Design of a high-availability hybrid-cloud architecture for regional monitoring in the ecuadorian coastal region: network and hardware analysis
DOI:
https://doi.org/10.70577/cieninter.v4i3.133Keywords:
Accelerographs, cloud computing, high availability, seismic monitoring, virtual private network.Abstract
Real-time seismic monitoring is fundamental to reducing disaster risk and optimizing early warning systems along vulnerable coastal areas exposed to high seismic activity. This article aimed to design, adapt, and field-validate the comprehensive hardware, telecommunications and cloud-server infrastructure of the Seismic Monitoring Center (CEMSIS) for Guayaquil and its remote sensing stations in Portoviejo and Manta. The study followed a quantitative applied-engineering approach structured into four operational phases: first, routing simulation using an extended star topology within Cisco Packet Tracer; second, electronic conditioning, calibration, and stabilization of REFTEK 130 and 130S accelerographs using regulated 15 V DC / 3 A power supplies and industrial-grade Amphenol connectors; third, deployment of multi-carrier redundant connectivity featuring automated failover over fiber optics and 4G LTE cellular links via AES-256 encrypted OpenVPN tunnels toward a centralized cloud instance; and fourth, continuous stream automation through custom self-healing watchdog scripts operating under systemd. Empirical validation demonstrated that this architecture achieved 99.9% operational uptime and sustained continuous data transmission without packet loss, reporting average transmission latency of 25 to 30 ms across fiber backbones and 45 to 50 ms over mobile networks, at a maintenance cost of approximately 9 USD per station per month. Consequently, this hybrid edge-to-cloud model successfully removes single points of failure, delivering a highly scalable, robust, and cost-effective technological baseline to reinforce urban resilience and critical emergency decision-making during high-magnitude events.
Downloads
References
Armbrust, M., Stoica, I., Zaharia, M., Fox, A., Griffith, R., Joseph, A. D., Katz, R., Konwinski, A., Lee, G., Patterson, D., Rabkin, A., & Stoica, I. (2010). A view of cloud computing. Communications of the ACM, 53(4), 50–58. https://doi.org/10.1145/1721654.1721672 DOI: https://doi.org/10.1145/1721654.1721672
Bindi, D., Zaccarelli, R., Strollo, A., Di Giacomo, D., Heinloo, A., Evans, P., Cotton, F., & Tilmann, F. (2024). Enriching the GEOFON seismic catalog with automatic energy magnitude estimations. Earth System Science Data, 16(4), 1733–1745. https://doi.org/10.5194/essd-16-1733-2024 DOI: https://doi.org/10.5194/essd-16-1733-2024
Biondi, E., Tepp, G., Yu, E., Saunders, J. K., Yartsev, V., Black, M., Watkins, M., Bhaskaran, A., Bhadha, R., Zhan, Z., & Husker, A. L. (2025). Real-time processing of distributed acoustic sensing data for earthquake monitoring operations. arXiv. https://doi.org/10.48550/arXiv.2505.24077 DOI: https://doi.org/10.1785/0220250208
Blaso, L., Clemente, P., Giovinazzi, S., Giuliani, G., & Gozo, N. (2021). Towards standardized and interoperable platforms for supporting the seismic vulnerability assessment and seismic monitoring of Italian bridges and viaducts. En Lecture Notes in Civil Engineering (pp. 471–480). Springer. https://doi.org/10.1007/978-3-030-91877-4_54 DOI: https://doi.org/10.1007/978-3-030-91877-4_54
Bravo-Haro, M. A., Ding, X., & Elghazouli, A. Y. (2021). MEMS-based low-cost and open-source accelerograph for earthquake strong-motion. Engineering Structures, 230, 111675. https://doi.org/10.1016/j.engstruct.2020.111675 DOI: https://doi.org/10.1016/j.engstruct.2020.111675
Chanakitkarnchok, A., Kawila, K., Sato, G., Owada, Y., & Charnsripinyo, C. (2019). Disaster-resilient communication framework for heterogeneous vehicular networks. En 2019 IEEE 30th Annual International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC) (pp. 1–6). IEEE. https://doi.org/10.1109/PIMRC.2019.8904211 DOI: https://doi.org/10.1109/PIMRC.2019.8904211
Dugan, R. C., McGranaghan, M. F., Santoso, S., & Beaty, H. W. (2012). Electrical power systems quality (3.ª ed.). McGraw-Hill.
Havskov, J., & Alguacil, G. (2016). Instrumentation in earthquake seismology (2.ª ed.). Springer. https://doi.org/10.1007/978-3-319-21314-9 DOI: https://doi.org/10.1007/978-3-319-21314-9
IEEE Standards Association. (2022). IEEE Std 802.3: Ethernet standards. IEEE.
