Briseño, Ramón A.

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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)
Del-Valle-Soto, Carolina
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Alvarez-Garcia, Maria Fernanda
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Briseño Martínez, Ramón Alejandro
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Visconti, Paolo
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Rodríguez, Jafet
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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Briseño Martínez, Ramón Alejandro
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Hybrid BLE–LoRa architectures for energy-efficient and resilient wireless sensor networks: Experimental validation and adaptive clustering strategies
(Springer Science and Business Media LLC, 2026-06-03)
Del-Valle-Soto, Carolina
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Briseño, Ramón A.
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Valdivia, Leonardo
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Varela-Aldás, José
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Visconti, Paolo
Wireless Sensor Networks are increasingly deployed in mission-critical scenarios where resilience and energy efficiency are paramount. This paper presents a hybrid architecture combining Bluetooth Low Energy (BLE) and Long Range (LoRa) technologies to enhance both robustness and energy-aware performance in adversarial environments characterized by reactive jamming attacks. A comprehensive experimental testbed was developed, integrating BLE and LoRa nodes, a dual-protocol gateway, and a reactive jammer emulator. We introduce an adaptive clustering algorithm that performs energy-aware role assignment and jamming mitigation based on signal anomaly detection and multi-metric routing. To validate its effectiveness, we conducted an extensive time-series analysis on energy consumption, retransmission rates, and signal resilience under both mitigated and non-mitigated conditions across BLE-only, LoRa-only, and hybrid BLE-LoRa networks. The results show protocol-dependent performance trade-offs under the proposed mitigation algorithm. While LoRa-only and hybrid BLE–LoRa networks exhibit consistent reductions in energy consumption, retransmissions, and variability, the BLE-only configuration demonstrates improved resilience at the cost of a moderate increase in energy usage and retransmission activity due to clustering and control overhead. Notably, the BLE-LoRa architecture balances delivery assurance and energy efficiency while maintaining communication even under interference. Furthermore, we provide statistical modeling and hypothesis testing confirming the mitigation algorithm’s significant impact. These findings offer a critical empirical contribution to the design of resilient and energy-aware heterogeneous WSNs and demonstrate the viability of real-time adaptive mitigation strategies for emerging smart environments.
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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)
Briseño Martínez, Ramón Alejandro
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Rodríguez, Wilmer
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Cossio, Edgar
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Del-Valle-Soto, Carolina
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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.

Briseño, Ramón A.

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