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Does the use of dedicated mobile devices in magnetism classes improve student learning?
(Frontiers Media SA, 2026-04-21)
Varela-Aldás, José
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Collay, Washington
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Del-Valle-Soto, Carolina
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Palacios-Navarro, Guillermo
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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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Recent Advances in Multi-Camera Computer Vision for Industry 4.0 and Smart Cities: A Systematic Review
(MDPI AG, 2026-03-25)
Fierro-Silva, Carlos Julio
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Del-Valle-Soto, Carolina
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Mostafa, Samih M.
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Varela-Aldás, José
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Stochastic Characterization of MAC-Level Reliability and Reassociation Dynamics in IEEE 802.15.4 Networks for Smart Grid Applications
(MDPI AG, 2026-04-14)
Del-Valle-Soto, Carolina
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Del-Puerto-Flores, J. Alberto
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Velázquez, Ramiro
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Botero-Valencia, Juan Sebastián
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Valdivia, Leonardo
Wireless communication networks based on IEEE 802.15.4 and ZigBee PRO constitute a critical component of smart grid infrastructures, where reliability and availability requirements exceed those typically assumed in low-power wireless deployments. Despite extensive analytical modeling, most existing studies rely on independence assumptions for packet errors and simplified abstractions of reassociation dynamics. This work presents stochastic reliability characterization grounded on real MAC-layer traffic capture from an operational IEEE 802.15.4/ZigBee PRO network. The methodology combines statistical hypothesis testing, first-order Markov modeling, spectral-gap analysis, large-deviation theory, renewal processes, and survival analysis of realignment intervals. Empirical results reject the hypothesis of independent frame errors and demonstrate significant temporal dependence with geometric mixing behavior. The estimated transition structure reveals burst-error persistence, inflating long-run variance relative to memoryless models. Furthermore, coordinator realignment intervals deviate from exponential behavior, exhibiting non-constant event rates consistent with regenerative dynamics. These findings indicate that effective communication reliability is governed not only by average frame error probability but also by dependence structure and regeneration mechanisms. The proposed probabilistic framework provides a rigorous and reproducible methodology for dependence-aware reliability assessment in smart grid communication systems.
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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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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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Del-Puerto-Flores, J. Alberto
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Peña-Campos, Fernando
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del Puerto-Flores, Dunstano
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López-Pimentel, Juan Carlos
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A transformer-based method for radio-frequency fingerprinting of IoT devices
(Elsevier BV, 2026-04)
Herrera-Loera, Carlos
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Del-Valle-Soto, Carolina
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Valdivia, Leonardo
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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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Del-Valle-Soto, Carolina
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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)
Herrera-Loera, Carlos
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Del-Valle-Soto, Carolina
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Valdivia, Leonardo
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Vázquez-Castillo, Javier
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Mex-Perera, Carlos

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