Instituto Geofísico de la Escuela Politécnica Nacional. (2022). Manual de procedimientos para la instalación y mantenimiento de acelerógrafos en campo. Escuela Politécnica Nacional. https://www.igepn.edu.ec/acelerografos
Internet Engineering Task Force. (1996). RFC 1918: Address allocation for private internets. https://www.rfc-editor.org/rfc/rfc1918
Kamiloğlu, M. E. (2015). Cisco network performance evaluation using Packet Tracer. International Journal of Electronics, Mechanical and Mechatronics Engineering, 5(1), 905–911. https://doi.org/10.17932/IAU.IJEMME.m.21460604.2015.5/1.905-911 DOI: https://doi.org/10.17932/IAU.IJEMME.m.21460604.2015.5/1.905-911
Kotuliak, I., Rybár, P., & Trúchly, P. (2011). Performance comparison of IPsec and TLS based VPN technologies. En 2011 9th International Conference on Emerging eLearning Technologies and Applications (ICETA) (pp. 217–221). IEEE. https://doi.org/10.1109/ICETA.2011.6112567 DOI: https://doi.org/10.1109/ICETA.2011.6112567
Kurose, J. F., & Ross, K. W. (2017). Computer networking: A top-down approach (7.ª ed.). Pearson.
Li, W., Koehler, J., Chakraborty, M., Quinteros-Cartaya, C., Rümpker, G., & Srivastava, N. (2022). Real-time earthquake monitoring using deep learning: A case study on the Turkey earthquake aftershock sequence. arXiv. https://doi.org/10.48550/arXiv.2211.09539
Love, R. (2013). Linux system programming: Talking directly to the kernel and C library (2.ª ed.). O'Reilly Media.
Mackey, S., Mihov, I., Nosenko, A., Vega, F., & Cheng, Y. (2020). A performance comparison of WireGuard and OpenVPN. En Proceedings of the Tenth ACM Conference on Data and Application Security and Privacy (pp. 162–164). Association for Computing Machinery. https://doi.org/10.1145/3374664.3379532 DOI: https://doi.org/10.1145/3374664.3379532
Nesbitt, I., Boaz, R., & Long, J. (2021). rsudp: A Python package for real-time seismic monitoring with Raspberry Shake instruments. Journal of Open Source Software, 6(68), 2565. https://doi.org/10.21105/joss.02565 DOI: https://doi.org/10.21105/joss.02565
Ni, Y., Denolle, M. A., Münchmeyer, J., Wang, Y., Feng, K.-F., Garcia Jurado Suarez, C., Thomas, A. M., Trabant, C., Hamilton, A., & Mencin, D. (2025). A review of cloud computing and storage in seismology. Geophysical Journal International, 243(1), ggaf322. https://doi.org/10.1093/gji/ggaf322 DOI: https://doi.org/10.1093/gji/ggaf322
Nocquet, J.-M., Jarrín, P., Vallée, M., Mothes, P. A., Grandin, R., Rolandone, F., Delouis, B., Yepes, H., Font, Y., Fuentes, D., Régnier, M., Laurendeau, A., Cisneros, D., Hernández, S., Sladen, A., Singaucho, J.-C., Mora, H., Gómez, J., Montes, L., & Charvis, P. (2017). Supercycle at the Ecuadorian subduction zone revealed after the 2016 Pedernales earthquake. Nature Geoscience, 10(2), 145–149. https://doi.org/10.1038/ngeo2864 DOI: https://doi.org/10.1038/ngeo2864
Qadir, J. (2015). Artificial intelligence based cognitive routing for cognitive radio networks. Artificial Intelligence Review, 45(1), 25–96. https://doi.org/10.1007/s10462-015-9438-6 DOI: https://doi.org/10.1007/s10462-015-9438-6
REFTEK Systems. (2023). Manual de usuario de REFTEK 130S y 130S-DC: Configuración y manejo de datos sísmicos. REFTEK Systems Inc. https://manuals.reftek.com/category/130S/
Silberschatz, A., Galvin, P. B., & Gagne, G. (2018). Operating system concepts (10.ª ed.). Wiley.
Stallings, W. (2021). Network security essentials: Applications and standards (6.ª ed.). Pearson.
Tanenbaum, A. S., Feamster, N., & Wetherall, D. J. (2021). Computer networks (6.ª ed.). Pearson.
Wang, X., Liu, F., Su, R., Wang, Z., Fang, L., Zhou, L., Bai, L., & Ouyang, W. (2025). SeisMoLLM: Advancing seismic monitoring via cross-modal transfer with a pre-trained large language model. arXiv. https://doi.org/10.48550/arXiv.2502.19960
Ye, L., Kanamori, H., Avouac, J.-P., Li, L., Cheung, K. F., & Lay, T. (2016). The 16 April 2016, Mw 7.8 Ecuador earthquake: A quasi-repeat of the 1942 Ms 7.5 earthquake and partial re-rupture of the 1906 Ms 8.6 Colombia–Ecuador earthquake. Earth and Planetary Science Letters, 454, 248–258. https://doi.org/10.1016/j.epsl.2016.09.006 DOI: https://doi.org/10.1016/j.epsl.2016.09.006
Zhu, W., Hou, A. B., Yang, R., Datta, A., Mousavi, S. M., Ellsworth, W. L., & Beroza, G. C. (2022). QuakeFlow: A scalable machine-learning-based earthquake monitoring workflow with cloud computing. arXiv. https://doi.org/10.48550/arXiv.2208.14564 DOI: https://doi.org/10.1093/gji/ggac355
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2026 Walter Vicente Mera Ortiz, Galo Enrique Cornejo Goméz, José Miguel Erazo Ayón, Guillermo Alfonso Ponce Vásquez, Madelaine Alexandra Viera Gurumendi
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
