Enhancing Wireless Sensor Network in Structural Health Monitoring through TCP/IP Socket Programming-Based Mimic Broadcasting: Experimental Validation
Source abstract
Abstract
This paper presents the implementation of a synchronous Structural Health Monitoring (SHM) framework utilizing wireless, low-cost, and off-the-shelf components. Vibration-based condition monitoring plays a crucial role in assessing the reliability of structural systems by detecting damage through changes in vibration parameters. The adoption of low-cost Micro-Electro-Mechanical Systems (MEMS) sensors in Wireless Sensor Networks (WSNs) has gained traction, emphasizing the need for precise time synchronization to schedule wake-up times of multiple sensor nodes for data collection. To address this challenge, our proposed method introduces a TCP/IP socket programming-based mimic broadcasting mechanism and a scalable sensing network controlled by a central gateway, leveraging the Raspberry Pi Python platform. The system operates using Internet of Things (IoT) concepts and adopts a star topology, where a packet is transmitted from the gateway to initiate measurements simultaneously on multiple sensor nodes. The sensor node comprises a MEMS accelerometer, a real time clock DS3231 module and Raspberry Pi Zero 2W (RPi0-2W), while the gateway employs a Raspberry Pi 4 (RPi4). To ensure accurate time synchronization, all Pi0-2W nodes were configured as Network Time Protocol (NTP) clients, synchronizing with an RPi4 server using chrony, the reliable implementation of the NTP. Through experimental evaluations, the system demonstrates its effectiveness and reliability in achieving initial time synchronization. This study addresses the challenge of achieving precise time alignment between sensor nodes through the utilization of the Dynamic Time Wrapping (DTW) method for Frequency Domain Decomposition (FDD) applications. The contribution of this research significantly enhances the field by improving the accuracy and reliability of time-aligned measurements, with a specific focus on utilizing low-cost sensors. By developing a practical and cost-effective SHM framework, this work advances the accessibility and scalability of structural health monitoring solutions, facilitating more widespread adoption and implementation in various engineering applications
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Energy Efficient Wireless Sensor Networks
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Structural Engineering, Department of Civil and Infrastructure Engineering, School of Engineering and Technology, Asian Institute of Technology, Pathum Thani 12120, Thailand
Department of Information and Communications Technologies, School of Engineering and Technology, Asian Institute of Technology, Pathum Thani 12120, Thailand
National Electronics and Computer Technology Center, National Science and Technology Development Agency, Pathum Thani 12120, Thailand
Institute of Industrial Science, The University of Tokyo, Komaba 4-6-1, Meguro-ku, Tokyo 153-8505, Japan
Funding
National Electronics and Computer Technology Center (NECTEC) · National Science and Technology Development Agency (NSTDA)
References (76)
Show source-provided references
1. A review of vibration-based damage detection in civil structures: From traditional methods to Machine Learning and Deep Learning applications10.1016/j.ymssp.2020.107077 ↗
2. Tiboni, M., Remino, C., Bussola, R., and Amici, C. (2022). A review on vibration-based condition monitoring of rotating machinery. Appl. Sci., 12.10.3390/app12030972 ↗
3. Detection of soil-abutment interaction by monitoring bridge response using vehicle excitation10.1007/s13349-015-0114-y ↗
4. Fiber optic health monitoring and temperature behavior of bridge in cold region10.1002/stc.2020 ↗
5. Xiao, F., Chen, G.S., and Hulsey, J.L. (2017). Monitoring bridge dynamic responses using fiber Bragg grating tiltmeters. Sensors, 17.10.3390/s17102390 ↗
