CoChat
Sign Up
Cited by 124Structural Health Monitoring TechniquesArduino and IoT ApplicationsWireless Sensor Networks for Data Analysis

Structural Health Monitoring System With Narrowband IoT and MEMS Sensors

Open access
Di Nuzzo, Flavio, Brunelli, Davide, Polonelli, Tommaso, Benini, Luca·2021·IEEE Sensors Journal
DOI 10.1109/jsen.2021.3075093
Open this paper in CoChat — free accountOpen PDF

Abstract

Monitoring of civil infrastructures is critically needed to track aging, damages and ultimately to prevent severe failures which can endanger many lives. The ability to monitor in a continuous and fine-grained fashion the integrity of a wide variety of buildings, referred to as structural health monitoring, with low-cost, long-term and continuous measurements is essential from both an economic and a life-safety standpoint. To address these needs, we propose a low-cost wireless sensor node specifically designed to support modal analysis over extended periods of time with long-range connectivity at low power consumption. Our design uses very cost-effective MEMS accelerometers and exploits the Narrowband IoT protocol (NB-IoT) to establish long-distance connection with 4G infrastructure networks. Long-range wireless connectivity, cabling-free installation and multi-year lifetime are a unique combination of features, not available, to the best of our knowledge, in any commercial or research device. We discuss in detail the hardware architecture and power management of the node. Experimental tests demonstrate a lifetime of more than ten years with a 17000 mAh battery or completely energy-neutral operation with a small solar panel (60 mm × 120 mm). Further, we validate measurement accuracy and confirm the feasibility of modal analysis with the MEMS sensors: compared with a high-precision instrument based on a piezoelectric transducer, our sensor node achieves a maximum difference of 0.08% at a small fraction of the cost and power consumption.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1109/jsen.2021.3075093

Cites

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • In [4], Di Nuzzo et al. overcome the communication range issue using both LoRaWAN and NB-IoT protocols but support only a few kbps of transmission bandwidth.

    “Another SHM platform measuring vibration is proposed in [4], where Di Nuzzo et al. overcome the communication range issue using both LoRaWAN and NB-IoT protocols but supporting only a few kbps of transmission bandwidth.”

    From Aerosense: A Self-Sustainable and Long-Range Bluetooth Wireless Sensor Node for Aerodynamic and Aeroacoustic Monitoring on Wind Turbines (2023) · cites this paper as [4]

    The Related Work section (p. 3) says the system uses NB-IoT for its communication link, not both NB-IoT and LoRaWAN; it describes LoRaWAN as unsuitable for this application. The Narrowband IoT section (p. 3) gives rates of 20 kbps uplink and 200 kbps downlink, contrary to the claim that transmission is limited to only a few kbps. (LLM, conf=0.92)

    Checked by a reasoning model — may be wrong.

    Experimental — accuracy not yet measured

  • Previous wireless solutions installed on wind turbines mainly included vibration measurements on the wind turbine tower for SHM monitoring purposes rather than acquiring aerodynamic data directly on the blade(s).

    “However, previous designs mainly included vibration measurements [4], [17] on the wind turbine tower for SHM monitoring purposes rather than proposing a solution capable of acquiring aerodynamic data directly on the blade(s).”

    From Aerosense: A Self-Sustainable and Long-Range Bluetooth Wireless Sensor Node for Aerodynamic and Aeroacoustic Monitoring on Wind Turbines (2023) · cites this paper as [4]

    The Introduction (p. 2) describes a wireless, MEMS-based system for vibration-based SHM, supporting that part of the citation. It specifies building modal analysis, however, and the In-Field Validation (p. 8) uses a laboratory structure simulating building behavior. The paper does not support the claim that measurements were made on a wind-turbine tower or the comparison with aerodynamic measurements on blades. (LLM, conf=0.87)

    Experimental — accuracy not yet measured

  • It is crucial to design energy-efficient and reliable devices able to acquire, process and transmit data for several years and eventually for the full wind turbine lifetime, exploiting environmental sources such as small solar photovoltaic panels.

    “Thus, it is crucial to design energy-efficient and reliable devices able to acquire, process and transmit data for several years and, eventually, for the full wind turbine lifetime, exploiting environmental sources such as small solar photovoltaic panels [3], [4], [10].”

    From Aerosense: A Self-Sustainable and Long-Range Bluetooth Wireless Sensor Node for Aerodynamic and Aeroacoustic Monitoring on Wind Turbines (2023) · cites this paper as [4]

    Split into 2 atoms — 1 confirmed, 1 partially supported, 0 disputed, 0 misattributed, 0 unverifiable. Expand the claim to see per-atom verdicts.

