Varela Aldas, José

Preferred name

Varela Aldas, José

Main Affiliation

Centro de investigación en Mecatrónica y Sistemas Interactivos

Ambato

josevarela@uti.edu.ec

ORCID

0000-0002-4084-1424

Scopus Author ID

57192546163

Now showing 1 - 10 of 168

Some of the metrics are blocked by your
consent settings
J-DEAS: A Jamming-Driven Exponential Adaptive Sleeping Technique for Energy-Aware Mitigation in LoRa Networks
(MDPI AG, 2026-07-02)
Del-Valle-Soto, Carolina
;
Mex-Perera, Carlos
;
Velazquez, Eduard
;
Varela Aldas, José
;
Valdivia, Leonardo J.
Low-Power Wide-Area Networks based on LoRa are widely deployed in smart city, agricultural, and environmental monitoring, where their constrained energy budget makes them vulnerable to radio-frequency jamming. (1) Background: a node that keeps transmitting into a jammed channel wastes energy on undeliverable packets, yet detection and energy management are usually treated separately. (2) Methods: we present J-DEAS, a Jamming-Driven Exponential Adaptive Sleeping technique that couples a lightweight, threshold-based detector with an exponential sleep back-off scheduler. The detector uses only the RSSI and SNR reported by commodity transceivers, and a single exponentially weighted confidence variable drives the sleep interval; we analyze the decision boundary, confidence dynamics, steady-state duty cycle, and latency–energy trade-off in closed form. (3) Results: on a measurement dataset the detector reaches an AUC of 0.985 and an F1 of 0.969; under sustained jamming, J-DEAS cuts the duty cycle from 100% to 5.5% and wasted transmissions from 94.3% to 8.3%, with a single-slot median latency and a sub-2% false-sleep rate on clean channels. (4) Conclusions: the technique needs no training and no extra hardware, making it suitable for resource-constrained end devices. © 2026 by the authors.
Some of the metrics are blocked by your
consent settings
Multipath Routing Protocol: For Wireless Ad-hoc Networks Based on Fibonacci
(Zarqa University, 2026-07-04)
Sun, Haiyun
;
Varela Aldas, José
A 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 your
consent settings
Experimental and data-driven evaluation of transport-layer and application-layer security for MQTT-based IoT networks
(Elsevier BV, 2026-12)
Del-Valle-Soto, Carolina
;
Alvarez-Garcia, Maria Fernanda
;
Valdivia, Leonardo J.
;
Del-Puerto-Flores, José A.
;
Varela Aldas, José
The 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.
Some of the metrics are blocked by your
consent settings
Does the use of dedicated mobile devices in magnetism classes improve student learning?
(Frontiers Media SA, 2026-04-21)
Varela Aldas, José
;
Collay, Washington
;
Del-Valle-Soto, Carolina
;
Palacios-Navarro, Guillermo
Mobile devices have been evaluated from multiple perspectives as tools to support classroom learning; however, devices specifically dedicated to learning activities still require further research to elucidate their influence in the classroom. This study aimed to evaluate whether the use of dedicated mobile devices for teaching basic magnetism content improves learning among secondary school students. A quantitative, comparative quasi-experimental study with non-equivalent groups was conducted ((Formula presented)) with secondary school students in Ecuador. The experimental group used an educational application running on a dedicated mobile device (M5Stack Core2) and interacted with an electromagnet as a didactic peripheral, whereas the control group received a traditional teacher-centered lesson supported by slides. Learning outcomes were measured using a teacher-designed theoretical test administered at the end of a 90-min session. Mean scores were similar between groups (experimental: 7.32; control: 7.58). After verifying assumptions of normality and homogeneity of variance, an independent-samples t-test showed no statistically significant differences ((Formula presented)), and the effect size was small (Cohen’s (Formula presented)). Under the conditions evaluated, the dedicated mobile device did not produce measurable gains in knowledge performance compared to the traditional lesson. Longer interventions, larger samples, and longitudinal study designs are needed to clarify when and how this technology may provide educational benefits.
Some of the metrics are blocked by your
consent settings
A Multi-Camera Temporal Fusion for False Alarm Suppression in Edge-Based Weapon Surveillance
(Institute of Electrical and Electronics Engineers (IEEE), 2026)
Fierro Silva, Carlos Julio
;
Del-Valle-Soto, Carolina
;
Mostafa, Samih M.
;
Karim, Faten Khalid
;
Varela Aldas, José