6. Optimal static strain sensor placement for truss bridges10.1177/1550147717707929 ↗
7. Signal processing techniques for vibration-based health monitoring of smart structures10.1007/s11831-014-9135-7 ↗
8. Signature extraction from the dynamic responses of a bridge subjected to a moving vehicle using complete ensemble empirical mode decomposition10.1177/1461348419872878 ↗
9. Empirical mode decomposition and its variants: A review with applications in structural health monitoring10.1088/1361-665X/aba539 ↗
10. Hassan, Q.F. (2018). Internet of Things A to Z: Technologies and Applications, John Wiley & Sons.10.1002/9781119456735 ↗
11. Internet of things for smart ports: Technologies and challenges10.1109/MIM.2018.8278808 ↗
12. Structural health monitoring framework based on Internet of Things: A survey10.1109/JIOT.2017.2664072 ↗
13. Haque, M.E., Asikuzzaman, M., Khan, I.U., Ra, I.H., Hossain, M.S., and Shah, S.B.H. (2020). Comparative study of IoT-based topology maintenance protocol in a wireless sensor network for structural health monitoring. Remote Sens., 12.10.3390/rs12152358 ↗
14. Villa, V., Naticchia, B., Bruno, G., Aliev, K., Piantanida, P., and Antonelli, D. (2021). IoT open-source architecture for the maintenance of building facilities. Appl. Sci., 11.10.3390/app11125374 ↗
15. Structural health monitoring of civil engineering structures by using the internet of things: A review10.1016/j.jobe.2021.103954 ↗
16. MEMS accelerometers for mechanical vibrations analysis: A comprehensive review with applications10.1007/s40430-018-1445-5 ↗
17. Wireless MEMS-based accelerometer sensor boards for structural vibration monitoring: A review10.1109/JSEN.2016.2630008 ↗
18. MEMS-based low-cost and open-source accelerograph for earthquake strong-motion10.1016/j.engstruct.2020.111675 ↗
19. MEMS technology and applications in geotechnical monitoring: A review10.1088/1361-6501/ac4f00 ↗
20. Suitability of MEMS accelerometers for condition monitoring: An experimental study10.3390/s8020784 ↗
21. Evaluation of low-cost MEMS accelerometers for SHM: Frequency and damping identification of civil structures10.1590/1679-78255308 ↗
22. Lee, J., Khan, I., Choi, S., and Kwon, Y.W. (2019). A smart iot device for detecting and responding to earthquakes. Electronics, 8.10.3390/electronics8121546 ↗
23. Ambient vibration testing of a pedestrian bridge using low-cost accelerometers for SHM applications10.3390/smartcities2010002 ↗
24. A validated robust and automatic procedure for vibration analysis of bridge structures using MEMS accelerometers10.1515/jag-2020-0010 ↗
25. Damage assessment of reinforced concrete beams using cost-effective MEMS accelerometers10.1016/j.istruc.2022.04.101 ↗
26. Caballero-Russi, D., Ortiz, A.R., Guzmán, A., and Canchila, C. (2022). Design and Validation of a Low-Cost Structural Health Monitoring System for Dynamic Characterization of Structures. Appl. Sci., 12.10.3390/app12062807 ↗
27. Design and Validation of an Accurate Low-Cost Data Acquisition System for Structural Health Monitoring of a Pedestrian Bridge10.32732/jcec.2022.11.3.113 ↗
28. Sarkar, S.K., Basavaraju, T.G., and Puttamadappa, C. (2007). Ad Hoc Mobile Wireless Networks: Principles, Protocols and Applications, Auerbach Publications.10.1201/9781420062229 ↗
29. Routing protocols for wireless sensor networks: What the literature says?10.1016/j.aej.2016.08.003 ↗
30. Cache-it: A distributed architecture for proactive edge caching in heterogeneous iot scenarios10.1016/j.adhoc.2024.103413 ↗
31. Time synchronization in vehicular ad-hoc networks: A survey on theory and practice
32. Review of protocol stack development of flying ad-hoc networks for disaster monitoring applications10.1007/s11831-022-09791-y ↗
33. De Angelis, A., Santoni, F., Carbone, P., Cecconi, M., Vecchietti, A., and Di Lorenzo, F. (2020). Development of an IoT Structural Monitoring System Applied to a Hypogeal Site. Sensors, 20.10.3390/s20236769 ↗
