Mejores Prácticas en la Implementación del Edge Computing: Un Enfoque Basado en Casos de Éxito
DOI:
https://doi.org/10.33936/isrtic.v8i2.6879Palabras clave:
Edge Computing, mejores prácticas, videovigilancia, agricultura, atención médicaResumen
El Edge Computing ha emergido como una tecnología clave para transformar diversos sectores industriales, ofreciendo capacidades avanzadas de procesamiento y almacenamiento de datos cerca del punto de generación. Este estudio presenta una revisión sistemática de la literatura sobre la implementación de esta tecnología en videovigilancia, agricultura y atención médica, con el objetivo de identificar mejores prácticas a partir de casos de éxito utilizando la metodología PRISMA. Se analizaron 42 estudios publicados entre 2020 y 2024, evaluando aspectos como seguridad, interoperabilidad, eficiencia operativa y escalabilidad. Los resultados revelan un alto cumplimiento en eficiencia operativa, requisitos específicos del negocio y rendimiento de puntos finales. Sin embargo, se identificaron desafíos en seguridad, resiliencia e integración de middleware. Basados en estos hallazgos, se propone un conjunto de mejores prácticas que abordan estos aspectos críticos, incluyendo adaptabilidad e intercambio de datos. Esta propuesta busca proporcionar un marco de referencia para la implementación potencial en sectores clave, ayudando a las organizaciones a optimizar sus procesos y aprovechar los beneficios de esta innovación de manera más efectiva.
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Abbasi, R., Martinez, P., & Ahmad, R. (2022). The digitization of agricultural industry – a systematic literature review on agriculture 4.0. Smart Agricultural Technology, 2, 100042. https://doi.org/10.1016/j.atech.2022.100042
Abreha, H. G., Hayajneh, M., & Serhani, M. A. (2022). Federated Learning in Edge Computing: A Systematic Survey. Sensors, 22(2), 450. https://doi.org/10.3390/s22020450
Aguilera, C. A., Figueroa-Flores, C., Aguilera, C., & Navarrete, C. (2023). Comprehensive Analysis of Model Errors in Blueberry Detection and Maturity Classification: Identifying Limitations and Proposing Future Improvements in Agricultural Monitoring. Agriculture, 14(1), 18. https://doi.org/10.3390/agriculture14010018
Alam, M. U., & Rahmani, R. (2023). FedSepsis: A Federated Multi-Modal Deep Learning-Based Internet of Medical Things Application for Early Detection of Sepsis from Electronic Health Records Using Raspberry Pi and Jetson Nano Devices. Sensors, 23(2), 970. https://doi.org/10.3390/s23020970
Ali, A., Ali, H., Saeed, A., Ahmed Khan, A., Tin, T. T., Assam, M., Ghadi, Y. Y., & Mohamed, H. G. (2023). Blockchain-Powered Healthcare Systems: Enhancing Scalability and Security with Hybrid Deep Learning. Sensors, 23(18), 7740. https://doi.org/10.3390/s23187740
Alwakeel, A. M. (2021). An Overview of Fog Computing and Edge Computing Security and Privacy Issues. Sensors, 21(24), 8226. https://doi.org/10.3390/s21248226
Alzu’bi, A., Alomar, A., Alkhaza’leh, S., Abuarqoub, A., & Hammoudeh, M. (2024). A Review of Privacy and Security of Edge Computing in Smart Healthcare Systems: Issues, Challenges, and Research Directions. Tsinghua Science and Technology, 29(4), 1152–1180. https://doi.org/10.26599/TST.2023.9010080
