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Recent Advances in Multi-Camera Computer Vision for Industry 4.0 and Smart Cities: A Systematic Review
(2026)
Fierro Silva, Carlos Julio
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Carolina Del-Valle-Soto
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Samih M. Mostafa
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Varela Aldas, José
The rapid deployment of surveillance cameras in urban, industrial, and domestic environments has intensified the need for intelligent systems capable of analyzing video streams beyond the limitations of single-camera setups. Unlike traditional single-camera approaches, multi-camera systems expand spatial coverage, reduce blind spots, and enable consistent tracking of people and objects across non-overlapping views, thereby improving robustness against occlusions and viewpoint changes. This article presents a comprehensive review of multi-camera vision systems published between 2020 and 2025, covering application domains including public security and biometrics, intelligent transportation, smart cities and IoT, healthcare monitoring, precision agriculture, industry and robotics, pan–tilt–zoom (PTZ) camera networks, and emerging areas such as retail and forensic analysis. The review synthesizes predominant technical approaches, including deep-learning-based detection, multi-target multi-camera tracking (MTMCT), re-identification (Re-ID), spatiotemporal fusion, and edge computing architectures. Persistent challenges are identified, particularly in inter-camera data association, scalability, computational efficiency, privacy preservation, and dataset availability. Emerging trends such as distributed edge AI, cooperative camera networks, and active perception are discussed to outline future research directions toward scalable, privacy-aware, and intelligent multi-camera infrastructures.
5
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Stochastic Characterization of MAC-Level Reliability and Reassociation Dynamics in IEEE 802.15.4 Networks for Smart Grid Applications
(2026)
Carolina Del-Valle-Soto
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José A. Del-Puerto-Flores
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Ramiro Velázquez
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Juan Sebastián Botero-Valencia
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Leonardo J. Valdivia
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.
6
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Electrodermal Response Patterns and Emotional Engagement Under Continuous Algorithmic Video Stimulation: A Multimodal Biometric Analysis
(2026)
Carolina Del-Valle-Soto
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Violeta Corona
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Jesus GomezRomero-Borquez
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David Contreras-Tiscareno
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Diego Sebastian Montoya-Rodriguez
Excessive use of short-form video platforms such as TikTok has raised growing concerns about digital addiction and its impact on young users’ emotional well-being. This study examines the relationship between continuous TikTok exposure and emotional engagement in young adults aged 20–23 through a multimodal experimental design. The purpose of this research is to determine whether emotional engagement increases, remains stable, or declines during prolonged exposure and to assess the degree of correspondence between facially inferred engagement and physiological arousal. To achieve this, multimodal biometric data were collected using the iMotions platform, integrating galvanic skin response (GSR) sensors and facial expression analysis via Affectiva’s AFFDEX SDK 5.1. Engagement levels were binarized using a logistic transformation, and a binomial test was conducted. GSR analysis, merged with a 50 ms tolerance, revealed no significant differences in skin conductance between engaged and non-engaged states. Findings indicate that although TikTok elicits strong initial emotional engagement, engagement levels significantly decline over time, suggesting habituation and emotional fatigue. The results refine our understanding of how algorithm-driven, short-form content affects users’ affective responses and highlight the limitations of facial metrics as sole indicators of physiological arousal. Implications for theory include advancing multimodal models of emotional engagement that account for divergences between expressivity and autonomic activation. Implications for practice emphasize the need for ethical platform design and improved digital well-being interventions. The originality and value of this study lie in its controlled experimental approach that synchronizes facial and physiological signals, offering objective evidence of the temporal decay of emotional engagement during continuous TikTok use and underscoring the complexity of measuring affect in highly stimulating digital environments.
7
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A Literature Review on Real-Time Crime Detection Using Deep Learning and Edge Computing
(2025)
Carlos Julio Fierro Silva
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Carolina Del-Valle-Soto
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Varela Aldas, José
The growing need for security in urban and commercial environments has driven the development of intelligent surveillance systems capable of detecting criminal activities in real time. While traditional cloud-based solutions offer advanced capabilities, they face limitations in terms of latency, privacy, and bandwidth usage. In this context, Edge Computing has emerged as a promising alternative, enabling local and fast processing of video data through embedded artificial intelligence models. This review article presents a comprehensive analysis of recent advances in real-time detection of thefts and weapons using Edge Artificial Intelligence (Edge AI). A total of 30 scientific articles published between 2018 and 2025 were selected and categorized, taking into account detection models, computing platforms, evaluation metrics, datasets, and real-world applications. The results highlight the predominant use of lightweight convolutional neural networks, especially YOLO-based models, implemented on devices such as Jetson Nano, Raspberry Pi, and Google Coral. Key challenges addressed include detection under lowlight conditions, identification of small or partially concealed weapons, and the reduction of false positives. The review identifies gaps in the current literature, such as the lack of annotated real-world datasets and the need for behavior-based models in retail contexts. Finally, emerging trends and future research directions are discussed, aiming at the development of efficient, accurate, and privacy-respecting Edge AI systems for real-time security surveillance. © 2025 IEEE.
14
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Genre-Sensitive Prediction of Emotional Arousal in Virtual Reality: A Neural Modeling Approach Using Skin Conductance Peaks
(2025)
Carolina Del-Valle-Soto
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Demián Velasco Gómez Llanos
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Santiago Arreola Munguía
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Marco Antonio Manjarrez Fernandez
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Juan Pablo Villaseñor Navares
Understanding how different virtual reality (VR) game genres modulate physiological arousal is crucial for designing emotionally adaptive immersive systems. This study introduces a novel experimental framework combining high-resolution Skin Conductance Response (SCR) data and neural predictive modeling to compare emotional activation across horror, skill-based, and exercise VR games. Using Galvanic Skin Response (GSR) sensors, we recorded phasic peaks in SCR from 25 university-aged participants during gameplay sessions with controlled exposure times and standardized transitions. However, given the minimal difference relative to the large variability, this observation should be considered preliminary and specific to the tested games and cohort. A feed-forward neural network was developed to forecast individual arousal levels based solely on genre-induced features, achieving strong predictive performance. This dual contribution empirical genre comparison and lightweight predictive modeling offers a scalable tool for integrating emotional responsiveness into VR systems without continuous biosignal monitoring. The findings not only advance the state of the art in affective computing but also open new avenues for therapeutic, educational, and entertainment applications grounded in physiological adaptation
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