Del-Valle-Soto, Carolina
Main Affiliation
Preferred name
Del-Valle-Soto, Carolina
Official Name
del Valle Soto, Carolina
ORCID
0000-0002-0272-3275
Researcher ID
T-5779-2017
Scopus Author ID
56257456700
133 results
Now showing 1 - 10 of 133
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Item type:Publication, Experimental and data-driven evaluation of transport-layer and application-layer security for MQTT-based IoT networks(Elsevier BV, 2026-12); ; ; Varela-Aldás, JoséThe rapid expansion of Internet of Things (IoT) deployments has intensified the need for secure and efficient communication mechanisms tailored to resource-constrained devices. Message Queuing Telemetry Transport (MQTT) is widely adopted due to its lightweight design; however, it lacks native security support, requiring external protection mechanisms that may significantly affect system performance. This paper presents a comprehensive experimental and data-driven evaluation of two security paradigms for MQTT-based IoT networks implemented on ESP32 microcontrollers: transport-layer security using TLS and application-layer encryption based on elliptic curve cryptography (ECC) for key exchange combined with AES symmetric encryption. The analysis jointly evaluates memory utilization, end-to-end latency, energy consumption, and resistance to passive traffic interception under identical experimental conditions. In addition to conventional metric-based comparisons, multivariate statistical analysis and unsupervised learning techniques are employed as exploratory tools to characterize the system-level behavior induced by each security scheme. Results show that Transport Layer Security (TLS) offers stronger confidentiality guarantees at the cost of higher memory overhead, while the ECC–(Advanced Encryption Standard) AES approach significantly reduces memory footprint with moderate latency penalties and comparable energy consumption. Multivariate analysis further reveals that each security mechanism induces a distinct performance regime, providing a compact joint characterization of the security–performance trade-offs across the evaluated configurations. © 2026 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Hierarchical Multimodal Sleep Staging with Optimized EEG, EOG, and PPG Features for Wearable Applications(MDPI AG, 2026-08-16); ; Visconti, PaoloAutomatic sleep staging is fundamental for diagnosing sleep disorders and enabling long-term sleep monitoring with wearable devices. Although Deep Learning has significantly improved classification performance, balancing accuracy with computational efficiency remains challenging, particularly for resource-constrained systems. This paper proposes a lightweight two-stage Deep Learning framework for five-class sleep staging based on optimized multimodal physiological features extracted from electroencephalogram (EEG), electrooculogram (EOG), and photoplethysmography (PPG) signals. The framework is trained and tested using the Bitbrain Open Access Sleep (BOAS) database, considering a 31-subject dataset partitioned into training (24 subjects) and independent test (7 subjects) sets. Feature selection is performed using the minimum Redundancy Maximum Relevance (mRMR) algorithm, followed by Principal Component Analysis (PCA) for EEG and EOG features, while respiratory and cardiac features derived from PPG are directly incorporated into the multimodal representation. A hierarchical Long Short-Term Memory (LSTM) architecture first classifies sleep into Wake, REM, and NREM, then further distinguishes the N1, N2, and N3 stages. On an independent test set, the classifier achieves 88.2% five-class accuracy on the multimodal feature set (EEG + EOG + PPG) with a model size of 3.14 MB, and 87.1% accuracy on the EEG-only feature set using only 2.95 MB of memory. Leave-One-Subject-Out (LOSO) cross-validation yields 86.9% accuracy, supporting subject-independent generalization. Inference latency ranged from 2.55 ms (EEG-only) to 4.02 ms (multimodal), with measured energy per inference of 2.88–10.9 mJ across feature sets. Additional validation on the RichSleep and ISRUC datasets demonstrates robustness across different recording conditions, achieving mean accuracies of 78.2% and 77.3%, respectively. The proposed framework provides a favorable trade-off among classification performance, complexity, and memory footprint, suggesting its potential suitability for wearable and edge-based sleep-monitoring systems. © 2026 by the authors. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Fiber–matrix interaction governs compressive strength in agave-bagasse-reinforced adobe: a factorial experiment with two-way ANOVA and competing mechanism analysis(Frontiers Media SA, 2026-07-31); ; ; Varela-Aldás, JoséNatural-fiber reinforcement is widely cited as a pathway to improve the mechanical performance of adobe, but reported effects on compressive strength are inconsistent across studies: some find improvement, others find degradation, and the choice of experimental conditions rarely disentangles the role of the fiber from that of the matrix. This study quantifies the coupling through a balanced factorial experiment. Forty-nine adobe specimens of (Formula presented) cm were manufactured with three granular compositions (sand-dominated, jal-dominated, and balanced, where jal is a regional non-plastic silt of Jalisco, Mexico) and four mass fractions of agave-bagasse fiber (0%, 0.5%, 1%, and 2%), and were tested under Mexican standard NMX-C-036-ONNCCE by an accredited external laboratory. Three complementary analytical tools are applied to the resulting dataset: (i) a two-way analysis of variance (ANOVA), (ii) a reinforcement efficiency index (Formula presented) with bootstrap confidence intervals, and (iii) a competing mechanism phenomenological descriptor (Formula presented) that separates a saturating reinforcement term from a linear disruption term. The two-way ANOVA reveals a highly significant mixture–fiber interaction ((Formula presented), (Formula presented), and partial (Formula presented)), which is stronger than either main effect and statistically demonstrates that the sign of the fiber effect is not an intrinsic property of the fiber but rather a property of the fiber–matrix pair. For sand-containing mixtures, the reinforcement efficiency index is (Formula presented) [M1, 95% bootstrap CI (0.96, 1.32)] and (Formula presented) [M3, (0.92, 1.59)] at the optimum (Formula presented); a non-parametric bootstrap over 5, 000 resamples places the optimum at (Formula presented) with posterior probability (Formula presented) (M1) and (Formula presented) (M3). For the jal-dominated mixture, fiber inclusion is net destructive [(Formula presented), (0.68, 0.95) at (Formula presented)], with Welch (Formula presented)-tests rejecting equivalence with the control at (Formula presented) (0.5%) and (Formula presented) (2%) and Cohen’s effect sizes (Formula presented). The best-performing conditions yield mean compressive strengths of 3.22 MPa, which exceeds the 2.0 MPa minimum required by NMX-C-441-ONNCCE-2011 for non-structural masonry by 60%. An immersion test shows that unstabilized specimens disintegrate within 2–3 min, bounding applications to non-exposed or externally protected uses and defining the primary direction for future work. Copyright © 2026 De-Obaldia-Escalante, Del-Valle-Soto, Acevedo-Parra, Montoya-Márquez and Varela-Aldás. