Del-Valle-Soto, Carolina
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Del-Valle-Soto, Carolina
Official Name
del Valle Soto, Carolina
Alternative Name
cvalle
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Scopus Author ID
56257456700
Researcher ID
T-5779-2017

Research Output

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Distributed software architecture for accessing the NFT-Vehicle

2024 , López-Pimentel, Juan Carlos , Luis Alberto Morales-Rosales , Del-Valle-Soto, Carolina , José Alberto Del Puerto-Flores

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Wireless Sensor Network Energy Model and Its Use in the Optimization of Routing Protocols

2020 , Del-Valle-Soto, Carolina , Carlos Mex-Perera , Juan Arturo Nolazco-Flores , Velázquez, Ramiro , Rossa Sierra, Alberto

In this study, a Wireless Sensor Network (WSN) energy model is proposed by defining the energy consumption at each node. Such a model calculates the energy at each node by estimating the energy of the main functions developed at sensing and transmitting data when running the routing protocol. These functions are related to wireless communications and measured and compared to the most relevant impact on an energy standpoint and performance metrics. The energy model is validated using a Texas Instruments CC2530 system-on-chip (SoC), as a proof-of-concept. The proposed energy model is then used to calculate the energy consumption of a Multi-Parent Hierarchical (MPH) routing protocol and five widely known network sensors routing protocols: Ad-hoc On-demand Distance Vector (AODV), Dynamic Source Routing (DSR), ZigBee Tree Routing (ZTR), Low Energy Adaptive Clustering Hierarchy (LEACH), and Power Efficient Gathering in Sensor Information Systems (PEGASIS). Experimental test-bed simulations were performed on a random layout topology with two collector nodes. Each node was running under different wireless technologies: Zigbee, Bluetooth Low Energy, and LoRa by WiFi. The objective of this work is to analyze the performance of the proposed energy model in routing protocols of diverse nature: reactive, proactive, hybrid and energy-aware. Experimental results show that the MPH routing protocol consumes 16%, 13%, and 5% less energy when compared to AODV, DSR, and ZTR, respectively; and it presents only 2% and 3% of greater energy consumption with respect to the energy-aware PEGASIS and LEACH protocols, respectively. The proposed model achieves a 97% accuracy compared to the actual performance of a network. Tests are performed to analyze the consumption of the main tasks of a node in a network.

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Energy Recycling Laboratory Experimental Test Bench for Three-Phase FACTS Devices Prototypes

2019 , Jesus E. Valdez-Resendiz , Mayo Maldonado, Jonathan , Rosas-caro, Julio , Alejo-Reyes, Avelina , Armando Llamas-Terres , Valderrabano-Gonzalez, Antonio , Del-Valle-Soto, Carolina , Valdivia, Leonardo

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Usability evaluation of foot-based interfaces for blind travelers

2020 , Velázquez, Ramiro , Edwige Pissaloux , Del-Valle-Soto, Carolina , Aime Lay-Ekuakille , Bruno Ando

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Photonic millimeter-wave bridge for multi-Gbps passive optical networks

2018 , Ivan Aldaya , Del-Valle-Soto, Carolina , Gabriel Campuzano , Elias Giacoumidis , Rafael González , Gerardo Castañón

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Enhancing Elderly Care through Low-Cost Wireless Sensor Networks and Artificial Intelligence: A Study on Vital Sign Monitoring and Sleep Improvement

2024 , Del-Valle-Soto, Carolina , Ramon A. Briseño , Velázquez, Ramiro , Gabriel Guerra-Rosales , Santiago Perez-Ochoa , Isaac H. Preciado-Bazavilvazo , Paolo Visconti , José Varela-Aldás

This research explores the application of wireless sensor networks for the non-invasive monitoring of sleep quality and vital signs in elderly individuals, addressing significant challenges faced by the aging population. The study implemented and evaluated WSNs in home environments, focusing on variables such as breathing frequency, deep sleep, snoring, heart rate, heart rate variability (HRV), oxygen saturation, Rapid Eye Movement (REM sleep), and temperature. The results demonstrated substantial improvements in key metrics: 68% in breathing frequency, 68% in deep sleep, 70% in snoring reduction, 91% in HRV, and 85% in REM sleep. Additionally, temperature control was identified as a critical factor, with higher temperatures negatively impacting sleep quality. By integrating AI with WSN data, this study provided personalized health recommendations, enhancing sleep quality and overall health. This approach also offered significant support to caregivers, reducing their burden. This research highlights the cost-effectiveness and scalability of WSN technology, suggesting its feasibility for widespread adoption. The findings represent a significant advancement in geriatric health monitoring, paving the way for more comprehensive and integrated care solutions.

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Performance of MRC Detection in OFDM System with Virtual Carriers over V2V Channels

2019 , Del-Puerto-Flores, J. Alberto , Joaquin Cortez , Gutierrez, Carlos A. , Del-Valle-Soto, Carolina , Velázquez, Ramiro , Valdivia, Leonardo

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Implications for Serious Game Design: Quantification of Cognitive Stimulation in Virtual Reality Puzzle Games through MSC and SpEn EEG Analysis

2024 , Gomez Romero Borquez, Jesus Alberto , Del-Valle-Soto, Carolina , Del-Puerto-Flores, J. Alberto , Castillo-Soria, Francisco R. , Maciel-Barboza F.M.

This paper investigates the cognitive stimulation experienced by players engaging in virtual reality (VR) puzzle games through the analysis of electroencephalography (EEG) data. The study employs magnitude-square coherence (MSC) and spectral entropy (SpEn) metrics to quantify neural activity patterns associated with problem-solving processes during gameplay. Results reveal unique coherence and entropy profiles across different VR gaming tasks, with Tetris gameplay eliciting heightened coherence and entropy values compared to other games. Specifically, Tetris demonstrates increased coherence between frontal and temporal brain regions, indicative of enhanced visuospatial processing and decision making. These findings underscore the importance of considering both spectral coherence and entropy when assessing the cognitive effects of video game tasks on brain activity. Insights from this study may inform the design of serious VR games aimed at promoting cognitive development and problem-solving skills in players.

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Affect-Driven VR Environment for Increasing Muscle Activity in Assisted Gait Rehabilitation

2024 , Rodríguez, Jafet , Del-Valle-Soto, Carolina , Gonzalez Sanchez, Javier

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Decentralization: The Failed Promise of Cryptocurrencies

2019 , Valdivia, Leonardo , Del-Valle-Soto, Carolina , Rodríguez, Jafet , Alcaraz Rivera, Miguel

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