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

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Abstract

Purpose

As Internet of Things (IoT) evolves, additional focus should be provided in aggregating and ingesting the most valuable data. Several techniques aim to identify the data portions that can offer significant impact, targeting to distinguish the IoT devices’ nature, whose applicability is lacking in simultaneous sectors.

Methods

The current manuscript proposes a Data Ingestion and IoT Data Prioritization process, supporting the connection and integration of heterogeneous IoT medical devices with healthcare platforms, to ingest IoT data of any device and store them in the preferred Cloud Storage service. In sequel, a specific prioritization process is provided, through which the ingested data of the IoT device can be filtered, providing slices of data which are more significant to the requesting stakeholders. This is constructed to firstly ingest and then perform data prioritization to the data to be provided from a citizen’s IoT device to the requesting Healthcare Practitioner, in which initial steps involve the connection of the IoT device to a specifically designed middleware, for identifying its nature and the programmatic methods being responsible for ingesting the device’s data, using computer vision and semantic fingerprinting. The ingested data can then be segmented, provided with scores based on their significance, and receive metadata annotation about their nature. Subsequently, the data are sliced, being forwarded to the Healthcare Practitioner that requested them, following a methodology for metadata ranking.

Results

An evaluation was performed with several types of data, ingesting in an efficient manner healthcare data from IoT devices of low or high type of priority. For the Data Ingestion process, the extracted outcomes were that the overall process could always successfully connect all the IoT Bluetooth-enabled devices through the provided Bluetooth interface. With regards to the IoT Data Prioritization process, the extracted outcome was that IoT gathered data were segmented and split into smaller chunks of low and high priority data based on specific thresholds, prioritizing the most important data that could drive towards more efficient decision-making and overall results.

Conclusions

It is undeniable that the challenges of IoT devices’ management are a very demanding research topic, especially regarding the gathering and ingesting of heterogeneous IoT devices’ data. For that reason, an efficient process was designed and implemented to function in the context of the healthcare sector, giving an increased genericity regarding data ingestion and prioritization for the data to be shared in the healthcare domain. Multiple experiments and tests have taken place, all of them concluding to efficient and high-value data ingestion and prioritization processes, proving the overall process’s applicability.

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Availability of data and material

The data that support the findings of this study are available from University of Piraeus Research Center, but restrictions apply to the availability of these data, which were used under license for the current study, and so are not publicly available. Data are however available from the authors upon reasonable request and with permission of University of Piraeus Research Center.

Code availability

The software code that supports this study is available from the University of Piraeus Research Center, but restrictions apply to the availability of the software code, which was used under license for the current study. Software code is however available from the authors upon reasonable request and with permission of University of Piraeus Research Center.

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Funding

The research leading to this result has received funding from the European Union and Greek national funds through the Operational Program Competitiveness, Entrepreneurship and Innovation, under the call RESEARCH - CREATE - INNOVATE (project code: DIASTEMA - T2EDK-04612) and from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 826106 (InteropEHRate project).

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Authors and Affiliations

  1. Department of Digital Systems, University of Piraeus, Piraeus, Greece

    Athanasios Kiourtis, Argyro Mavrogiorgou & Dimosthenis Kyriazis

  2. University of Piraeus, 80, M. Karaoli & A. Dimitriou St, 18534, Piraeus, Greece

    Athanasios Kiourtis

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  1. Athanasios Kiourtis
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  2. Argyro Mavrogiorgou
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Contributions

All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Athanasios Kiourtis, and Argyro Mavrogiorgou. The first draft of the manuscript was written by Athanasios Kiourtis and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

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Correspondence to Athanasios Kiourtis.

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Kiourtis, A., Mavrogiorgou, A. & Kyriazis, D. A computer vision-based IoT data ingestion architecture supporting data prioritization. Health Technol. 13, 391–411 (2023). https://doi.org/10.1007/s12553-023-00748-0

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