A computer vision-based IoT data ingestion architecture supporting data prioritization

Kiourtis, Athanasios; Mavrogiorgou, Argyro; Kyriazis, Dimosthenis

doi:10.1007/s12553-023-00748-0

Cited by 3 publications

(1 citation statement)

References 47 publications

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“…According to Zahedina et al, data prioritization approaches focus on accessing correct data as quickly as possible. Therefore, IoT data are prioritized based on properties such as timeliness and significance to only use the most valuable data [25,26]. As a result, the approach of Zahedina et al [25], for example, removes low-priority data from the cache.…”

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%

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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Assessing the impact of health information exchange on hospital data breach risk

Choi

Chen

Tan

2023

International Journal of Medical Informatics

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Interfacing the IoT in composite manufacturing: An overview

Gowtham,

Jayasheela,

Sivamani

et al. 2024

Reviews on Advanced Materials Science

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It is a well-known fact that many sophisticated works consume a lot of human resources, leading to the need to find effective alternative. The manufacturing industry demands a lot of human resources, with around half of the global working population participating in this sector. Challenges such as sudden conflicts in the data, disasters, and loss of productivity are encountered by the manufacturing industries and can be overcome by monitoring machine performance data and automatically configuring the machines according to changing needs. This emphasizes the importance of the Internet of Things (IoT) in addressing niche areas of manufacturing. IoT is a buzzword heard everywhere around the globe. Implementing this technology makes most of the work more accessible than other conventional methods. This has created a lot of research interest on this topic. Among many manufacturing sectors, polymer composite material manufacturing is one of the most demanding. This review article purely focuses on polymer composite manufacturing and its allied processes. The consolidation of data is based on the influence of IoT on the extraction of fibers and manufacturing of polymer composite material using novel techniques, quality assessment of manufactured polymer composite material, challenges faced in exploring the use of IoT, and future scope. It can be stated from the survey that various researchers have minimally explored the incorporation of IoT, but its future looks very promising in terms of producing high-quality products at less time and lower cost by integrating this technique with conventional methods.

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A computer vision-based IoT data ingestion architecture supporting data prioritization

Cited by 3 publications

References 47 publications

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

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

Assessing the impact of health information exchange on hospital data breach risk

Interfacing the IoT in composite manufacturing: An overview

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