Abstract
In this work we propose a Disaster Management System on 5G ultra Reliable Low Latency Networks that targets unprecedented reliability levels as well as low latency. In fact, referring to the 5G vision a Structural Health Monitoring system can be considered depending on the operational scenario: in the case of data collection and processing from sensors in monitored buildings, considering the high number of sensors installed, it can refer to the massive Machine Type Communications context. Vice versa, during a seismic event or just after it, the use case requires high reliability connectivity and, sometimes, low latency. Those features refer to the ultra Reliable Low Latency context. It seems interesting to evaluate and experiment the ability of 5G network to dynamically adapt to the changing scenario that this use case can provide. Moreover this work presents an innovative 5G architecture for Earthquake Early Warning that uses Structural Health Monitoring systems to detect a seismic event and to propagate a message reporting the event detection to all the buildings that may be damaged by the event.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
Topic of great importance for the municipality and citizens due to the aftermath of the 2009 L’Aquila earthquake.
References
Alliance, N.: 5G white paper. In: Next Generation Mobile Networks, White Paper, vol. 1 (2015)
3GPP TS 38.913: Technical specification group radio access network; study on scenarios and requirements for next generation access technologies. Release 15, June 2018
Li, S., Da Xu, L., Zhao, S.: 5G internet of things: a survey. J. Ind. Inf. Integr. 10, 1–9 (2018)
Antonelli, C., Cassioli, D., Franchi, F., Graziosi, F., Marotta, A., Pratesi, M., Rinaldi, C., Santucci, F.: The city of L’aquila as a living lab: the incipict project and the 5G trial. In: 2018 IEEE 5G World Forum (5GWF), pp. 410–415, July 2018
Krishnamurthy, V., Fowler, K., Sazonov, E.: The effect of time synchronization of wireless sensors on the modal analysis of structures. Smart Mater. Struct. 17(5), 055018 (2008)
Lynch, J.P., Loh, K.J.: A summary review of wireless sensors and sensor networks for structural health monitoring. Shock Vibr. Digest 38(2), 91–130 (2006)
Tajima, F., Hayashida, T.: Earthquake early warning: what does “seconds before a strong hit” mean? Progr. Earth Planetary Sci. 5(1), 63 (2018)
3GPP TS 38.201: Technical specification group radio access network; NR; physical layer; general description. Release 15, December 2017
Sachs, J., Wikstrom, G., Dudda, T., Baldemair, R., Kittichokechai, K.: 5G radio network design for ultra-reliable low-latency communication. IEEE Netw. 32(2), 24–31 (2018)
Kaippallimalil, J., Lee, Y., Saboorian, T., Shalash, M., Kozat, U.: Traffic engineered transport for 5G networks. In: 2019 IEEE Conference on Standards for Communications and Networking (CSCN), pp. 1–6 (2019)
D’Errico, L., Franchi, F., Graziosi, F., Marotta, A., Rinaldi, C., Boschi, M., Colarieti, A.: Structural health monitoring and earthquake early warning on 5G uRLLC network. In: 2019 IEEE 5th World Forum on Internet of Things (WF-IoT), pp. 783–786. IEEE (2019)
Potenza, F., Federici, F., Lepidi, M., Gattulli, V., Graziosi, F., Colarieti, A.: Long-term structural monitoring of the damaged basilica S. Maria di Collemaggio through a low-cost wireless sensor network. J. Civ. Struct. Health Monit. 5(5), 655–676 (2015)
Ha, D., Park, H., Choi, S., Kim, Y.: A wireless MEMS-based inclinometer sensor node for structural health monitoring. Sensors 13(12), 16 090–16 104 (2013)
Lorenzoni, F., Caldon, M., da Porto, F., Modena, C., Aoki, T.: Post-earthquake controls and damage detection through structural health monitoring: applications in L’aquila. J. Civ. Struct. Health Monit. 8(2), 217–236 (2018)
Behl, M., Smarra, F., Mangharam, R.: Dr-advisor: a data-driven demand response recommender system. Appl. Energy 170, 30–46 (2016)
Jain, A., Smarra, F. Mangharam, R.: Data predictive control using regression trees and ensemble learning. In: 2017 IEEE 56th Annual Conference on Decision and Control (CDC), pp. 4446–4451. IEEE (2017)
Jain, A., Smarra, F., Behl, M., Mangharam, R.: Data-driven model predictive control with regression trees–an application to building energy management. ACM Trans. Cyber-Phys. Syst. 2(1), 1–21 (2018)
Smarra, F., Jain, A., de Rubeis, T., Ambrosini, D., D’Innocenzo, A., Mangharam, R.: Data-driven model predictive control using random forests for building energy optimization and climate control. Appl. Energy 226, 1252–1272 (2018)
Smarra, F., Di Girolamo, G.D., Gattulli, V., Graziosi, F., D’Innocenzo, A.: Learning models for seismic-induced vibrations optimal control in structures via random forests. J. Optim. Theory Appl. 1–20 (2020)
Smarra, F., Di Girolamo, G.D., De Iuliis, V., Jain, A., Mangharam, R., D’Innocenzo, A.: Data-driven switching modeling for MPC using regression trees and random forests. Nonlinear Anal: Hybrid Syst. 36, 100882 (2020)
Di Girolamo, G., Smarra, F., Gattulli, V., Potenza, F., Graziosi, F., D’Innocenzo, A.: Data-driven optimal predictive control of seismic induced vibrations in frame structures. Struct. Control Health Monit. 27(4), e2514 (2020)
Acknowledgment
This work was partially supported by the Italian Government under CIPE resolution no. 135 (December 21, 2012), project INnovating City Planning through Information and Communication Technologies (INCIPICT). SHM-Board v2 has been developed thanks to a close collaboration between University of L’Aquila and WEST Aquila S.r.l. (University spin-off).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
Franchi, F., Graziosi, F., Marotta, A., Rinaldi, C. (2021). Structural Health Monitoring over 5G uRLLC Network. In: Rizzo, P., Milazzo, A. (eds) European Workshop on Structural Health Monitoring. EWSHM 2020. Lecture Notes in Civil Engineering, vol 127. Springer, Cham. https://doi.org/10.1007/978-3-030-64594-6_7
Download citation
DOI: https://doi.org/10.1007/978-3-030-64594-6_7
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-64593-9
Online ISBN: 978-3-030-64594-6
eBook Packages: EngineeringEngineering (R0)