34. Design and implementation of a synchronized IoT-based structural health monitoring system10.1016/j.iot.2022.100639 ↗
35. Wireless and Low-Power System for Synchronous and Real-Time Structural-Damage Assessment10.1109/JSEN.2023.3270961 ↗
36. Precision time synchronization using IEEE 1588 for wireless sensor networks10.1109/CSE.2009.264 ↗
37. High-performance wireless piezoelectric sensor network for distributed structural health monitoring10.1155/2016/3846804 ↗
38. Jornet-Monteverde, J.A., Galiana-Merino, J.J., and Soler-Llorens, J.L. (2021). Design and implementation of a wireless sensor network for seismic monitoring of buildings. Sensors, 21.10.3390/s21113875 ↗
39. Meng, Q., and Zhu, S. (2020). Developing iot sensing system for construction-induced vibration monitoring and impact assessment. Sensors, 20.10.3390/s20216120 ↗
40. Wireless sensor networks for continuous structural health monitoring of historic masonry towers10.1080/15583058.2020.1719229 ↗
41. Komarizadehasl, S., Lozano, F., Lozano-Galant, J.A., Ramos, G., and Turmo, J. (2022). Low-cost wireless structural health monitoring of bridges. Sensors, 22.10.3390/s22155725 ↗
42. Clock synchronization in industrial Internet of Things and potential works in precision time protocol: Review, challenges and future directions
43. Komarizadehasl, S., Huguenet, P., Lozano, F., Lozano-Galant, J.A., and Turmo, J. (2022). Operational and analytical modal analysis of a bridge using low-cost wireless Arduino-based accelerometers. Sensors, 22.10.3390/s22249808 ↗
44. A Low-Cost Wireless Multinode Vibration Monitoring System for Civil Structures
45. Dinar, A.E., Merabet, B., and Ghouali, S. (2020). Applications of Internet of Things: Proceedings of ICCCIOT 2020, Springer.
46. Chowdhury, D.D. (2021). NextGen Network Synchronization, Springer.10.1007/978-3-030-71179-5 ↗
47. Dynamic programming algorithm optimization for spoken word recognition10.1109/TASSP.1978.1163055 ↗
48. Silva, D., Carvalho, L.I., Soares, J., and Sofia, R.C. (2021). A performance analysis of internet of things networking protocols: Evaluating MQTT, CoAP, OPC UA. Appl. Sci., 11.10.3390/app11114879 ↗
49. Basavaraju, N., Alexander, N., and Seitz, J. (November, January 31). Performance Evaluation of Advanced Message Queuing Protocol (AMQP): An Empirical Analysis of AMQP Online Message Brokers. Proceedings of the 2021 International Symposium on Networks, Computers and Communications (ISNCC), Dubai, United Arab Emirates.10.1109/ISNCC52172.2021.9615705 ↗
50. Ioana, A., and Korodi, A. (2021). DDS and OPC UA protocol coexistence solution in real-time and industry 4.0 context using non-ideal infrastructure. Sensors, 21.10.3390/s21227760 ↗
51. Wytrębowicz, J., Cabaj, K., and Krawiec, J. (2021). Messaging protocols for IoT systems—A pragmatic comparison. Sensors, 21.10.3390/s21206904 ↗
52. Ajayi, O., Bagula, A., Bode, J., and Damon, M. (2022, January 5–7). A Comparison of Publish-Subscribe and Client-Server Models for Streaming IoT Telemetry Data. Proceedings of the International Conference on Emerging Technologies for Developing Countries, Bloemfontein, South Africa.10.1007/978-3-031-35883-8_9 ↗
53. Yassein, M.B., Shatnawi, M.Q., Aljwarneh, S., and Al-Hatmi, R. (2017, January 8–10). Internet of Things: Survey and open issues of MQTT protocol. Proceedings of the 2017 International Conference on Engineering & MIS (ICEMIS), Monastir, Tunisia.10.1109/ICEMIS.2017.8273112 ↗
54. A survey on communication protocols and performance evaluations for Internet of Things10.1016/j.dcan.2022.03.013 ↗
55. Oliveira, G.M., Costa, D.C., Cavalcanti, R.J., Oliveira, J.P., Silva, D.R., Nogueira, M.B., and Rodrigues, M.C. (2018, January 16–18). Comparison between MQTT and WebSocket protocols for Iot applications using ESP8266. Proceedings of the 2018 Workshop on Metrology for Industry 4.0 and IoT, Brescia, Italy.10.1109/METROI4.2018.8428348 ↗
56. McMillan, G. (2024, February 08). Socket Programming HOWTO. Available online: https://docs.python.org/3/howto/sockets.html#socket-howto.