Real-time weapon detection is a critical component of intelligent surveillance systems, particularly for perimeter monitoring applications on embedded edge platforms. However, reliable alarm generation remains challenging because false positives, temporal instability, and viewpoint inconsistencies can propagate through conventional multi-camera fusion strategies. To address these limitations, this work proposes a lightweight Adaptive Multi-Camera Temporal Fusion (ACTF) framework that combines confidence-aware evidence separation, temporal persistence, and short-window cross-camera validation at the decision level, thereby confirming detections without requiring additional neural-network inference. The framework was evaluated using a TensorRT-optimized YOLO26s detector in controlled dual-camera scenarios involving clear visibility, partial occlusion, visually ambiguous distractors, and challenging illumination. While logical OR fusion achieved higher recall, it also propagated erroneous detections; in contrast, ACTF completely suppressed the distractor-induced false alarms while maintaining competitive performance and sub-second confirmation under favorable conditions. The original NVIDIA Jetson Nano implementation achieved an average throughput of 2.4 camera-pair cycles per second, corresponding to low-rate online embedded operation, whereas an additional NVIDIA Jetson Xavier NX benchmark achieved an average of 10.2 camera-pair cycles per second. This is equivalent to 10.2 processed frames per second for each camera stream and 20.4 camera images per second in aggregate. These results support reactive real-time embedded operation on the Xavier NX platform and demonstrate that ACTF improves alarm reliability with negligible decision-level computational overhead. © 2013 IEEE.
Some of the metrics are blocked by your
consent settings
Comparative analysis of previous YOLO detectors and YOLOv26s for real-time weapon detection in video surveillance
(Frontiers Media SA, 2026-04-28)
Fierro Silva, Carlos Julio
;
Del-Valle-Soto, Carolina
;
Bran, Carlos
;
Varela Aldas, José
Real-time weapon detection in video surveillance is a critical capability for artificial intelligence assisted security systems, particularly in scenarios constrained by low latency, limited computational resources, and strict power efficiency requirements typical of edge artificial intelligence deployments. This work presents a comparative analysis of lightweight YOLO based object detectors, namely YOLOv8s, YOLOv9s, YOLOv10s, and YOLOv11s, vs. the recently introduced YOLOv26s model. In contrast to conventional benchmarking studies, this work extends the evaluation to real-world edge deployment conditions using an NVIDIA Jetson Nano device, explicitly measuring end-to-end latency, including preprocessing, inference, and post-processing stages. While earlier YOLO variants primarily relied on convolutional neural network architectures and intermediate explorations such as attention centered designs aimed to improve detection accuracy, YOLOv26 represents a paradigm shift by being designed from the ground up for low power edge devices, emphasizing architectural simplicity and deployment efficiency. To ensure a fair and reproducible evaluation, all models are trained on the same weapon detection dataset under a unified experimental protocol using small scale variants.The experimental results reveal that, despite exhibiting comparable inference times, different models show significantly different real-time performance due to variations in post-processing complexity. Specifically, models such as YOLOv8s, YOLOv9s, and YOLOv11s incur a substantial post-processing overhead, whereas YOLOv10s and YOLOv26s produce compact output representations that drastically reduce post-processing cost.This leads to a clear separation in deployment behavior, where end-to-end latency is reduced from approximately 300 ms to 125–130 ms, effectively doubling the achievable frame rate on embedded hardware. Rather than proposing a universal ranking, the study analyzes the trade offs introduced by architectural evolution and optimization strategies, providing technical criteria to support model selection under resource constrained deployment scenarios and demonstrating that post-processing efficiency, rather than inference speed alone, is the dominant factor in real-time edge performance. Copyright © 2026 Fierro Silva, Del-Valle-Soto, Bran and Varela-Aldás.
Some of the metrics are blocked by your
consent settings
Temperature control system with hysteresis for drying oven using IoT
(2023)
Bucay-Valdiviezo J.
;
Veronica-Ocana Parra S.
;
Saa F.
;
Varela Aldas, José
The fourth industrial revolution (Industry 4.0) spread to the whole world, improving all kinds of industrial processes. Thus, temperature control and monitoring of ovens through the Internet of Things (IoT) is part of this technology, especially in the automotive industry. In this work, the temperature control with hysteresis of a drying oven is carried out incorporating the IoT, with the objective of maintaining the temperature within a certain range in the drying process inside an oven. For this, a thermocouple, heat lamps, couplers and an electronic board are used. The temperature data from the drying oven is sent to the internet via WiFi using a router. The oven temperature data is recorded in the cloud for its respective subsequent analysis using the ThingSpeak services. For the results, the temperature is configured in the range of 50 ° C to 70 ° C that is monitored through a mobile application developed in App Inventor. The tests carried out demonstrate the proper functioning of the system and the high usability of the application. © 2023 IEEE.
