Expected impact of this project on Scientific Research and Businesses

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Last update: 17-07-2024
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This research project focuses on prototyping smart open hardware electronics for data acquisition for innovative experimental setup and procedure for the automation and management of collected experimental data in real-time and compatible with any open environment. It is being conceptualized and prototyped as a Swarm Learning (SL) architecture (hardware and software), a fully decentralized algorithm principle to improve the trustworthiness of sensor measurements in an experiment, experimental campaign, or scientific research project. Conceptually, SL is a decentralized approach to validate and maintain a trustworthy database of experimental data in real-time, publicly accessible, through data redundancy, validation, and authentication of data records across multiple smart data acquisition devices (SDAD) connected locally or remotely. Every participating SDAD is a node in the Swarm network with the purpose of performing data validation and authentication tasks by sharing local hardware resources. Data trustworthiness and sovereignty are ensured with the real-time generation of a Unique Data Fingerprint Identification (UDFID) for a single experimental data record. New SDADs can enter a Swarm Network via a blockchain smart contract, regulating access and operational conditions in a fully autonomous way. New Swarm nodes agree to the collaboration terms, obtain the model, and perform local validation and authentication until all tasks are completed. This allows the acquisition of much larger experimental datasets, validated and authenticated publicly, while at the same time enabling data analysis from sources outside the primary scientific research of a given specific site or laboratory. This offers new opportunities to overcome the limitations of collaborative work in science, by enabling any research site to easily connect and join a swarm network increasing experimental data trustworthiness from unknown sources.

The proposed Smart Open hardware electronics for Data Acquisition uses a UDFID based on Micah Altman's [13] UNF algorithm and adds minimum hardware electronics requirements onboard each SDAD to add the proposed "Unique Data Fingerprint Identification" (UDFID) for the creation of new data records after each collection of measurements from external sensors connected to a specimen.

The key characteristic of the proposed method for data acquisition of values from sensors set up in a specimen is in the distributed nature of the Data acquisition system required for an experimental setup. The proposed DAQ system requires one SDAD for each specimen and its advantages over traditional shared DAQ systems can be summarized as follows:

• In case of malfunction of an SDAD, the experiment is not interrupted and can continue to collect sensor measurements from all other specimens. having several SDADs in the same experiment makes the DAQ system more resilient to external attacks, tampering, and mishandling during the collection of measurements in an experiment.
• Allow real-time experimental data validation of its origins and on-site authoring with swarm intelligence among SDADs. using UDFID
• The proposed method is capable of real-time uploads of individual data records to a data repository after the collection of measurements during an experiment.
• allows remote auditing of an experiment and reduces the time necessary to detect any anomalies or malfunctions during an experiment
• allows third-party automated auditing of the experimental data produced during an experiment

in short, the open hardware electronics being prototyped and the open firmware code being programmed by this project allows

• a scientific researcher to publish in higher-ranked scientific journals;
• scientific researchers from outside academia and sci. research enterprise or business to publish in high-ranked scientific journals;
• businesses and institutions to use collected data in cases of legal dispute and court of law;

References

[13] Altman, M. (2008). A Fingerprint Method for Scientific Data Verification. In: Sobh, T. (eds) Advances in Computer and Information Sciences and Engineering. Springer, Dordrecht. https://doi.org/10.1007/978-1-4020-8741-7_57