Revisión sistemática PRISMA 2020 sobre redes de sensores ópticos inteligentes integradas a plataformas IoT para el monitoreo ambiental en tiempo real y la integración del marco conceptual MCS-RSOI
DOI:
https://doi.org/10.29057/estr.v14iEspecial.18603Palabras clave:
Sensores ópticos, Internet de las cosas, Inteligencia Artificial, Monitoreo ambiental, revisión sistemática, PRISMA 2020Resumen
La convergencia entre la fotónica aplicada, las plataformas de Internet de las Cosas (IoT) y la inteligencia artificial (IA) ha generado una creciente producción científica sobre redes de sensores ópticos inteligentes para el monitoreo ambiental en tiempo real, sin un marco conceptual que articule sistemáticamente los tres dominios. El presente reporte de investigación tiene como objetivo sintetizar la evidencia disponible y proponer un marco conceptual integrador denominado MCS-RSOI (Marco Conceptual de Sistemas de Redes de Sensores Ópticos Inteligentes). Se aplicó el protocolo PRISMA 2020 en cinco bases de datos bibliográficas: Scopus, IEEE Xplore, ScienceDirect, SpringerLink y MDPI. El corpus final comprende 220 documentos, sometidos a codificación temática con doble revisión independiente (Porcentaje de Acuerdo Intracodificador: PAI = 91.3%; coeficiente Kappa de Cohen: κ = 0.87). El análisis temático identificó 23 principios de diseño organizados en el modelo de cinco capas MCS-RSOI: Percepción Óptica, Gestión Energética y Comunicación, Cómputo en el Borde, Plataforma IoT y Gestión de Datos, e Inteligencia Analítica. La valoración del marco por un panel de cinco expertos mediante el instrumento CUVE-RSOI obtuvo una Media de Valoración Experta (MVE) = 3.4/4.0 (85.0%), por encima del umbral de aceptación del 80% (Lynn, 1986). La brecha crítica identificada es la escasa implementación de IA explicable (XAI) en sistemas de monitoreo ambiental, formalizada en el marco como un principio de diseño obligatorio. El MCS-RSOI constituye una contribución teórica original para investigadores, diseñadores de sistemas y reguladores en el ámbito del monitoreo ambiental automático.
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[1] Akpakwu, G. A., Silva, B. J., Hancke, G. P., y Abu-Mahfouz, A. M. (2018). A survey on 5G networks for the Internet of Things: Communication technologies and challenges. IEEE Access, 6, 3619–3647. https://doi.org/10.1109/ACCESS.2017.2779844 Alotaibi, E., y Nassif, N. (2024). Artificial intelligence in environmental monitoring: In-depth analysis. Discover Artificial Intelligence, 4, 84. https://doi.org/10.1007/s44163-024-00198-1
[2] Bandara, R., Jayasinghe, A., y Retscher, G. (2025). The integration of IoT (Internet of Things) sensors and location-based services for water quality monitoring: A systematic literature review. Sensors, 25(6), 1918. https://doi.org/10.3390/s25061918
[3] Booth, A., Sutton, A., Clowes, M., y Martyn-St James, M. (2026). Systematic approaches to a successful literature review (3.era ed.). SAGE Publications. https://doi.org/10.4135/9781036232009
[4] Darwish, M., Abd-Elaziem, W., Elsheikh, A., y Zayed, A. (2024). Advancements in nanomaterials for nanosensors: A comprehensive review. Nanoscale Advances, 6 (16), 4015-4046. https://doi.org/10.1039/d4na00214h
[5] de Camargo, E., Spanhol, F., Slongo, J., da Silva, M., Pazinato, J., de Lima Lobo, A., . . . Oyamada, M. (2023). Low-Cost Water Quality Sensors for IoT: A Systematic Review. Sensors, 23(9), 1-27. https://doi.org/10.3390/s23094424
[6] deSouza, P., Kahn, R., Tehya, S., Obermann, W., Crawford, B., Wang, A., . . . Kinney, P. (2022). Calibrating networks of low-cost air quality sensors. Atmospheric Measurement Techniques, 15(21), 6309-6328. https://doi.org/10.5194/amt-15-6309-2022
[7] Essamlali, I., Nhaila, H., y El Khaili, M. (2024). Advances in machine learning and IoT for water quality monitoring: A comprehensive review. Heliyon, 10(6), e27920. https://doi.org/10.1016/j.heliyon.2024.e27920
[8] European Parliament y Council of the European Union. (2024). Regulation (EU) 2024/1689: Artificial Intelligence Act. Official Journal of the European Union, L 2024/1689. https://eur-lex.europa.eu/eli/reg/2024/1689/oj
[9] Fdez-Sanromán, A., Bernárdez-Rodas, N., Rosales, E., Pazos, M., González-Romero, E., y Sanromán, M. (2025). Biosensor Technologies for Water Quality: Detection of Emerging Contaminants and Pathogens. Biosensors, 15(3). https://doi.org/10.3390/bios15030189
[10] Gubbi, J., Buyya, R., Marusic, S., y Palaniswami, M. (2013). Haddaway, N. R., Macura, B., Whaley, P., y Pullin, A. S. (2018). ROSES Reporting standards for Systematic Evidence Syntheses. Environmental Evidence, 7(7). https://doi.org/10.1186/s13750-018-0121-7 Internet of Things (IoT): A vision, architectural elements, and future directions. Future Generation Computer Systems, 29(7), 1645-1660. https://doi.org/10.1016/j.future.2013.01.010
