A Cloud-IoT Architecture for Latency-Aware Localization in Earthquake Early Warning

An effective earthquake early warning system requires rapid and reliable earthquake source detection. Despite the numerous proposed epicenter localization solutions in recent years, their utilization within the Internet of Things (IoT) framework and integration with IoT-oriented cloud platforms remain underexplored. This paper proposes a complete IoT architecture for earthquake detection, localization, and event notification. The architecture, which has been designed, deployed, and tested on a standard cloud platform, introduces an innovative approach by implementing P-wave "picking" directly on IoT devices, deviating from traditional regional earthquake early warning (EEW) approaches. Pick association, source localization, event declaration, and user notification functionalities are also deployed on the cloud. The cloud integration simplifies the integration of other services in the architecture, such as data storage and device management. Moreover, a localization algorithm based on the hyperbola method is proposed, but here, the time difference of arrival multilateration is applied that is often used in wireless sensor network applications. The results show that the proposed end-to-end architecture is able to provide a quick estimate of the earthquake epicenter location with acceptable errors for an EEW system scenario. Rigorous testing against the standard of reference in Italy for regional EEW showed an overall 3.39 s gain in the system localization speed, thus offering a tangible metric of the efficiency and potential proposed system as an EEW solution.

Satriano, C.; Wu, Y.M.; Zollo, A.; Kanamori, H. Earthquake early warning: Concepts, methods and physical grounds. Soil Dyn. Earthq. Eng. 2011, 31, 106–118. [Google Scholar] [CrossRef]
Sokolov, V.; Furumura, T.; Wenzel, F. On the use of JMA intensity in earthquake early warning systems. Bull. Earthq. Eng. 2010, 8, 767–786. [Google Scholar] [CrossRef]
Satriano, C.; Lomax, A.; Zollo, A. Real-time evolutionary earthquake location for seismic early warning. Bull. Seismol. Soc. Am. 2008, 98, 1482–1494. [Google Scholar] [CrossRef]
Zanoli, S.M.; Pepe, C. Thermal, Lighting and IAQ Control System for Energy Saving and Comfort Management. Processes 2023, 11, 222. [Google Scholar] [CrossRef]
Zanoli, S.; Pepe, C.; Orlietti, L.; Barchiesi, D. A Model Predictive Control strategy for energy saving and user comfort features in building automation. In Proceedings of the 2015 19th International Conference on System Theory, Control and Computing (ICSTCC), Cheile Gradistei, Romania, 14–16 October 2015; IEEE: Piscataway, NJ, USA, 2015; pp. 472–477. [Google Scholar]
Esposito, M.; Belli, A.; Palma, L.; Pierleoni, P. Design and Implementation of a Framework for Smart Home Automation Based on Cellular IoT, MQTT, and Serverless Functions. Sensors 2023, 23, 4459. [Google Scholar] [CrossRef]
Pierleoni, P.; Conti, M.; Belli, A.; Palma, L.; Incipini, L.; Sabbatini, L.; Valenti, S.; Mercuri, M.; Concetti, R. IoT Solution based on MQTT Protocol for Real-Time Building Monitoring. In Proceedings of the 2019 IEEE 23rd International Symposium on Consumer Technologies (ISCT), Ancona, Italy, 19–21 June 2019; pp. 57–62. [Google Scholar] [CrossRef]
