The DOI matches its Crossref record; 71 of the 71 references it cites were found in a registry or an index.
Structural Health Monitoring Using Wireless Sensor Networks: A Comprehensive Survey
Abstract
Structural health monitoring (SHM) using wireless sensor networks (WSNs) has gained research interest due to its ability to reduce the costs associated with the installation and maintenance of SHM systems. SHM systems have been used to monitor critical infrastructure such as bridges, high-rise buildings, and stadiums and has the potential to improve structure lifespan and improve public safety. The high data collection rate of WSNs for SHM pose unique network design challenges. This paper presents a comprehensive survey of SHM using WSNs outlining the algorithms used in damage detection and localization, outlining network design challenges, and future research directions. Solutions to network design problems such as scalability, time synchronization, sensor placement, and data processing are compared and discussed. This survey also provides an overview of testbeds and real-world deployments of WSNs for SH.
Overview
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.
Integrity signals
Registry
Cites
Statistics
Verification
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.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Chiang, L. H., Russell, E. L., & Braatz, R. D. (2001) · Advanced Textbooks in Control and Signal Processing · doi:10.1007/978-1-4471-0347-9_4 · Cited by 6,594
An eigensystem realization algorithm for modal parameter identification and model reduction
Juang, J.-N., & Pappa, R. S. (1985) · Journal of Guidance, Control, and Dynamics · doi:10.2514/3.20031 · Cited by 2,762
Subspace Identification for Linear Systems
Van Overschee, P., & De Moor, B. (1996) · Springer eBooks · doi:10.1007/978-1-4613-0465-4 · Cited by 2,586
Subspace identification for linear systems: theory, implementation, applications
Overschee, P. V., & Moor, B. L. R. D. (1996) · CERN Document Server (European Organization for Nuclear Research) · Cited by 2,271
Mobile crowdsensing: current state and future challenges
Ganti, R., Ye, F., & Lei, H. (2011) · IEEE Communications Magazine · doi:10.1109/mcom.2011.6069707 · Cited by 2,054
Guided Lamb waves for identification of damage in composite structures: A review
Su, Z., Ye, L., & Lu, Y. (2006) · Journal of Sound and Vibration · doi:10.1016/j.jsv.2006.01.020 · Cited by 1,560
A Summary Review of Wireless Sensors and Sensor Networks for Structural Health Monitoring
Lynch, J. P. (2006) · The Shock and Vibration Digest · doi:10.1177/0583102406061499 · Cited by 1,533
A wireless sensor network For structural monitoring
Xu, N., Rangwala, S., Chintalapudi, K. K., Ganesan, D., Broad, A., Govindan, R., & Estrin, D. (2004) · Proceedings of the 2nd international conference on Embedded networked sensor systems · doi:10.1145/1031495.1031498 · Cited by 1,088
Damage Detection in Structures Using Changes in Flexibility
Pandey, A. K., & Biswas, M. (1994) · Journal of Sound and Vibration · doi:10.1006/jsvi.1994.1002 · Cited by 1,012
Energy efficiency in wireless sensor networks: A top-down survey
Rault, T., Bouabdallah, A., & Challal, Y. (2014) · Computer Networks · doi:10.1016/j.comnet.2014.03.027 · Cited by 908
Cited by
Malekloo, A., Ozer, E., AlHamaydeh, M., & Girolami, M. (2022) · Structural Health Monitoring · doi:10.1177/14759217211036880 · Cited by 607
Deep Learning for Detecting Building Defects Using Convolutional Neural Networks
Perez, H., Tah, J. H. M., & Mosavi, A. (2019) · Sensors · doi:10.3390/s19163556 · Cited by 253
Hassani, S., & Dackermann, U. (2023) · Sensors · doi:10.3390/s23063293 · Cited by 146
AI in structural health monitoring for infrastructure maintenance and safety
Plevris, V., & Papazafeiropoulos, G. (2024) · Infrastructures · doi:10.3390/infrastructures9120225 · Cited by 132
Structural Health Monitoring Damage Detection Systems for Aerospace
(2021) · Springer Aerospace Technology · doi:10.1007/978-3-030-72192-3 · Cited by 92
Metadata
- Authors
- Noel, A. B., Abdaoui, A., Elfouly, T., Ahmed, M. H., Badawy, A., & Shehata, M. S.
- Venue
- IEEE Communications Surveys & Tutorials
- Published
- 2017
- Volume
- 19
- Issue
- 3
- Pages
- 1403-1423
- ISSN
- 1553-877X (ISSN-L)
- OpenAlex
- W2604321178
- Cited by
- 489
- References
- 71
- FWCI
- 50.80
- Type
- journal article
- Language
- en
- Open access
- closed
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
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Structural Health Monitoring Using Wireless Sensor Networks: A Comprehensive Survey. https://app.cochat.ai/papers/10.1109/comst.2017.2691551
- The paper
- Noel, A. B., Abdaoui, A., Elfouly, T., Ahmed, M. H., Badawy, A., & Shehata, M. S. (2017). Structural Health Monitoring Using Wireless Sensor Networks: A Comprehensive Survey. IEEE Communications Surveys & Tutorials. https://doi.org/10.1109/comst.2017.2691551
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