[11] Kairuz-Cabrera, D., Hernandez-Rodriguez, V., Schalm, O., Martinez, A., Merino Laso, P., y Alejo-Sánchez, D. (2024). Development of a Unified IoT Platform for Assessing Meteorological and Air Quality Data in a Tropical Environment. Sensors, 24(9). https://doi.org/10.3390/s24092729
[12] Khizar, S., Zine, N., Jaffrezic-Renault, N., Elaissari, A., y Errachid, A. (2022). Prospective analytical role of sensors for environmental screening and monitoring. TrAC Trends in Analytical Chemistry, 157. https://doi.org/10.1016/j.trac.2022.116751
[13] Kumar, M., Khamis, K., Stevens, R., Hannah, D., y Bradley, C. (2024). In-situ optical water quality monitoring sensors: Applications, challenges, and future opportunities. Frontiers in Water, 6, 1380133. https://doi.org/10.3389/frwa.2024.1380133
[14] Landis, J. R., y Koch, G. G. (1977). The measurement of observer agreement for categorical data. Biometrics, 33(1), 159-174. https://doi.org/10.2307/2529310
[15] Lawshe, C. H. (1975). A quantitative approach to content validity. Personnel Psychology, 28(4), 563-575. https://doi.org/10.1111/j.1744-6570.1975.tb01393.x
[16] Lynn, M. R. (1986). Determination and quantification of content validity. Nursing Research, 35(6), 382-386. https://doi.org/10.1097/00006199-198611000-00017
[17] Miles, M. B., Huberman, A. M., y Saldana, J. (2018). Mineraud, J., Mazhelis, O., Su, X., y Tarkoma, S. (2016). A gap analysis of Internet-of-Things platforms. Computer Communications, 89–90, 5–16. https://doi.org/10.1016/j.comcom.2016.03.015 Qualitative data analysis: A methods sourcebook (4.a ed.). SAGE Publications.
[18] Miller, T., Irmina, D., Kostecka, E., Kozlovska, P., Lobodzinska, A., Sokolowska, S., y Nowy, A. (2025). Integrating AI agents with IoT for enhanced environmental monitoring. Electronics, 14(4), 696. https://doi.org/10.3390/electronics14040696
[19] Moiroux-Arvis, L., Royer, L., Sarramia, D., De Sousa, G., Claude, A., Latour, D., y Chanet, J.-P. (2023). ConnecSenS, a versatile IoT platform for environment monitoring. Sensors, 23(6), 2896. https://doi.org/10.3390/s23062896
[20] Naifar, S., Kanoun, O., y Trigona, C. (2024). Energy harvesting technologies for Internet of Things and Wireless Sensor Networks. Sensors, 24(14), 4688. https://doi.org/10.3390/s24144688
[21] Tong, A., Flemming, K., McInnes, E., Oliver, S., y Craig, J. (2012). Enhancing transparency in reporting the synthesis of qualitative research: ENTREQ. BMC Medical Research Methodology, 12(181). https://doi.org/10.1186/1471-2288-12-181 Tricco, A. C., Lillie, E., Zarin, W., O'Brien, K. K., Colquhoun, H., Levac, D., Moher, D., Peters, M. D. J., Horsley, T., Weeks, L., Hempel, S., Akl, E. A., Chang, C., McGowan, J., Stewart, L., Hartling, L., Aldcroft, A., Wilson, M. G., Garritty, C., … Straus, S. E. (2018). PRISMA extension for scoping reviews (PRISMA-ScR): Checklist and explanation. Annals of Internal Medicine, 169(7), 467–473. https://doi.org/10.7326/M18-0850 Narayana, T., Venkatesh, C., Kiran, A., Babu, C., Kumar, A., Khan, S., y Quasim, M. (2024). Advances in real-time smart monitoring of environmental parameters using IoT and sensors. Heliyon, 10(7), e28195. https://doi.org/10.1016/j.heliyon.2024.e28195
[22] Page, M. J., McKenzie, J. E., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D., Shamseer, L., Tetzlaff, J. M., Akl, E. A., Brennan, S. E., Chou, R., Ghersi, D., Guyatt, G. H., Hrobjartsson, A., Lalu, M. M., Li, T., Loder, E. W., Mayo-Wilson, E., McDonald, S., … Moher, D. (2021). The PRISMA 2020 statement: An updated guideline for reporting systematic reviews. BMJ, 372, n71. https://doi.org/10.1136/bmj.n71
[23] Pendao, C., y Silva, I. (2022). Optical fiber sensors and sensing networks: Overview of the main principles and applications. Sensors, 22(19), 7554. https://doi.org/10.3390/s22197554
[24] Popescu, S., Mansoor, S., Wani, O., Kumar, S., Sharma, V., Sharma, A., y Chung, Y. (2024). Artificial intelligence and IoT driven technologies for environmental pollution monitoring and management. Frontiers in Environmental Science, 12, 1336088. https://doi.org/10.3389/fenvs.2024.1336088
[25] Xiao, Y., y Watson, M. (2019). Guidance on conducting a systematic literature review. Journal of Planning Education and Research, 39(1), 93-112. https://doi.org/10.1177/0739456X17723971
[26] Zanella, A., Bui, N., Castellani, A., Vangelista, L., y Zorzi, M. (2014). Internet of Things for smart cities. IEEE Internet of Things Journal, 1(1), 22-32. https://doi.org/10.1109/JIOT.2014.2306328
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Derechos de autor 2026 Yatzuki L. De Castilla Rosales, Edgar Rosas Amaro, Hilario Martines Arano
Esta obra está bajo una licencia internacional Creative Commons Atribución-NoComercial-SinDerivadas 4.0.