Zanoli, S.M.; Pepe, C.; Astolfi, G.; Orlietti, L. Applications of Advanced Process Control Techniques to an Italian Water Distribution Network. IEEE Trans. Control. Netw. Syst. 2022, 9, 1767–1779. [Google Scholar] [CrossRef]
Zanoli, S.; Astolfi, G.; Orlietti, L.; Frisinghelli, M.; Pepe, C. Water Distribution Networks Optimization: A real case study. IFAC-PapersOnLine 2020, 53, 16644–16650. [Google Scholar] [CrossRef]
Zanoli, S.M.; Pepe, C.; Astolfi, G.; Luzi, F. Reservoir Advanced Process Control for Hydroelectric Power Production. Processes 2023, 11, 300. [Google Scholar] [CrossRef]
Esposito, M.; Palma, L.; Belli, A.; Sabbatini, L.; Pierleoni, P. Recent Advances in Internet of Things Solutions for Early Warning Systems: A Review. Sensors 2022, 22, 62124. [Google Scholar] [CrossRef]
Ray, P.P.; Mukherjee, M.; Shu, L. Internet of things for disaster management: State-of-the-art and prospects. IEEE Access 2017, 5, 18818–18835. [Google Scholar] [CrossRef]
Satriano, C.; Elia, L.; Martino, C.; Lancieri, M.; Zollo, A.; Iannaccone, G. PRESTo, the earthquake early warning system for southern Italy: Concepts, capabilities and future perspectives. Soil Dyn. Earthq. Eng. 2011, 31, 137–153. [Google Scholar] [CrossRef]
Obara, K.; Takanami, T.; Kitagawa, G. Hi-Net: High Sensitivity Seismograph Network; Springer: Tokyo, Japan, 2008; pp. 79–88. [Google Scholar] [CrossRef]
Luca Elia and RISSC-Lab, Physics Department, University “Federico II” of Naples. PRESTo v1. Software. Available online: http://www.prestoews.org/documentation.php (accessed on 30 September 2022).
Picozzi, M.; Zollo, A.; Brondi, P.; Colombelli, S.; Elia, L.; Martino, C. Exploring the feasibility of a nationwide earthquake early warning system in Italy. J. Geophys. Res. Solid Earth 2015, 120, 2446–2465. [Google Scholar] [CrossRef]
Ladina, C.; Marzorati, S.; Amato, A.; Cattaneo, M. Feasibility Study of an Earthquake Early Warning System in Eastern Central Italy. Front. Earth Sci. 2021, 9, 685751. [Google Scholar] [CrossRef]
Zuccolo, E.; Cremen, G.; Galasso, C. Comparing the performance of regional earthquake early warning algorithms in Europe. Front. Earth Sci. 2021, 9, 686272. [Google Scholar] [CrossRef]
Pierleoni, P.; Concetti, R.; Marzorati, S.; Belli, A.; Palma, L. Internet of Things for Earthquake Early Warning Systems: A Performance Comparison between Communication Protocols. IEEE Access 2023, 11, 43183–43194. [Google Scholar] [CrossRef]
Kohler, M.D.; Smith, D.E.; Andrews, J.; Chung, A.I.; Hartog, R.; Henson, I.; Given, D.D.; de Groot, R.; Guiwits, S. Earthquake early warning ShakeAlert 2.0: Public rollout. Seismol. Res. Lett. 2020, 91, 1763–1775. [Google Scholar] [CrossRef]
Cua, G.; Heaton, T. The Virtual Seismologist (VS) method: A Bayesian approach to earthquake early warning. In Earthquake Early Warning Systems; Springer: Berlin/Heidelberg, Germany, 2007; pp. 97–132. [Google Scholar]
Trnkoczy, A. Understanding and parameter setting of STA/LTA trigger algorithm. In New Manual of Seismological Observatory Practice (NMSOP); Deutsches GeoForschungsZentrum GFZ: Berlin, Germany, 2009; pp. 1–20. [Google Scholar]
Heinloo, A.; Trabant, C. SeisComP 2.1 Manual; GeoForschungsZentrum: Potsdam, Germany, 2004. [Google Scholar]
Allen, R.M.; Kong, Q.; Martin-Short, R. The MyShake Platform: A Global Vision for Earthquake Early Warning. Pure Appl. Geophys. 2020, 177, 1699–1712. [Google Scholar] [CrossRef]
