A Study on Data Loss Compensation of WiFi-Based Wireless Sensor Networks for Structural Health Monitoring
Abstract
In wireless data transmissions processes, data loss is an important factor that reduces the robustness of wireless sensor networks (WSNs). In many practical engineering applications, data loss compensation algorithms are then required to maintain the robustness, and such algorithms are typically based on compressive sensing with a large memory of microcontrollers needed. This paper presents an improved algorithm, based on random demodulator, to overcome the difficulty of microcontroller-dependence in the traditional data loss algorithms. The newly developed algorithm demonstrates the following advantages: low space complexity; low floating-point calculations; and low time complexity. Therefore, it is more suitable to be embedded into ordinary nodes, comparing with the traditional algorithms. In this paper, a WSN based on WiFi is also developed for verifying the effectiveness and feasibility of the proposed algorithm. Field experiment on Xinghai Bay Bridge is done. Experimental results show that the WSN works properly and steady. Moreover, the data loss can be compensated effectively and efficiently through the use of the present algorithm.
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The vulnerability of wireless communication channels can result in data loss during transmission, as well as errors in data analysis and prediction.
“The vulnerability of wireless communication channels can result in data loss during transmission, as well as errors in data analysis and prediction [109].”
From Review of Wearable Devices and Data Collection Considerations for Connected Health (2021) · cites this paper as [109]
Cites
Donoho, D. L. (2006) · IEEE Transactions on Information Theory · doi:10.1109/tit.2006.871582 · Cited by 23,994 · see citing works
Donoho, D. L. (2004) · Cited by 17,049 · see citing works
Candes, E. J., Romberg, J., & Tao, T. (2006) · IEEE Transactions on Information Theory · doi:10.1109/tit.2005.862083 · Cited by 15,639 · see citing works
An Introduction To Compressive Sampling
Candes, E. J., & Wakin, M. B. (2008) · IEEE Signal Processing Magazine · doi:10.1109/msp.2007.914731 · Cited by 10,017 · see citing works
Decoding by Linear Programming
Candès, E. J., & Tao, T. (2005) · IEEE Transactions on Information Theory · doi:10.1109/tit.2005.858979 · Cited by 6,987 · see citing works
Near-Optimal Signal Recovery From Random Projections: Universal Encoding Strategies?
Candès, E. J., & Tao, T. (2006) · IEEE Transactions on Information Theory · doi:10.1109/tit.2006.885507 · Cited by 6,973 · see citing works
Sparsity and incoherence in compressive sampling
Candès, E. J., & Romberg, J. K. (2007) · Inverse Problems · doi:10.1088/0266-5611/23/3/008 · Cited by 2,109 · see citing works
Beyond Nyquist: Efficient Sampling of Sparse Bandlimited Signals
Tropp, J. A., Laska, J. N., Duarte, M. F., Romberg, J. K., & Baraniuk, R. G. (2010) · IEEE Transactions on Information Theory · doi:10.1109/tit.2009.2034811 · Cited by 1,089 · see citing works
Smart sensing technology: opportunities and challenges
Spencer, B. F., Ruiz-Sandoval, M. E., & Kurata, N. (2004) · Structural Control and Health Monitoring · doi:10.1002/stc.48 · Cited by 572 · see citing works
Theory and Implementation of an Analog-to-Information Converter using Random Demodulation
Laska, J. N., Kirolos, S., Duarte, M. F., et al. (2007) · IEEE International Symposium on Circuits and Systems proceedings · doi:10.1109/iscas.2007.378360 · Cited by 422 · see citing works
Analog-to-Information Conversion via Random Demodulation
Kirolos, S., Laska, J., Wakin, M., Duarte, M., Baron, D., Ragheb, T., Massoud, Y., & Baraniuk, R. (2006) · 2006 IEEE Dallas/CAS Workshop on Design, Applications, Integration and Software · doi:10.1109/dcas.2006.321036 · Cited by 393 · see citing works
Structural health monitoring using smart sensors
Nagayama, T., Spencer, J. F., & Rice, J. (2008) · CRC Press eBooks · doi:10.1201/9781439828441.ch129 · Cited by 235 · see citing works
The applications of WiFi-based Wireless Sensor Network in Internet of Things and Smart Grid
Li, L., Hu, X., Chen, K., & Ketai, H. (2011) · IEEE Conference on Industrial Electronics and Applications (ICIEA) · doi:10.1109/iciea.2011.5975693 · Cited by 235 · see citing works
Data loss and reconstruction in sensor networks
Kong, L., Xia, M., Liu, X., et al. (2013) · IEEE Conference on Computer Communications (INFOCOM) · doi:10.1109/infcom.2013.6566962 · Cited by 213 · see citing works
Bao, Y., Li, H., Sun, X., Yu, Y. P., & Ou, J. (2012) · Structural Health Monitoring · doi:10.1177/1475921712462936 · Cited by 188 · see citing works
