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Stability-Aware Security–Performance Trade-Off Analysis in Resource-Constrained IoT Systems: A Time-Series and Bootstrap-Based Evaluation of TLS and Hybrid ECC–AES Mechanisms
(MDPI AG, 2026-05-02)
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Alvarez-Garcia, Maria Fernanda
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Visconti, Paolo
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The increasing deployment of resource-constrained Internet of Things (IoT) devices requires security mechanisms that preserve confidentiality without compromising energy efficiency or responsiveness. Although Transport Layer Security (TLS) provides standardized protection for MQTT-based communication, its computational overhead may significantly affect embedded architectures. This study presents a controlled experimental evaluation of three communication configurations implemented on ESP32-based nodes: unencrypted Message Queuing Telemetry Transport (MQTT), MQTT over TLS 1.2, and an application-layer hybrid scheme combining Elliptic Curve Diffie–Hellman key exchange with AES-128 encryption. Second-level measurements of instantaneous current, accumulated energy, end-to-end latency, and memory footprint were collected across repeated experimental runs. Time-series diagnostics were performed to assess autocorrelation and stationarity, and block bootstrap resampling was applied to ensure dependence-aware statistical inference. The results indicate that TLS introduces the highest cumulative energy growth and latency dispersion, while the hybrid ECC–AES configuration demonstrates intermediate behavior with reduced overhead relative to TLS. Pareto frontier analysis shows that TLS is dominated in the joint energy–latency space, whereas the hybrid scheme represents a non-dominated compromise between security and efficiency. These findings provide a stability-aware and statistically robust framework for evaluating security–performance trade-offs in embedded IoT systems.
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Generative AI and the scientific landscape: a bibliometric exploration of its global impact
(Editorial Académica Dragón Azteca, 2026-02-16)
Cossio Franco, Edgar Gonzalo
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Sossa Azuela, Juan Humberto
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Larios Rosillo, Víctor Manuel
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Maciel Arellano, Rocio
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Arreola Marín, María Esmeralda
The present comparative bibliometric study (2020-2025) of the Scopus and WoS databases on Generative Artificial Intelligence (GenAI) reveals accelerated growth, concentrating more than 95% of the production and reaching its peak impact in 2025. Thematically, the intersection of communication and technology/education dominates. Geographically, the United States leads production, but Asia-Pacific institutions (Hong Kong) are key. The field of GenAI is a massive trend driven by concentrated collaboration between North America and Asia.
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Efficient Deep Learning-Based M-PSK Detection for OFDM V2V Systems Using MobileNetV3
(MDPI AG, 2026-03-11)
Tonix-Gleason, Luis E.
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Peña-Campos, Fernando
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del Puerto-Flores, Dunstano
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A transformer-based method for radio-frequency fingerprinting of IoT devices
(Elsevier BV, 2026-04)
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Bazdresch, Miguel
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Mex-Perera, Carlos
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A Literature Review on Real-Time Crime Detection Using Deep Learning and Edge Computing
(IEEE, 2025-10-21)
Silva, Carlos Julio Fierro
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Varela-Aldás, José
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A Systematic Review and Energy-Centric Taxonomy of Jamming Attacks and Countermeasures in Wireless Sensor Networks
(MDPI AG, 2026-01-15)
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Vázquez-Castillo, Javier
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Mex-Perera, Carlos
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BiciVR: A Software Engineering Framework for AI-Driven Bicycle Mobility Risk Simulation
(Instituto Politecnico Nacional/Centro de Investigacion en Computacion, 2025-12-30)
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Rodríguez, Wilmer
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Cossio, Edgar
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Electrodermal Response Patterns and Emotional Engagement Under Continuous Algorithmic Video Stimulation: A Multimodal Biometric Analysis
(MDPI AG, 2026-01-18)
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David Contreras-Tiscareno
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Diego Sebastian Montoya-Rodriguez
Excessive use of short-form video platforms such as TikTok has raised growing concerns about digital addiction and its impact on young users’ emotional well-being. This study examines the relationship between continuous TikTok exposure and emotional engagement in young adults aged 20–23 through a multimodal experimental design. The purpose of this research is to determine whether emotional engagement increases, remains stable, or declines during prolonged exposure and to assess the degree of correspondence between facially inferred engagement and physiological arousal. To achieve this, multimodal biometric data were collected using the iMotions platform, integrating galvanic skin response (GSR) sensors and facial expression analysis via Affectiva’s AFFDEX SDK 5.1. Engagement levels were binarized using a logistic transformation, and a binomial test was conducted. GSR analysis, merged with a 50 ms tolerance, revealed no significant differences in skin conductance between engaged and non-engaged states. Findings indicate that although TikTok elicits strong initial emotional engagement, engagement levels significantly decline over time, suggesting habituation and emotional fatigue. The results refine our understanding of how algorithm-driven, short-form content affects users’ affective responses and highlight the limitations of facial metrics as sole indicators of physiological arousal. Implications for theory include advancing multimodal models of emotional engagement that account for divergences between expressivity and autonomic activation. Implications for practice emphasize the need for ethical platform design and improved digital well-being interventions. The originality and value of this study lie in its controlled experimental approach that synchronizes facial and physiological signals, offering objective evidence of the temporal decay of emotional engagement during continuous TikTok use and underscoring the complexity of measuring affect in highly stimulating digital environments.
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IoT-Based Smart Gas Meter With LTE Connectivity and Cloud Analytics for Stationary Tanks
(Institute of Electrical and Electronics Engineers (IEEE), 2026)
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Millions of homes in developing countries rely on stationary LPG tanks, yet the methods for monitoring fuel levels remain manual, unsafe, and highly inefficient. This paper addresses this issue by presenting the design, development, and implementation of an IoT-based smart gas meter that uses a noninvasive Hall-effect sensor to digitally read existing level gauges. Data is transmitted via LTE, eliminating the need for Wi-Fi and optimizing connectivity. The system is designed for low power consumption, achieving a battery life of more than eight years. Additionally, a cloud architecture is implemented in AWS to process the collected data, allowing real-time analysis, predictive maintenance, and logistics optimization. A field test was also conducted with 15 prototypes, demonstrating accurate gas level monitoring, reliable refill detection, and gas theft prevention.
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Tecnología Funcional de Nariz Electrónica para el Monitoreo de Gases en el Aire
(Escuela Politecnica Nacional, 2025-11-30)
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Claudia L. Garzón-Castro
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Annamaría Filomena-Ambrosio
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Roberto De Fazio
En los últimos años, las narices electrónicas se han consolidado como herramientas innovadoras para el monitoreo ambiental, particularmente en la detección de contaminantes en el aire. En este trabajo, se presenta el diseño e implementación de una tecnología funcional, portátil y de bajo costo de nariz electrónica, capaz de identificar gases como el monóxido de carbono, el metano y varios compuestos volátiles. Esta tecnología integra un arreglo de sensores y un módulo de adquisición de datos junto con algoritmos avanzados de procesamiento de señales. Se propone la aplicación del Método de Filtrado y Diagonalización (FDM) para la extracción de características espectrales, combinado con Bosques Aleatorios (RF) para la clasificación de gases. Los resultados experimentales demuestran una precisión del 96.4 % en la identificación de compuestos gaseosos, validando la efectividad de la combinación FDM-RF. Este estudio contribuye al avance de tecnologías accesibles para el monitoreo de la calidad del aire y así como de nuevos métodos de detección y clasificación de gases ambientales.

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