Panizzi, E. The SeismoCloud App: Your Smartphone as a Seismometer. In Proceedings of the International Working Conference on Advanced Visual Interfaces, Bari, Italy, 7–10 June 2016; pp. 336–337. [Google Scholar]
Klapez, M.; Grazia, C.A.; Zennaro, S.; Cozzani, M.; Casoni, M. First experiences with earthcloud, a low-cost, cloud-based iot seismic alert system. In Proceedings of the 2018 14th International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob), Limassol, Cyprus, 15–17 October 2018; IEEE: Piscataway, NJ, USA, 2018; pp. 262–269. [Google Scholar]
Geiger, L. Probability method for the determination of earthquake epicenters from the arrival time only. Bull. St. Louis Univ. 1912, 8, 56–71. [Google Scholar]
Lomax, A.; Virieux, J.; Volant, P.; Berge-Thierry, C. Probabilistic earthquake location in 3D and layered models. In Advances in Seismic Event Location; Springer: Berlin/Heidelberg, Germany, 2000; pp. 101–134. [Google Scholar]
Havskov, J.; Bormann, P.; Schweitzer, J. Seismic source location. In New Manual of Seismological Observatory Practice 2 (NMSOP-2); Deutsches GeoForschungsZentrum GFZ: Berling, Germany, 2012; pp. 1–139. [Google Scholar]
Rydelek, P.; Pujol, J. Real-time seismic warning with a two-station subarray. Bull. Seismol. Soc. Am. 2004, 94, 1546–1550. [Google Scholar] [CrossRef]
Ochoa, L.H.; Niño, L.F.; Vargas, C.A. Fast estimation of earthquake epicenter distance using a single seismological station with machine learning techniques. Dyna 2018, 85, 161–168. [Google Scholar] [CrossRef]
Mousavi, S.M.; Beroza, G.C. Bayesian-Deep-Learning Estimation of Earthquake Location from Single-Station Observations. IEEE Trans. Geosci. Remote Sens. 2020, 58, 8211–8224. [Google Scholar] [CrossRef]
Perol, T.; Gharbi, M.; Denolle, M. Convolutional neural network for earthquake detection and location. Sci. Adv. 2018, 4, e1700578. [Google Scholar] [CrossRef] [PubMed]
Chin, T.L.; Chen, K.Y.; Chen, D.Y.; Wang, T.H. An Attention-Based Hypocenter Estimator for Earthquake Localization. IEEE Trans. Geosci. Remote Sens. 2022, 60, 5905510. [Google Scholar] [CrossRef]
Saad, O.M.; Chen, Y.; Trugman, D.; Soliman, M.S.; Samy, L.; Savvaidis, A.; Khamis, M.A.; Hafez, A.G.; Fomel, S.; Chen, Y. Machine Learning for Fast and Reliable Source-Location Estimation in Earthquake Early Warning. IEEE Geosci. Remote Sens. Lett. 2022, 19, 8025705. [Google Scholar] [CrossRef]
Wu, L.; Fan, J.; Zou, Y. An Accurate Earthquake Localization Algorithm Using the TDOA Measurements between P and S Waves. In Proceedings of the 2022 14th International Conference on Wireless Communications and Signal Processing (WCSP), Nanjing, China, 1–3 November 2022; IEEE: Piscataway, NJ, USA, 2022; pp. 501–504. [Google Scholar]
Ahn, H.; Kim, H.; Choi, A.; You, K. Hybrid TDOA/AOA Hypocenter Localization Using the Constrained Least Squares Method with Deep Learning P-Onset Picking. Processes 2022, 10, 2505. [Google Scholar] [CrossRef]
Jin, B.; Xu, X.; Zhang, T. Robust time-difference-of-arrival (TDOA) localization using weighted least squares with cone tangent plane constraint. Sensors 2018, 18, 778. [Google Scholar] [CrossRef] [PubMed]
Kong, Q.; Allen, R.M.; Schreier, L.; Kwon, Y.W. MyShake: A smartphone seismic network for earthquake early warning and beyond. Sci. Adv. 2016, 2, e1501055. [Google Scholar] [CrossRef] [PubMed]
IRIS: SeedLink. Available online: http://ds.iris.edu/ds/nodes/dmc/services/seedlink/ (accessed on 30 March 2020).
