IoT Data Quality Issues and Potential Solutions: A Literature Review

Mansouri, Taha; Moghadam, Mohammad Reza Sadeghi; Monshizadeh, Fatemeh; Zareravasan, Ahad

doi:10.1093/comjnl/bxab183

Cited by 14 publications

(1 citation statement)

References 87 publications

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“…In the literature, data quality often refers to aspects such as completeness, correctness, timeliness, accuracy, and availability (cf. [7,[10][11][12]). The criteria completeness and correctness are similar to those in our work.…”

Section: Related Workmentioning

confidence: 99%

A Finite State Automaton for Green Data Validation in a Real-World Smart Manufacturing Environment with Special Regard to Time-Outs and Overtaking

Paasche,

Groppe

2023

Future Internet

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Since data are the gold of modern business, companies put a huge effort into collecting internal and external information, such as process, supply chain, or customer data. To leverage the full potential of gathered information, data have to be free of errors and corruptions. Thus, the impacts of data quality and data validation approaches become more and more relevant. At the same time, the impact of information and communication technologies has been increasing for several years. This leads to increasing energy consumption and the associated emission of climate-damaging gases such as carbon dioxide (CO2). Since these gases cause serious problems (e.g., climate change) and lead to climate targets not being met, it is a major goal for companies to become climate neutral. Our work focuses on quality aspects in smart manufacturing lines and presents a finite automaton to validate an incoming stream of manufacturing data. Through this process, we aim to achieve a sustainable use of manufacturing resources. In the course of this work, we aim to investigate possibilities to implement data validation in resource-saving ways. Our automaton enables the detection of errors in a continuous data stream and reports discrepancies directly. By making inconsistencies visible and annotating affected data sets, we are able to increase the overall data quality. Further, we build up a fast feedback loop, allowing us to quickly intervene and remove sources of interference. Through this fast feedback, we expect a lower consumption of material resources on the one hand because we can intervene in case of error and optimize our processes. On the other hand, our automaton decreases the immaterial resources needed, such as the required energy consumption for data validation, due to more efficient validation steps. We achieve the more efficient validation steps by the already-mentioned automaton structure. Furthermore, we reduce the response time through additional recognition of overtaking data records. In addition, we implement an improved check for complex inconsistencies. Our experimental results show that we are able to significantly reduce memory usage and thus decrease the energy consumption for our data validation task.

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Section: Related Workmentioning

confidence: 99%