in programming, during the completion of their final year project. Results suggest that using Asterisk has a positive impact on the students’ perception of their programming knowledge and skills, as well as an increment in the interest and comfort regarding programming.

Intelligent fault detection and classification is a cornerstone of prognostic and health management of rotating machinery (RM) research. Correctly classifying and predicting RM faults not only increases productivity in industrial plants but also reduces maintenance costs. The datasets from real facilities needed to train fault classifiers often have few samples due to the expense of provoking faults in real scenarios to obtain data. This article proposes the use of the tabular prior-data fit network (TabPFN) model for the classification of faults in RM. TabPFN is a model which has been pretrained with a large amount of synthetic data with many causal relationships. This allows the model to perform Bayesian inference on the data used for training. The advantages of this model are its ability to be trained with limited data without generating overfitting problems and its high speed (if a graphics processing unit (GPU) is available). To compare its performance with traditional algorithms for tabular classification such as XGboost and random forest, three public datasets were used. Results show that TabPFN performs more accurately than algorithms with limited data, so it is suitable to be deployed in real scenarios when the amount of data available from the monitored RM is limited.

Condition monitoring of industrial equipment has become a critical aspect in Industry 4.0. This paper shows the design, implementation and testing of a low-cost Industrial Internet of Things (IIoT) system designed to monitor electric motors in real-time. This system can be used to detect operating anomalies and paves the way for building predictive maintenance models. The system is built using low-cost hardware components (wireless multi-sensor modules and single-board computers as gateways), open-source software and open cloud services, where all the relevant information is stored. The module collects real-time vibration data from electric motors. Vibration analyses in the temporal and frequency domains were carried out in both modules and gateways to compare their capabilities. This approach is also a springboard to using edge/fog computing to save cloud resources. A system prototype has been tested in the laboratory and in an industrial dairy plant. The results show that the proposed system can be used for continuous monitoring of any rotatory machine with similar accuracy to professional monitoring devices but at a significantly lower cost.

Remaining useful lifetime (RUL) predictions of electric motors are of vital importance in the maintenance and reduction of repair costs. Thanks to technological advances associated with Industry 4.0, physical models used for prediction and prognostics have been replaced by data-driven models that do not require specialized staff for feature selection, as the model itself learns what features are important. However, these models are usually trained and tested with the same datasets. That makes it difficult to reuse models with different datasets, so they should be retrained with data from the specific motor being analyzed. This paper presents a novel and robust health prognostics technique that predicts the remaining useful lifetime of the bearings of electric motors under different motor conditions (shaft frequency, load, type of bearing) without retraining or fine-tuning the model used. The model integrates the frequency-domain signal analysis and a stacked autoencoder (SAE) with a bidirectional long short-term memory (BiLSTM) neural network. The proposed model is trained with the IMS-bearing dataset and is then tested with IMS, FEMTO, and XJTU-SY datasets without retraining it, providing accurate results in all of them, and proving its robustness with different electric motors and work conditions.

The defence-in-depth (DiD) methodology is a defensive approach usually performed by network administrators to implement secure networks by layering and segmenting them. Typically, segmentation is implemented in the second layer using the standard virtual local area networks (VLANs) or private virtual local area networks (PVLANs). Although defence in depth is usually manageable in small networks, it is not easily scalable to larger environments. Software-defined networks (SDNs) are emerging technologies that can be very helpful when performing network segmentation in such environments. In this work, a corporate networking scenario using PVLANs is emulated in order to carry out a comparative performance analysis on defensive strategies regarding CPU and memory usage, communications delay, packet loss, and power consumption. To do so, a well-known PVLAN attack is executed using simulated attackers located within the corporate network. Then, two mitigation strategies are analysed and compared using the traditional approach involving access control lists (ACLs) and SDNs. The results show the operation of the two mitigation strategies under different network scenarios and demonstrate the better performance of the SDN approach in oversubscribed network designs.

Wireless sensor networks are usually deployed in mesh topologies using radio communication links. The mesh selforganizes to route data packets from sensors to the sink. However, if not carefully designed, this may create holes of uncovered areas and energy holes when many networks paths traverse a limited number of sensors. This paper presents the design and performance evaluation of a low-cost clustered wireless sensor network for Building Energy Management (BEM) applications using Bluetooth Low Energy (BLE) and Better Approach to Mobile Ad-hoc Networking (BATMAN). The latter is used to interconnect gateways and cluster headers that have enough power to forward packets and make computations without compromising their battery lifetime, while the former is used to connect sensors to a cluster header. A prototype of a BEM application has been developed and the performance of the network was tested. Results show that the throughput and latency achieved are adequate for BEM applications.