Alzuhair, A., & Alghaihab, A. (2023). The Design and Optimization of an Acoustic and Ambient Sensing AIoT Platform for Agricultural Applications. Sensors, 23(14), 6262. https://doi.org/10.3390/s23146262
Armijo, A., & Zamora-Sánchez, D. (2024). Integration of Railway Bridge Structural Health Monitoring into the Internet of Things with a Digital Twin: A Case Study. Sensors, 24(7), 2115. https://doi.org/10.3390/s24072115
Assunção, E., Gaspar, P. D., Alibabaei, K., Simões, M. P., Proença, H., Soares, V. N. G. J., & Caldeira, J. M. L. P. (2022). Real-Time Image Detection for Edge Devices: A Peach Fruit Detection Application. Future Internet, 14(11), 323. https://doi.org/10.3390/fi14110323
Awad, A. I., Fouda, M. M., Khashaba, M. M., Mohamed, E. R., & Hosny, K. M. (2023). Utilization of mobile edge computing on the Internet of Medical Things: A survey. ICT Express, 9(3), 473–485. https://doi.org/10.1016/j.icte.2022.05.006
Bai, T., Pan, C., Deng, Y., Elkashlan, M., Nallanathan, A., & Hanzo, L. (2020). Latency Minimization for Intelligent Reflecting Surface Aided Mobile Edge Computing. IEEE Journal on Selected Areas in Communications, 38(11), 2666–2682. https://doi.org/10.1109/JSAC.2020.3007035
Baktayan, A. A., Thabit Zahary, A., & Ahmed Al-Baltah, I. (2024). A Systematic Mapping Study of UAV-Enabled Mobile Edge Computing for Task Offloading. IEEE Access, 12, 101936–101970. https://doi.org/10.1109/ACCESS.2024.3431922
Bavaresco, R., Silveira, D., Reis, E., Barbosa, J., Righi, R., Costa, C., Antunes, R., Gomes, M., Gatti, C., Vanzin, M., Junior, S. C., Silva, E., & Moreira, C. (2020). Conversational agents in business: A systematic literature review and future research directions. Computer Science Review, 36, 100239. https://doi.org/10.1016/j.cosrev.2020.100239
Bommu, S., M, A. K., Babburu, K., N, S., Thalluri, L. N., G, V. G., Gopalan, A., Mallapati, P. K., Guha, K., Mohammad, H. R., & S, S. K. (2023). Smart City IoT System Network Level Routing Analysis and Blockchain Security Based Implementation. Journal of Electrical Engineering & Technology, 18(2), 1351–1368. https://doi.org/10.1007/s42835-022-01239-4
Bua, C., Adami, D., & Giordano, S. (2024). GymHydro: An Innovative Modular Small-Scale Smart Agriculture System for Hydroponic Greenhouses. Electronics, 13(7), 1366. https://doi.org/10.3390/electronics13071366
Cárdenas Villavicencio, O. E., Zea Ordoñez, M. P., Honores Tapia, J. A., & Lamar Peña, F. S. (2024). Visiones del Futuro Urbano: El Paradigma Teórico de las Smart Cities. Informática y Sistemas: Revista de Tecnologías de la Informática y las Comunicaciones, 8(1). https://doi.org/10.33936/isrtic.v8i1.6324
Chahed, H., Usman, M., Chatterjee, A., Bayram, F., Chaudhary, R., Brunstrom, A., Taheri, J., Ahmed, B. S., & Kassler, A. (2023). AIDA—A holistic AI-driven networking and processing framework for industrial IoT applications. Internet of Things, 22, 100805. https://doi.org/10.1016/j.iot.2023.100805
Chen, S., Li, Q., Zhou, M., & Abusorrah, A. (2021). Recent Advances in Collaborative Scheduling of Computing Tasks in an Edge Computing Paradigm. Sensors, 21(3), 779. https://doi.org/10.3390/s21030779
Chui, K. T., Gupta, B. B., Liu, J., Arya, V., Nedjah, N., Almomani, A., & Chaurasia, P. (2023). A Survey of Internet of Things and Cyber-Physical Systems: Standards, Algorithms, Applications, Security, Challenges, and Future Directions. Information, 14(7), 388. https://doi.org/10.3390/info14070388
