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Cited by 27Blockchain Technology Applications and SecurityData Quality and ManagementFood Supply Chain Traceability

IoT Data Qualification for a Logistic Chain Traceability Smart Contract

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Ahmed, Mohamed, Taconet, Chantal, Ould, Mohamed, Chabridon, Sophie, Bouzeghoub, Amel·2021·Sensors
DOI 10.3390/s21062239PMC PMC8005206
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Abstract

In the logistic chain domain, the traceability of shipments in their entire delivery process from the shipper to the consignee involves many stakeholders. From the traceability data, contractual decisions may be taken such as incident detection, validation of the delivery or billing. The stakeholders require transparency in the whole process. The combination of the Internet of Things (IoT) and the blockchain paradigms helps in the development of automated and trusted systems. In this context, ensuring the quality of the IoT data is an absolute requirement for the adoption of those technologies. In this article, we propose an approach to assess the data quality (DQ) of IoT data sources using a logistic traceability smart contract developed on top of a blockchain. We select the quality dimensions relevant to our context, namely accuracy, completeness, consistency and currentness, with a proposition of their corresponding measurement methods. We also propose a data quality model specific to the logistic chain domain and a distributed traceability architecture. The evaluation of the proposal shows the capacity of the proposed method to assess the IoT data quality and ensure the user agreement on the data qualification rules. The proposed solution opens new opportunities in the development of automated logistic traceability systems.

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  • Some primary studies use data quality issues and dimensions interchangeably (e.g., data freshness).

    “Some primary studies use data quality issues and dimensions interchangeably (e.g., data freshness [1]).”

    From A Systematic Review of Data Quality in CPS and IoT for Industry 4.0 (2023) · cites this paper as [1]

    Section 4.1, Data Quality Definitions, identifies freshness as another name for currentness, a data-quality dimension. Section 4.2, Related Works Study Criteria, says dimensions are used to address data-quality issues, but does not use “issue” and “dimension” interchangeably. No page numbers are available in our copy. (LLM, conf=0.82)

    Experimental — accuracy not yet measured

  • Five primary studies [1,14,26,42,44] focus on data quality management in the Cloud-based architecture layers (L4, L5, and L6).

    “Five primary studies [1,14,26,42,44] focus on data quality management in the Cloudbased architecture layers (L4, L5, and L6).”

    From A Systematic Review of Data Quality in CPS and IoT for Industry 4.0 (2023) · cites this paper as [1]

    Section 4.3 describes this study as proposing an IoT data-quality assessment architecture, so it supports identifying it as a study focused on data-quality management. Section 6 describes a distributed blockchain-based architecture, but our copy does not identify it as cloud-based or assign it to layers L4, L5, and L6. The count of five studies belongs to the citing document’s grouping, not to this source. (LLM, conf=0.79)

    Experimental — accuracy not yet measured

  • There is an explicit dependency between the data storage solution and the data quality support (i.e., ensuring data security) in these two works.

    “There is an explicit dependency between the data storage solution and the data quality support (i.e., ensuring data security) in these two works.”

    From A Systematic Review of Data Quality in CPS and IoT for Industry 4.0 (2023) · cites this paper as [1]

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Research works printed but not linked: 33

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Cited by

Sorted by citations

Authors
Ahmed, M., Taconet, C., Ould, M., Chabridon, S., & Bouzeghoub, A.
Affiliations
  • Institut Polytechnique de Paris (FR)
  • ALIS International, 4 Rue du Meunier, 95724 Roissy-en-France, France
Corresponding author
  • Ahmed, Mohamed
  • Taconet, Chantal
ORCID
Venue
Sensors
Publisher
MDPI AG
Published
2021-03-23
Volume
21
Issue
6
Pages
2239
ISSN
1424-8220 (ISSN-L)
DOI
10.3390/s21062239
PMC
PMC8005206
OpenAlex
W3136600682
Cited by
27
References
3
Citations by year
2026: 3 · 2025: 4 · 2024: 5 · 2023: 7 · 2022: 7 · 2021: 1
FWCI
4.52
Type
journal article
Language
en
Open access
Licence
Locations
  • Sensors (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • HAL (submittedVersion, cc-by)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
  • Oskar-Bordeaux (Universite de Bordeaux) (submittedVersion, cc-by)
Funders
ALIS (THESIS FUNDING)
Topics
  • Physical Sciences › Computer Science › Information Systems › Blockchain Technology Applications and Security
  • Social Sciences › Decision Sciences › Management Science and Operations Research › Data Quality and Management
  • Life Sciences › Agricultural and Biological Sciences › Food Science › Food Supply Chain Traceability
Keywords
data quality assessment, smart contracts, blockchain, data quality dimensions, accuracy, completeness, consistency, data quality model, supply chain transparency

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