Network monitoring based on packet sniffing is one of the most useful techniques applied by system administrators and security analysts in order to identify threats within a local network. Despite being supposedly a simple task, it could sometimes be a highly resource consuming process. In this paper, the use of two sniffing techniques, raw sockets and scapy, to achieve better performance in terms of maximum capture packet rate are analyzed and compared. Furthermore, both techniques are optimized by using BSD Packet Filtering to improve packet capture, and a multicore architecture in order to reduce the exposition to denegation of service attacks. Finally, a system based on those techniques that is able to automatically detect layers 2 and 3 common vulnerabilities and attacks within the scope of corporative networks is developed. The result is an enhanced system focused on link and network layers that can be deployed in corporative environments.

Nuestra propuesta trata de promover el trabajo autónomo del alumnado en una materia de un título universitario en ingeniería como vía para mejorar el rendimiento del trabajo en equipo. Tanto los contenidos como la evaluación de las prácticas de aula en la asignatura se han rediseñado, habiendo logrado resultados significativos no solo en el rendimiento, sino también en la asistencia a las prácticas de aula y la satisfacción del alumnado.

Predictive maintenance of industrial equipment has become a critical aspect in the Industry 4.0. This paper shows the design, implementation and testing of an Industrial Internet of Things (IIoT) system designed to monitor electric motors in real-time. This system will be the basis for detection of operating anomalies and a future predictive maintenance system. The system has been designed using low-cost hardware components (wireless multi-sensor modules and single-board computer as gateway), open-source software and a free version of an IoT analytics service in the cloud, where all the relevant information is stored. The module gathers real-time data about the vibrations and temperature of an electric motor. Vibration analysis in the temporal and frequency domains was carried out. Furthermore, analysis in the frequency domain was carried out both in the module and in the gateway to compare their capabilities. This approach is also the springboard to take advantage of edge and fog computing as a complement to cloud computing. The prototype has been tested in a laboratory and in an industrial dairy plant.

Voice over IP (VoIP) is a set of software and hardware technologies used for making voice calls over the Internet. VoIP has been massively deployed in corporative environments since voice and data network convergence enables unified communication services while reducing costs. The main component of a VoIP network infrastructure is the private branch exchange (PBX). Nowadays, Asterisk is the most widespread PBX deployed within corporations due to its open access technology, along with its modular and flexible design. The configuration of PBX systems usually relies on multiple configuration files composed of a vast number of parameters that may have an impact on the security of the system. Therefore, the setup of such systems tends to be complicated and prone to errors and usually requires highly specialized human intervention. In this research, a diagnosis platform for discovering vulnerabilities and security breaches in the configuration of an Asterisk PBX is presented. The proposed platform performs both reactive and proactive actions in order to reconfigure and harden an Asterisk PBX. Firstly, the platform reacts after certain events by modifying the configuration of the Asterisk PBX in order to mitigate risks. Secondly, the platform performs several on-demand assessments that also reconfigure the Asterisk PBX to improve overall security. Finally, the functionality of the platform is easily extensible and highly customizable. Extensive tests have been carried out to assess the security and performance of the Asterisk PBX when facing attacks. Results show that the security of the platform increases, avoiding performance degradation when using the proposed platform.

This paper shows an implementation of an Industrial Internet of Things (IIoT) system designed to monitor electric motors in order to detect operating anomalies. This system will also be the basis for a future predictive maintenance system. The design and testing of the prototype, developed using multisensor microcontrollers and single-board computers as gateways, are presented. Each microcontroller gathers real-time data about the vibrations and temperature of an electric motor. The IIoT prototype has been designed using low-cost hardware components, open-source software and a free version of an IoT analytics service in the cloud, where all the relevant information is stored. During the development of this prototype, vibration analysis in the frequency domain was carried out both in the microcontroller and in the gateway to analyse their capabilities. This approach is also the springboard to take advantage of edge and fog computing as complement to cloud computing. The prototype has been tested in a laboratory and in an industrial dairy plant.