    Experimental — accuracy not yet measured

  • Vibration analysis on wind turbine blades is an important parameter in assessing their structural health.

    “Indeed, vibration analysis on wind turbine blades is an important parameter in assessing their structural health [4].”

    From Aerosense: A Self-Sustainable and Long-Range Bluetooth Wireless Sensor Node for Aerodynamic and Aeroacoustic Monitoring on Wind Turbines (2023) · cites this paper as [4]

    Related Work (p. 2) says modal-vibration analysis can detect structural damage and calls vibration-based analysis essential to structural health monitoring. In-Field Validation (p. 8) tests a structure simulating building behavior, not wind turbine blades; our copy does not establish the claim for blades specifically. (LLM, conf=0.88)

    Experimental — accuracy not yet measured

  • Wireless connectivity is one of the most relevant sources of consumption for long-range coverage.

    “Previous works, like [4] and [11] show that wireless connectivity is one of the most relevant sources of consumption for long-range coverage.”

    From Aerosense: A Self-Sustainable and Long-Range Bluetooth Wireless Sensor Node for Aerodynamic and Aeroacoustic Monitoring on Wind Turbines (2023) · cites this paper as [4]

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Cites

No references could be resolved yet.

Research works printed but not linked: 31

  1. [1][1] B. Romano, F. Zullo, L. Fiorini, A. Marucci, and S. Ciab` o, 'Land transformation of italy due to half a century of urbanization,' Land use policy , vol. 67, pp. 387-400, 2017. -
  2. [2][2] G. M. Calvi, M. Moratti, G. J. O'Reilly, N. Scattarreggia, R. Monteiro, D. Malomo, P. M. Calvi, and R. Pinho, 'Once upon a time in italy: The tale of the morandi bridge,' Structural Engineering International , vol. 29, no. 2, pp. 198-217, 2019. -
  3. [3][3] C. A. Tokognon, B. Gao, G. Y. Tian, and Y. Yan, 'Structural health monitoring framework based on internet of things: A survey,' IEEE Internet of Things Journal , vol. 4, no. 3, pp. 619-635, 2017. -
  4. [4][4] F. Zonzini, A. Girolami, L. De Marchi, A. Marzani, and D. Brunelli, 'Cluster-based vibration analysis of structures with gsp,' IEEE Transactions on Industrial Electronics , vol. 68, no. 4, pp. 3465-3474, 2021. -
  5. [5][5] M. R. Dhage and S. Vemuru, 'Structural health monitoring of railway tracks using wsn,' in 2017 International Conference on Computing, Communication, Control and Automation (ICCUBEA) . IEEE, 2017, pp. 1-5. -
  6. [6][6] S. Valenti, M. Conti, P. Pierleoni, L. Zappelli, A. Belli, F. Gara, S. Carbonari, and M. Regni, 'A low cost wireless sensor node for building monitoring,' in 2018 IEEE Workshop on Environmental, Energy, and Structural Monitoring Systems (EESMS) . IEEE, 2018, pp. 1-6. -
  7. [7][7] D. Catenazzo, B. O'Flynn, and M. Walsh, 'On the use of wireless sensor networks in preventative maintenance for industry 4.0,' in 2018 12th International Conference on Sensing Technology (ICST) . IEEE, 2018, pp. 256-262. -
  8. [8][8] A. Burrello, A. Marchioni, D. Brunelli, S. Benatti, M. Mangia, and L. Benini, 'Embedded streaming principal components analysis for network load reduction in structural health monitoring,' IEEE Internet of Things Journal , vol. 8, no. 6, pp. 4433-4447, 2021. -
  9. [9][9] J. Haxhibeqiri, E. De Poorter, I. Moerman, and J. Hoebeke, 'A survey of lorawan for iot: From technology to application,' Sensors , vol. 18, no. 11, p. 3995, 2018. -
  10. [10][10] M. Ballerini, T. Polonelli, D. Brunelli, M. Magno, and L. Benini, 'Experimental evaluation on NB-IoT and lorawan for industrial and iot applications,' in 2019 IEEE 17th International Conference on Industrial Informatics (INDIN) , vol. 1. IEEE, 2019, pp. 1729-1732. -
  11. [11][11] M. Ballerini, T. Polonelli, D. Brunelli, M. Magno, and L. Benini, 'NBIoT vs. lorawan: An experimental evaluation for industrial applications,' IEEE Transactions on Industrial Informatics , 2020. -
  12. [12][12] A. Sabato, C. Niezrecki, and G. Fortino, 'Wireless mems-based accelerometer sensor boards for structural vibration monitoring: a review,' IEEE Sensors Journal , vol. 17, no. 2, pp. 226-235, 2016. -
  13. [13][13] H. Wang, P. Xiang, and L. Jiang, 'Strain transfer theory of industrialized optical fiber-based sensors in civil engineering: A review on measurement accuracy, design and calibration,' Sensors and Actuators A: Physical , vol. 285, pp. 414-426, 2019. -
  14. [14][14] E. G. Popkova, 'Preconditions of formation and development of industry 4.0 in the conditions of knowledge economy,' in Industry 4.0: Industrial Revolution of the 21st Century . Springer, 2019, pp. 65-72. -
  15. [15][15] A. Girolami, D. Brunelli, and L. Benini, 'Low-cost and distributed health monitoring system for critical buildings,' in 2017 IEEE Workshop on Environmental, Energy, and Structural Monitoring Systems (EESMS) , 2017, pp. 1-6. -
  16. [16][16] F. Naeim, 'Dynamics of structures-theory and applications to earthquake engineering,' Earthquake Spectra , vol. 23, no. 2, pp. 491-492, 2007. -
  17. [17][17] A. Burrello, D. Brunelli, M. Malavisi, and L. Benini, 'Enhancing structural health monitoring with vehicle identification and tracking,' in 2020 IEEE International Instrumentation and Measurement Technology Conference (I2MTC) . IEEE, 2020, pp. 1-6. -
  18. [19][19] Y. Liao, A. Kiremidjian, R. Rajagopal, and C.-H. Loh, 'Structural damage detection and localization with unknown post-damage feature distribution using sequential change-point detection method,' Journal of Aerospace Engineering Vol. 32, Issue 2 , 11 2018. -