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Multipath Routing Protocol: For Wireless Ad-hoc Networks Based on FibonacciA WANET, which stands for Wireless Ad hoc Network, is a temporary network in which nodes are connected to each other via wireless links and there is no central management. Due to its adaptive structure, WANET is very useful in situations when quick communication link setup is necessary. On the other hand, Channel Contention (CC), degrades the performance of WANET and is a major contributor to packet drops. To solve the CC problem, this paper proposes a routing protocol called Multipath Channel Contention Based Routing (MCCBR). Using MCCBR, several paths are discovered for data transmission that have minimal contention between the source and destination. Then the packets are distributed over the discovered routes based on the Fibonacci sequence. The process of searching for new paths for transmission is initiated when the number of found paths becomes less than 50%. The proposed routing protocol was tested and verified using NS2 simulator. The performance of MCCBR was evaluated against the Ad hoc On Demand Distance Vector (AODV) and Channel Contention Based Routing (CCBR) protocols. MCCBR outperformed AODV and CCBR in terms of Packet Delivery Ratio (PDR), End-to-End (E2E) delay, and normalized Media Access Control (MAC) overhead. © 2026, Zarka Private University. All rights reserved. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Autonomous Solar-Powered Smart Sensing Node: Integrating TinyML and Hybrid LoRaWAN/Wi-Fi Connectivity for Sustainable Precision Agriculture(MDPI AG, 2026-08-03) - Some of the metrics are blocked by yourconsent settings
Item type:Publication, A Multi-Camera Temporal Fusion for False Alarm Suppression in Edge-Based Weapon Surveillance(Institute of Electrical and Electronics Engineers (IEEE), 2026); - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Early-Warning Margins for Preamble-Directed Jamming in LoRa: A Dechirp-Domain Sensitivity Framework with Severity-Graded Composite-Channel Stress Testing(MDPI AG, 2026-09-27); ; ; Mex-Perera, CarlosPhysical-layer jamming directed at the LoRa preamble may become detectable before substantial degradation of the acquisition structure is evident at the receiver. The practically relevant question is therefore not only whether an attack can eventually be detected, but how much jammer-power headroom a monitor provides before the selected preamble-degradation onset is reached. This paper introduces a detection-theoretic evaluation framework that expresses this pre-degradation warning capability in decibels. Three quantities are defined: the detection sensitivity floor, i.e., the smallest relative jammer level yielding a detection probability of 0.9 at a false-alarm probability of 10−2; the preamble-based link-degradation onset, i.e., the level at which the preamble symbol error rate increases by five percentage points relative to its jamming-free baseline; and the resulting early-warning margin separating the two thresholds. The resulting early-warning margin is a power-domain measure of jammer-power headroom and should not be interpreted as elapsed warning time. The framework is evaluated over 13 isolated impairment configurations, corresponding to 221 jammer-level operating points, and complemented by a three-step severity-graded composite-channel ladder that jointly combines noise, multipath, co-channel interference, and synchronization impairments, for more than 5×105 packet-level evaluations. Alongside a convolutional autoencoder and a one-class support vector machine using the same three-channel in-phase, quadrature, and magnitude preamble representation, we evaluate a training-light dechirp-domain peak-to-mean ratio statistic and a simple score-level fusion rule; under independent threshold calibration, the fusion rule preserves the separability of its constituents but, under fading, interference, and synchronization impairments, does not recover the fixed-operating-point sensitivity of the dechirp statistic. Under strong co-channel interference, the proposed scalar statistic reaches the target detection probability at −8.5 dB, whereas the autoencoder does not reach the target detection probability anywhere within the evaluated sweep, corresponding to a sensitivity advantage of more than 14 dB and showing that the observed loss of autoencoder sensitivity is detector-dependent rather than caused by an absence of discriminative information in the received preamble. No detector dominates across all isolated impairments: the dechirp statistic is particularly effective under co-channel interference and synchronization offsets, whereas the autoencoder remains advantageous under severe multipath fading. Some learned-detector margins become negative under moderate additive noise and under severe multipath fading, and the composite-channel ladder shows a progressive degradation of detection sensitivity as joint impairment severity increases. These results provide a detector-agnostic, physically interpretable yardstick for evaluating the pre-degradation sensitivity of LoRa physical-layer jamming monitors. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Multimodal Biometric Framework for Evaluating Emotional Impact of Chromatic Manipulation in Cinematic Content(MDPI AG, 2026-05-25); ; 8 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Hypnogram-Driven Automatic Sleep Staging and a Quality-Index Assessment Through a Two-Stage LSTM-DNN Ensemble Learning Approach Using Multi-Biosignal Features for Sleep Disorder Detection(MDPI AG, 2026-06-27); - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Recent Advances in Multi-Camera Computer Vision for Industry 4.0 and Smart Cities: A Systematic Review(MDPI AG, 2026-03-25); 9