D. N, S., B, A., Hegde, S., Abhijit, C. S., & Ambesange, S. (2024). FedCure: A Heterogeneity-Aware Personalized Federated Learning Framework for Intelligent Healthcare Applications in IoMT Environments. IEEE Access, 12, 15867–15883. https://doi.org/10.1109/ACCESS.2024.3357514
Du, Y., Wang, Z., & Leung, V. C. M. (2021). Blockchain-Enabled Edge Intelligence for IoT: Background, Emerging Trends and Open Issues. Future Internet, 13(2), 48. https://doi.org/10.3390/fi13020048
Elbagoury, B. M., Vladareanu, L., Vlădăreanu, V., Salem, A. B., Travediu, A.-M., & Roushdy, M. I. (2023). A Hybrid Stacked CNN and Residual Feedback GMDH-LSTM Deep Learning Model for Stroke Prediction Applied on Mobile AI Smart Hospital Platform. Sensors, 23(7), 3500. https://doi.org/10.3390/s23073500
Emmi, L., Fernández, R., Gonzalez-de-Santos, P., Francia, M., Golfarelli, M., Vitali, G., Sandmann, H., Hustedt, M., & Wollweber, M. (2023). Exploiting the Internet Resources for Autonomous Robots in Agriculture. Agriculture, 13(5), 1005. https://doi.org/10.3390/agriculture13051005
Estrada-López, J. J., Vázquez-Castillo, J., Castillo-Atoche, A., Osorio-de-la-Rosa, E., Heredia-Lozano, J., & Castillo-Atoche, A. (2023). A Sustainable Forage-Grass-Power Fuel Cell Solution for Edge-Computing Wireless Sensing Processing in Agriculture 4.0 Applications. Energies, 16(7), 2943. https://doi.org/10.3390/en16072943
Famá, F., Faria, J. N., & Portugal, D. (2022). An IoT-based interoperable architecture for wireless biomonitoring of patients with sensor patches. Internet of Things, 19, 100547. https://doi.org/10.1016/j.iot.2022.100547
Fernández, E. I., Jara Valera, A. J., & Fernández Breis, J. T. (2024). Embedded machine learning of IoT streams to promote early detection of unsafe environments. Internet of Things, 25, 101128. https://doi.org/10.1016/j.iot.2024.101128
Gehlot, A., Malik, P. K., Singh, R., Akram, S. V., & Alsuwian, T. (2022). Dairy 4.0: Intelligent Communication Ecosystem for the Cattle Animal Welfare with Blockchain and IoT Enabled Technologies. Applied Sciences, 12(14), 7316. https://doi.org/10.3390/app12147316
Hyysalo, J., Dasanayake, S., Hannu, J., Schuss, C., Rajanen, M., Leppänen, T., Doermann, D., & Sauvola, J. (2022). Smart mask – Wearable IoT solution for improved protection and personal health. Internet of Things, 18, 100511. https://doi.org/10.1016/j.iot.2022.100511
Ijaz, M., Li, G., Lin, L., Cheikhrouhou, O., Hamam, H., & Noor, A. (2021). Integration and Applications of Fog Computing and Cloud Computing Based on the Internet of Things for Provision of Healthcare Services at Home. Electronics, 10(9), 1077. https://doi.org/10.3390/electronics10091077
ISO/IEC. (2020). Internet of Things (IoT) standards. https://www.iso.org/obp/ui/
Kalyani, Y., Vorster, L., Whetton, R., & Collier, R. (2024). Application Scenarios of Digital Twins for Smart Crop Farming through Cloud–Fog–Edge Infrastructure. Future Internet, 16(3), 100. https://doi.org/10.3390/fi16030100
Kim, J., Lee, J., & Kim, T. (2021). AdaMM: Adaptive Object Movement and Motion Tracking in Hierarchical Edge Computing System. Sensors, 21(12), 4089. https://doi.org/10.3390/s21124089