  19. [20][20] K. Krishnan Nair and A. S. Kiremidjian, 'Time Series Based Structural Damage Detection Algorithm Using Gaussian Mixtures Modeling,' Journal of Dynamic Systems, Measurement, and Control , vol. 129, no. 3, pp. 285-293, 08 2006. -
  20. [21][21] F. Zonzini, M. M. Malatesta, D. Bogomolov, N. Testoni, A. Marzani, and L. De Marchi, 'Vibration-based shm with upscalable and low-cost sensor networks,' IEEE Transactions on Instrumentation and Measurement , vol. 69, no. 10, pp. 7990-7998, 2020. -
  21. [22][22] T. Polonelli, D. Brunelli, M. Guermandi, and L. Benini, 'An accurate low-cost crackmeter with lorawan communication and energy harvesting capability,' in 2018 IEEE 23rd International Conference on Emerging Technologies and Factory Automation (ETFA) , vol. 1, 2018, pp. 671676. -
  22. [23][23] H. Wang, L. Jiang, and P. Xiang, 'Improving the durability of the optical fiber sensor based on strain transfer analysis,' Optical Fiber Technology , vol. 42, pp. 97-104, 2018. -
  23. [24][24] P. Ragam and N. Devidas Sahebraoji, 'Application of mems-based accelerometer wireless sensor systems for monitoring of blast-induced ground vibration and structural health: a review,' IET Wireless Sensor Systems , vol. 9, no. 3, pp. 103-109, 2019. -
  24. [25][25] K. Mekki, E. Bajic, F. Chaxel, and F. Meyer, 'A comparative study of lpwan technologies for large-scale iot deployment,' ICT Express , vol. 5, pp. 1-7, 03 2019. -
  25. [26][26] L. Alonso, J. Barbar´ an, J. Chen, M. D´ ıaz, L. Llopis, and B. Rubio, 'Middleware and communication technologies for structural health monitoring of critical infrastructures: A survey,' Computer Standards & Interfaces , vol. 56, 09 2018. -
  26. [27][27] C. Arcadius Tokognon, B. Gao, G. Y. Tian, and Y. Yan, 'Structural health monitoring framework based on internet of things: A survey,' IEEE Internet of Things Journal , vol. 4, no. 3, pp. 619-635, 2017. -
  27. [28][28] Y. . E. Wang, X. Lin, A. Adhikary, A. Grovlen, Y. Sui, Y. Blankenship, J. Bergman, and H. S. Razaghi, 'A primer on 3gpp narrowband internet of things,' IEEE Communications Magazine , vol. 55, no. 3, pp. 117-123, 2017. -
  28. [29][29] A. Adhikary, X. Lin, and Y. . E. Wang, 'Performance evaluation of nb-iot coverage,' in 2016 IEEE 84th Vehicular Technology Conference (VTC-Fall) , 2016, pp. 1-5. -
  29. [30][30] B. Martinez, F. Adelantado, A. Bartoli, and X. Vilajosana, 'Exploring the performance boundaries of nb-iot,' IEEE Internet of Things Journal , vol. 6, no. 3, pp. 5702-5712, 2019. -
  30. [31][31] N. Garg and R. Garg, 'Energy harvesting in iot devices: A survey,' in 2017 International Conference on Intelligent Sustainable Systems (ICISS) , 2017, pp. 127-131. -
  31. [32][32] F. G. Baptista and J. V. Filho, 'Optimal frequency range selection for pzt transducers in impedance-based shm systems,' IEEE Sensors Journal , vol. 10, no. 8, pp. 1297-1303, 2010. <!-- image --> <!-- image --> Flavio Di Nuzzo received the bachelor's degree in electronic engineering and the master's degree in electronic engineering from the University of Bologna in 2016 and 2019, respectively. He has done a year as a research fellow at the University of Bologna studying the Narrowband-IoT technology applied to Civil Structural Health Monitoring. He currently works in Ducati Motor Holding as an Electronic Engineer in the Vehicle Testing department. Davide Brunelli (Senior Member, IEEE) received the M.S. (cum laude) and Ph.D. degrees in electrical engineering from the University of Bologna, Bologna, Italy, in 2002 and 2007, respectively. He is currently an associate professor at the University of Trento, Italy. His research interests include IoT and distributed lightweight unmanned aerial vehicles, the development of new techniques of energy scavenging for low-power embedded systems and energy-neutral wearable devices, Drones, UAVs and Machine Learning. He was leading industrial coop- 创 eration activities with Telecom Italia, ENI, and STMicroelectronics. He has published more than 200 papers in international journals or proceedings of international conferences. He is an ACM member. <!-- image --> Tommaso Polonelli (Student Member, IEEE) received a Ph.D. and a master's degree in electronics and telecommunications engineering from the University of Bologna, Bologna, Italy, in 2020 and 2017, respectively. He is currently a postdoctoral fellow in the center of Project Based Learning, ETH Zurich, Switzerland. His research interests lie in the area of wireless sensor networks, wearable devices, structural health monitoring, autonomous unmanned vehicles, power management techniques, and the design of batteries-operating devices and embedded video surveillance. He has collaborated with several universities and research centers, such as the University College Cork, Cork, Ireland, Imperial College London, London, U.K., and the ETH, Zurich, Switzerland. He has authored over 20 papers in international journals and conferences. <!-- image --> R Luca Benini (Fellow, IEEE) holds the chair of digital circuits and systems at ETHZ and is Full Professor at the Universit` a di Bologna. He received a PhD from Stanford University. He served as chief architect in STMicroelectronics France. Dr. Benini's research interests are in energy-efficient parallel computing systems, smart sensing micro-systems and machine learning hardware. He has published more than 1000 peer-reviewed papers and five books. He is a fellow of the ACM and a member of the Academia Europaea.

