Briseño Martínez, Ramón Alejandro

Main Affiliation
Preferred name
Briseño Martínez, Ramón Alejandro
Official Name
Briseño Martínez, Ramón Alejandro
ORCID
0009-0009-6561-0807
Researcher ID
OEO-5406-2025
Scopus Author ID
57220190267

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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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    Architecture for Semantic Profile Matching Based on Text Descriptors
    (Pleiades Publishing Ltd, 2025-12)
    Cossio, E.
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    Vera, F.
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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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    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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