Self-citationIssues in structural health monitoring employing smart sensors
Nagayama, T., Sim, S., Miyamori, Y., & Spencer, B. F. (2007) · Smart Structures and Systems · doi:10.12989/sss.2007.3.3.299 · Cited by 164 · see citing works
Everlast: Long-life, Supercapacitor-operated Wireless Sensor Node
Simjee, F., & Chou, P. H. (2006) · International Symposium on Low Power Electronics and Design (ISLPED) · doi:10.1109/lpe.2006.4271835 · Cited by 155 · see citing works
A Tutorial on Fast Fourier Sampling
Gilbert, A. C., Strauss, M., & Tropp, J. A. (2008) · IEEE Signal Processing Magazine · doi:10.1109/msp.2007.915000 · Cited by 142 · see citing works
Zou, Z., Bao, Y., Li, H., Spencer, B. F., & Ou, J. (2014) · IEEE Sensors Journal · doi:10.1109/jsen.2014.2353032 · Cited by 120 · see citing works
Self-citationA WiFi based smart wireless sensor network for monitoring an agricultural environment
Mendez, G. R., Yunus, M. A. M., & Mukhopadhyay, S. C. (2012) · IEEE International Instrumentation and Measurement Technology Conference · doi:10.1109/i2mtc.2012.6229653 · Cited by 61 · see citing works
Wireless sensor networks for long-term structural health monitoring
Meyer, J., Bischoff, R., Feltrin, G., et al. (2010) · Smart Structures and Systems · doi:10.12989/sss.2010.6.3.263 · Cited by 58 · see citing works
Yu, Y. P., Ou, J., & Li, H. (2010) · Smart Structures and Systems · doi:10.12989/sss.2010.6.5_6.641 · Cited by 43 · see citing works
Self-citationWijetunge, S., Gunawardana, U., & Liyanapathirana, R. (2010) · doi:10.1109/ictel.2010.5478798 · Cited by 42 · see citing works
Distributed fault detection in industrial system based on sensor wireless network
Tan, K. K., Huang, S., Zhang, Y., et al. (2008) · Computer Standards & Interfaces · doi:10.1016/j.csi.2008.03.024 · Cited by 38 · see citing works
Characterization of Wireless Smart Sensor Performance
Linderman, L. E., Rice, J. A., Barot, S., et al. (2010) · Journal of Engineering Mechanics · doi:10.1061/(asce)em.1943-7889.0000187 · Cited by 37 · see citing works
Design of a wireless measurement system based on WSNs for large bridges
Liu, Z., Yu, Y. P., Liu, G., et al. (2014) · Measurement · doi:10.1016/j.measurement.2014.01.013 · Cited by 35 · see citing works
Self-citationA Method of Data Recovery Based on Compressive Sensing in Wireless Structural Health Monitoring
Ji, S., Sun, Y., & Shen, J. (2014) · Mathematical Problems in Engineering · doi:10.1155/2014/546478 · Cited by 23 · see citing works
Real-Time Wireless Data Acquisition for Structural Health Monitoring and Control
Linderman, L. E., Mechitov, K. A., & Spencer, B. F. (2011) · Illinois Digital Environment for Access to Learning and Scholarship (University of Illinois at Urbana-Champaign) · Cited by 14 · see citing works
The Application of WiFi-based Wireless Sensor Network (WSN) in Hill Slope Condition Monitoring
Yunus, M. A. M., Ibrahim, S., Khairi, M. T. M., et al. (2015) · Jurnal Teknologi · doi:10.11113/jt.v73.4250 · Cited by 2 · see citing works
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Review of Wearable Devices and Data Collection Considerations for Connected Health
Vijayan, V., Connolly, J. P., Condell, J., McKelvey, N., & Gardiner, P. (2021) · Sensors · doi:10.3390/s21165589 · Cited by 435 · see citing works
- Authors
- Yu, Y., Han, F., Bao, Y., & Ou, J.
- Affiliations
- Dalian University of Technology (CN)
- Harbin Institute of Technology (CN)
- Venue
- IEEE Sensors Journal
- Published
- 2016-05
- Volume
- 16
- Issue
- 10
- Pages
- 3811-3818
- ISSN
- 1530-437X (ISSN-L)
- 1558-1748
- 2379-9153
- OpenAlex
- W2340360321
- Cited by
- 48
- References
- 29
- Citations by year
- 2026: 2 · 2025: 6 · 2024: 7 · 2023: 6 · 2022: 2 · 2021: 6 · 2020: 6 · 2019: 3 · 2018: 6 · 2017: 2 · 2016: 2
- FWCI
- 5.50
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- IEEE Sensors Journal (publishedVersion)
- Funders
- National Natural Science Foundation of China (50921001, 51161120359)
- National Basic Research Program of China (2011CB013705)
- International Science and Technology Cooperation Program of China (2015DFG82080)
- Fundamental Research Funds for the Central Universities (DUT15ZD117)
- Topics
- Physical Sciences › Engineering › Civil and Structural Engineering › Structural Health Monitoring Techniques
- Physical Sciences › Computer Science › Computer Networks and Communications › Energy Efficient Wireless Sensor Networks
- Physical Sciences › Engineering › Computational Mechanics › Sparse and Compressive Sensing Techniques
- Keywords
- wireless sensor networks, random demodulator, compressed sensing, structural health monitoring, space complexity, low-complexity algorithms, bridge monitoring
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