Cited by

Sorted by citations

Authors
Di Nuzzo, F., Brunelli, D., Polonelli, T., & Benini, L.
Affiliations
  • University of Trento (IT)
  • University of Bologna (IT)
ORCID
Venue
IEEE Sensors Journal
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Published
2021-07-15
Volume
21
Issue
14
Pages
16371-16380
ISSN
  • 1530-437X (ISSN-L)
  • 1558-1748
  • 2379-9153
DOI
10.1109/jsen.2021.3075093
OpenAlex
W3159163441
Cited by
124
References
0
Citations by year
2026: 21 · 2025: 28 · 2024: 19 · 2023: 32 · 2022: 23 · 2021: 1
FWCI
9.69
Type
journal article
Language
en
Open access
Licence
Locations
  • IEEE Sensors Journal (publishedVersion)
  • arXiv (Cornell University) (submittedVersion)
  • Iris (University of Trento) (submittedVersion, other-oa)
  • Archivio istituzionale della ricerca (Alma Mater Studiorum Università di Bologna) (submittedVersion, other-oa)
  • arXiv (Cornell University) (cc-by)
Funders
  • Sacertis S.r.l.
  • Italian Ministry for Education, University and Research (MIUR) through the program Dipartimenti di Eccellenza
  • Electronic Components and Systems for European Leadership (ECSEL) Joint Undertaking (Arrowhead Tools) through the European Union Horizon 2020 Research and Innovation Programme and the member states (826452)
Topics
  • Physical Sciences › Engineering › Civil and Structural Engineering › Structural Health Monitoring Techniques
  • Physical Sciences › Engineering › Electrical and Electronic Engineering › Arduino and IoT Applications
  • Physical Sciences › Computer Science › Computer Networks and Communications › Wireless Sensor Networks for Data Analysis
Keywords
structural health monitoring, civil infrastructure monitoring, MEMS accelerometer, NB-IoT, wireless sensor nodes, modal analysis, low-power sensor nodes, energy harvesting, solar-powered sensors, energy-neutral operation, low-cost sensors, sensor node lifetime

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review