Kolosov, D., Kelefouras, V., Kourtessis, P., & Mporas, I. (2023). Contactless Camera-Based Heart Rate and Respiratory Rate Monitoring Using AI on Hardware. Sensors, 23(9), 4550. https://doi.org/10.3390/s23094550
Koubaa, A., Ammar, A., Abdelkader, M., Alhabashi, Y., & Ghouti, L. (2023). AERO: AI-Enabled Remote Sensing Observation with Onboard Edge Computing in UAVs. Remote Sensing, 15(7), 1873. https://doi.org/10.3390/rs15071873
Lamar Peña, F. S., Vega Mite, G. A., Honores Tapia, J. A., & Cárdenas Villavicencio, O. E. (2024). Validación y emisión de certificados en Educación Superior utilizando tecnología Blockchain. Informática y Sistemas: Revista de Tecnologías de la Informática y las Comunicaciones, 8(1), 36. https://doi.org/10.33936/isrtic.v8i1.6535
Lambropoulos, G., Mitropoulos, S., Douligeris, C., & Maglaras, L. (2024). Implementing Virtualization on Single-Board Computers: A Case Study on Edge Computing. Computers, 13(2), 54. https://doi.org/10.3390/computers13020054
Liu, L., Qiao, X., Liang, W., Oboamah, J., Wang, J., Rudnick, D. R., Yang, H., Katimbo, A., & Shi, Y. (2023). An Edge-computing flow meter reading recognition algorithm optimized for agricultural IoT network. Smart Agricultural Technology, 5, 100236. https://doi.org/10.1016/j.atech.2023.100236
Loukatos, D., Lygkoura, K.-A., Maraveas, C., & Arvanitis, K. G. (2022). Enriching IoT Modules with Edge AI Functionality to Detect Water Misuse Events in a Decentralized Manner. Sensors, 22(13), 4874. https://doi.org/10.3390/s22134874
Najeh, H., Lohr, C., & Leduc, B. (2024). Real-Time Human Activity Recognition on Embedded Equipment: A Comparative Study. Applied Sciences, 14(6), 2377. https://doi.org/10.3390/app14062377
Nguyen, H. H., Shin, D.-Y., Jung, W.-S., Kim, T.-Y., & Lee, D.-H. (2024). An Integrated IoT Sensor-Camera System toward Leveraging Edge Computing for Smart Greenhouse Mushroom Cultivation. Agriculture, 14(3), 489. https://doi.org/10.3390/agriculture14030489
Oluwole Temidayo Modupe, Aanuoluwapo Ayodeji Otitoola, Oluwatayo Jacob Oladapo, Oluwatosin Oluwatimileyin Abiona, Oyekunle Claudius Oyeniran, Adebunmi Okechukwu Adewusi, Abiola Moshood Komolafe, & Amaka Obijuru. (2024). REVIEWING THE TRANSFORMATIONAL IMPACT OF EDGE COMPUTING ON REAL-TIME DATA PROCESSING AND ANALYTICS. Computer Science & IT Research Journal, 5(3), 693–702. https://doi.org/10.51594/csitrj.v5i3.929
Patrikar, D. R., & Parate, M. R. (2022). Anomaly detection using edge computing in video surveillance system: Review. International Journal of Multimedia Information Retrieval, 11(2), 85–110. https://doi.org/10.1007/s13735-022-00227-8
Puig, F., Rodríguez Díaz, J. A., & Soriano, M. A. (2022). Development of a Low-Cost Open-Source Platform for Smart Irrigation Systems. Agronomy, 12(12), 2909. https://doi.org/10.3390/agronomy12122909
Qi, C., Chang, J., Zhang, J., Zuo, Y., Ben, Z., & Chen, K. (2022). Medicinal Chrysanthemum Detection under Complex Environments Using the MC-LCNN Model. Plants, 11(7), 838. https://doi.org/10.3390/plants11070838
Rastegari, H., Nadi, F., Lam, S. S., Ikhwanuddin, M., Kasan, N. A., Rahmat, R. F., & Mahari, W. A. W. (2023). Internet of Things in aquaculture: A review of the challenges and potential solutions based on current and future trends. Smart Agricultural Technology, 4, 100187. https://doi.org/10.1016/j.atech.2023.100187
