The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Data Cleaning for RFID and WSN Integration
Abstract
Today's manufacturing environments are very dynamic and turbulent. Traditional enterprise information systems (EISs) have mostly been implemented upon hierarchical architectures, which are inflexible to adapt changes and uncertainties promptly. Next-generation EISs must be agile and adaptable to accommodate changes without significant time delays. It is essential for an EIS to obtain real-time data from the distributed and dynamic manufacturing environment for decision making. Wireless sensor networks (WSNs) and radio-frequency identification (RFID) systems provide an excellent infrastructure for data acquisition, distribution, and processing. In this paper, some key challenges related to the integration of WSN and RFID technologies are discussed. A five-layer system architecture has been proposed to achieve synergistic performance. For the integration of WSN and RFID, one of the critical issues is the low efficiency of communication due to redundant data as redundant data increases energy consumption and causes time delay. To address it, an improved data cleaning algorithm has been proposed; its feasibility and effectiveness have been verified via simulation and a comparison with a published algorithm. To illustrate the capacity of the developed architecture and new data cleaning algorithm, their application in relief supplies storage management has been discussed.
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
EMG and EPP-integrated human-machine interface between the paralyzed and rehabilitation exoskeleton
Yin, Y. H., Fan, Y. J., & Xu, L. D. (2012) · IEEE Transactions on Information Technology in Biomedicine · doi:10.1109/titb.2011.2178034 · Cited by 197
Self-citation
Cited by
Internet of Things in industries: A survey
Xu, L. D., He, W., & Li, S. (2014) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2014.2300753 · Cited by 5,172
IoT-Based Smart Rehabilitation System
Fan, Y. J., Yin, Y. H., Xu, L. D., Zeng, Y., & Wu, F. (2014) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2014.2302583 · Cited by 444
Self-citationDesign and application of Internet of things-based warehouse management system for smart logistics
Lee, C. K. M., Lv, Y., Ng, K. K. H., Ho, W., & Choy, K. L. (2018) · International Journal of Production Research · doi:10.1080/00207543.2017.1394592 · Cited by 431
Internet of Things in Asset Management: Insights from Industrial Professionals and Academia
Kinnunen, S.-K., Ylä-Kujala, A., Marttonen-Arola, S., Kärri, T., & Baglee, D. (2018) · International Journal of Service Science, Management, Engineering, and Technology · doi:10.4018/ijssmet.2018040105 · Cited by 24
Amudhevalli, R., & Sivakumar, D. T. (2019) · International Journal of Engineering Applied Sciences and Technology · doi:10.33564/ijeast.2019.v04i05.030 · Cited by 2
Metadata
- Authors
- Wang, L., Xu, L. D., Bi, Z., & Xu, Y.
- Venue
- IEEE Transactions on Industrial Informatics
- Published
- 2014-02
- Volume
- 10
- Issue
- 1
- Pages
- 408-418
- ISSN
- 1551-3203 (ISSN-L)
- 1941-0050
- OpenAlex
- W2012296875
- Cited by
- 187
- References
- 1
- FWCI
- 41.91
- 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: Data Cleaning for RFID and WSN Integration. https://app.cochat.ai/papers/10.1109/tii.2013.2250510
- The paper
- Wang, L., Xu, L. D., Bi, Z., & Xu, Y. (2014). Data Cleaning for RFID and WSN Integration. IEEE Transactions on Industrial Informatics. https://doi.org/10.1109/tii.2013.2250510
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.