Pierleoni, P.; Marzorati, S.; Ladina, C.; Raggiunto, S.; Belli, A.; Palma, L.; Cattaneo, M.; Valenti, S. Performance evaluation of a low-cost sensing unit for seismic applications: Field testing during seismic events of 2016-2017 in Central Italy. IEEE Sens. J. 2018, 18, 6644–6659. [Google Scholar] [CrossRef]
Franchi, F.; Marotta, A.; Rinaldi, C.; Graziosi, F.; Fratocchi, L.; Parisse, M. What Can 5G Do for Public Safety? Structural Health Monitoring and Earthquake Early Warning Scenarios. Sensors 2022, 22, 3020. [Google Scholar] [CrossRef] [PubMed]
Pierleoni, P.; Belli, A.; Esposito, M.; Concetti, R.; Palma, L. Earthquake Early Warning Services Based on Very Low-Cost Internet of Things Devices. In Proceedings of the 2022 61st FITCE International Congress Future Telecommunications: Infrastructure and Sustainability (FITCE), Rome, Italy, 29–30 September 2022; pp. 1–5. [Google Scholar] [CrossRef]
Lomax, A.; Satriano, C.; Vassallo, M. Automatic picker developments and optimization: FilterPicker—A robust, broadband picker for real-time seismic monitoring and earthquake early warning. Seismol. Res. Lett. 2012, 83, 531–540. [Google Scholar] [CrossRef]
IRIS: SL Archive, 5.5.8 Seiscomp Release. Software. Available online: https://www.seiscomp.de/doc/apps/slarchive.html (accessed on 30 September 2022).
Festa, G.; Picozzi, M.; Caruso, A.; Colombelli, S.; Cattaneo, M.; Chiaraluce, L.; Elia, L.; Martino, C.; Marzorati, S.; Supino, M.; et al. Performance of earthquake early warning systems during the 2016–2017 MW 5–6.5 central Italy sequence. Seismol. Res. Lett. 2018, 89, 1–12. [Google Scholar] [CrossRef]
Latorre, D.; Di Stefano, R.; Castello, B.; Michele, M.; Chiaraluce, L. An updated view of the Italian seismicity from probabilistic location in 3D velocity models: The 1981–2018 Italian catalog of absolute earthquake locations (CLASS). Tectonophysics 2023, 846, 229664. [Google Scholar] [CrossRef]
Pujol, J. Earthquake location tutorial: Graphical approach and approximate epicentral location techniques. Seismol. Res. Lett. 2004, 75, 63–74. [Google Scholar] [CrossRef]
Behr, Y.; Clinton, J.; Kästli, P.; Cauzzi, C.; Racine, R.; Meier, M.A. Anatomy of an earthquake early warning (EEW) alert: Predicting time delays for an end-to-end EEW system. Seismol. Res. Lett. 2015, 86, 830–840. [Google Scholar] [CrossRef]
Massa, M.; Lovati, S.; Franceschina, G.; D’Alema, E.; Marzorati, S.; Mazza, S.; Cattaneo, M.; Selvaggi, G.; Amato, A.; Michelini, A.; et al. ISMD, a web portal for real-time processing and dissemination of INGV strong-motion data. Seismol. Res. Lett. 2014, 85, 863–877. [Google Scholar] [CrossRef]
Beyreuther, M.; Barsch, R.; Krischer, L.; Megies, T.; Behr, Y.; Wassermann, J. ObsPy: A Python toolbox for seismology. Seismol. Res. Lett. 2010, 81, 530–533. [Google Scholar] [CrossRef]
Helffrich, G.; Wookey, J.; Bastow, I. The Seismic Analysis Code: A Primer and User’s Guide; Cambridge University Press: Cambridge, UK, 2013. [Google Scholar]
Anthony Lomax—ALomax Scientific, Mouans-Sartoux, France. NonLinLoc: Probabilistic, Non-Linear, Global-Search Earthquake Location in 3D Media. Supported in part by IRSN (Institut de Radioprotection et de Sureté Nucléaire), France; European project TomoVes; ETH Zurch; INGV Rome. Software. Available online: https://github.com/alomax/NonLinLoc (accessed on 30 September 2022).