Ravindran, A. A. (2023). Internet-of-Things Edge Computing Systems for Streaming Video Analytics: Trails Behind and the Paths Ahead. IoT, 4(4), 486–513. https://doi.org/10.3390/iot4040021
Restrepo-Arias, J. F., Branch-Bedoya, J. W., & Awad, G. (2024). Image classification on smart agriculture platforms: Systematic literature review. Artificial Intelligence in Agriculture, 13, 1–17. https://doi.org/10.1016/j.aiia.2024.06.002
Reyana, A., Kautish, S., Alnowibet, K. A., Zawbaa, H. M., & Wagdy Mohamed, A. (2023). Opportunities of IoT in Fog Computing for High Fault Tolerance and Sustainable Energy Optimization. Sustainability, 15(11), 8702. https://doi.org/10.3390/su15118702
Rivadeneira, J. E., Borges, G. A., Rodrigues, A., Boavida, F., & Sá Silva, J. (2024). A unified privacy preserving model with AI at the edge for Human-in-the-Loop Cyber-Physical Systems. Internet of Things, 25, 101034. https://doi.org/10.1016/j.iot.2023.101034
Rudrakar, S., & Rughani, P. (2023). IoT based Agriculture (Ag-IoT): A detailed study on Architecture, Security and Forensics. Information Processing in Agriculture, S2214317323000665. https://doi.org/10.1016/j.inpa.2023.09.002
Sanguino Reyes, M. R. (2020). A systematic review of the literature on information technology outsourcing services. Journal of Physics: Conference Series, 1513(1), 012007. https://doi.org/10.1088/1742-6596/1513/1/012007
Shukla, S., Thakur, S., Hussain, S., Breslin, J. G., & Jameel, S. M. (2021). Identification and Authentication in Healthcare Internet-of-Things Using Integrated Fog Computing Based Blockchain Model. Internet of Things, 15, 100422. https://doi.org/10.1016/j.iot.2021.100422
Singh, P., Elmi, Z., Krishna Meriga, V., Pasha, J., & Dulebenets, M. A. (2022). Internet of Things for sustainable railway transportation: Past, present, and future. Cleaner Logistics and Supply Chain, 4, 100065. https://doi.org/10.1016/j.clscn.2022.100065
Tripathy, S. S., Rath, M., Tripathy, N., Roy, D. S., Francis, J. S. A., & Bebortta, S. (2023). An Intelligent Health Care System in Fog Platform with Optimized Performance. Sustainability, 15(3), 1862. https://doi.org/10.3390/su15031862
Wu, Y., Dai, H.-N., & Wang, H. (2021). Convergence of Blockchain and Edge Computing for Secure and Scalable IIoT Critical Infrastructures in Industry 4.0. IEEE Internet of Things Journal, 8(4), 2300–2317. https://doi.org/10.1109/JIOT.2020.3025916
Xavier, R., Silva, R. S., Ribeiro, M., Moreira, W., Freitas, L., & Oliveira-Jr, A. (2024). Integrating Multi-Access Edge Computing (MEC) into Open 5G Core. Telecom, 5(2), 433–450. https://doi.org/10.3390/telecom5020022
Yang, S., Zhang, Z., Xia, H., Li, Y., & Liu, Z. (2023). Edge Intelligence-Assisted Asymmetrical Network Control and Video Decoding in the Industrial IoT with Speculative Parallelization. Symmetry, 15(8), 1516. https://doi.org/10.3390/sym15081516
Zhang, T., Li, Y., & Philip Chen, C. L. (2021). Edge computing and its role in Industrial Internet: Methodologies, applications, and future directions. Information Sciences, 557, 34–65. https://doi.org/10.1016/j.ins.2020.12.021
Zheng, W., Wang, X., Xie, Z., Li, Y., Ye, X., Wang, J., & Xiong, X. (2024). Data management method for building internet of things based on blockchain sharding and DAG. Internet of Things and Cyber-Physical Systems, 4, 217–234. https://doi.org/10.1016/j.iotcps.2024.01.001
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