29
Some of the metrics are blocked by your
consent settings
Voltammetric Electronic Tongues Applied to Classify Sucrose Samples Through Multivariate Analysis
(2021)
Fuentes, E.M.
;
Varela Aldas, José
;
Verdú S.
;
Meló R.G.
;
Alcañiz M.
The aim of the present study was to classify samples of sugar with different concentrations through a Voltammetric Electronic tongues (VET), with a generic pulse sequence consisted of 22 pulses ranging from –1000 mV to + 1000 mV with a duration of 20 ms/pulse over different samples such as 1.25mM, 2.5mM, 5mM and 10mM, of sucrose concentration, these were measured 4 times each concentration and the test was developed 4 times, giving a total number of 506.880 data supervised learning algorithm using support vector machine was employed, choosing a linear function as a classifying element. In the training, 75% of the data was used to determine the coefficients of the classification function, and the remaining (25%) was used to evaluate the performance of the proposal. The results showed a concordance of more than 80% in the separation of sample, allowing to conclude as acceptable the performance of the classifier and the data acquired through the voltammetric tongue. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.
10
Some of the metrics are blocked by your
consent settings
An assessment of a ROS class using an educational mobile robot
(2024)
Varela Aldas, José
;
Junta, Christian
;
Buele, Jorge
;
Guillermo Palacios-Navarro
The Robot Operating System (ROS) is a middleware that standardizes robot programming, both in simulation and with real equipment. Despite this open-source tool being available for several years, there's still a need to enhance its utilization in robotics education across all educational levels. In this study, a ROS class is assessed among university students using a commercial educational robot. The primary objective is to measure academic emotions in learning and student performance to determine the impact of the class using the open-access tool from a GitHub repository (https://github.com/joseVarelaAldas/ROS-Crowbot). This tool is based on the rosserial package, compatible with the ESP32 board. For class, CrowBot robots connected to the local wireless network via WiFi are used. TThe participants in this study were eight students from an electronics degree program at a higher education institution, who had no prior experience with ROS and received practical training using the educational mobile robot. For data collection on class performance, three parameters are assessed: execution time, functionality, and motivation, and to measure academic emotions, a validated self-report instrument is used. The results show an overall performance of 82.1%, and in the self-report on academic emotions, a high score in enjoyment (95%) and the lowest score in boredom (24.1 %) were obtained. In conclusion, the repository provides an interesting, practical, and accessible tool for an introduction into the world of robotics using ROS.
23
Some of the metrics are blocked by your
consent settings
Effects of immersive virtual reality on the heart rate of athlete’s warm-up
(2019)
Varela Aldas, José
;
Palacios-Navarro G.
;
García-Magariño I.
;
Fuentes E.M.
An adequate warm-up prior to intensive exercise can bring benefits to athletes, these requirements may vary depending on the physical activity and the training needs. The immersive virtual reality could have benefits in the warm-up and it can be determined by physiological data of the athlete. This work presents a mobile virtual reality application to stimulate the warm-up of an athlete using a standard treadmill, where the developed application is composed of a pleasant and stimulating environment. A Smartphone and the Gear VR are used as an HMD device, and wireless headphones are placed in the users, in addition, the heart rate of the athletes is monitored using a Polar H7 sensor. Experimental results are obtained in athletes with similar characteristics and conditions, identified a direct relation of the virtual environment with the pulsations per minute (ppm), denoting pulsations greater than usual in case of the stimulating environment and lower pulsations for the pleasant environment. Finally, a usability test is performed that shows the level of sociability of the system. © Springer Nature Switzerland AG 2019.
Scopus© Citations 7 13

Varela Aldas, José

Filters

Settings

Sort By

Results per page