57. Analog Devices (2024, February 08). ADXL345: Small, Low Power, 3-Axis Accelerometer. Available online: https://www.analog.com/media/en/technical-documentation/data-sheets/adxl345.pdf.
58. Fu, Y., Hoang, T., Mechitov, K., Kim, J.R., Zhang, D., and Spencer, B.F. (2018). Sudden event monitoring of civil infrastructure using demand-based wireless smart sensors. Sensors, 18.10.3390/s18124480 ↗
59. Structural health monitoring of South America’s first 6-Story experimental light-frame timber-building by using a low-cost RaspberryShake seismic instrumentation10.1016/j.engstruct.2022.115278 ↗
60. Iwaniec, M., Holovatyy, A., Teslyuk, V., Lobur, M., Kolesnyk, K., and Mashevska, M. (2017, January 20–23). Development of vibration spectrum analyzer using the Raspberry Pi microcomputer and 3-axis digital MEMS accelerometer ADXL345. Proceedings of the 2017 XIIIth International Conference on Perspective Technologies and Methods in MEMS Design (MEMSTECH), Lviv, Ukraine.10.1109/MEMSTECH.2017.7937525 ↗
61. A multichannel low-power wide-area network with high-accuracy synchronization ability for machine vibration monitoring10.1109/JIOT.2019.2895158 ↗
62. Ghazali, M.H.M., and Rahiman, W. (2022). An investigation of the reliability of different types of sensors in the real-time vibration-based anomaly inspection in drone. Sensors, 22.10.3390/s22166015 ↗
63. Sensor4PRI: A sensor platform for the protection of railway infrastructures10.3390/s150304996 ↗
64. Development of a system for monitoring vibration accelerations based on the raspberry pi microcomputer and the adxl345 accelerometer
65. Chang, H.F., and Shokrolah Shirazi, M. (2021). Integration with 3D visualization and IoT-based sensors for real-time structural health monitoring. Sensors, 21.10.3390/s21216988 ↗
66. Prediction of displacement in Reinforced concrete based on artificial neural networks using sensors10.1016/j.measen.2023.100764 ↗
67. Advancing structural health monitoring: A vibration-based IoT approach for remote real-time systems10.1016/j.sna.2023.114863 ↗
68. The Chrony Project (2024, April 17). Chrony Documentation. Available online: https://chrony-project.org/documentation.html.
69. A methodology to handle spectral smearing in gearboxes using adaptive mode decomposition and dynamic time warping10.1109/TIM.2021.3056737 ↗
70. Optimal transport for domain adaptation10.1109/TPAMI.2016.2615921 ↗
71. Adaptive global time sequence averaging method using dynamic time warping10.1109/TSP.2019.2897958 ↗
72. Modal identification of output-only systems using frequency domain decomposition10.1088/0964-1726/10/3/303 ↗
73. Brincker, R., and Zhang, L. (2009, January 4–6). Frequency domain decomposition revisited. Proceedings of the 3rd International Operational Modal Analysis Conference-IOMAC, Portonovo, Italy.
74. (2024, April 16). Python Cryptography Package on PyPI. Available online: https://pypi.org/project/cryptography/.
75. (2024, April 16). Ssl—TLS/SSL Wrapper. Available online: https://docs.python.org/3/library/ssl.html.
76. Toward accurate dynamic time warping in linear time and space10.3233/IDA-2007-11508 ↗