Validation of Experimental Data Origins: A Swarm of DAQ devices able to Deliver Unique Experimental Data using Blockchain‐like Fingerprint ID to a Data Repository
An innovative experimental setup and procedure for the automation and management of collected experimental data in real-time compatible with any open environment. It was conceptualized and prototyped a Swarm Learning (SL) architecture (hardware and software) as a fully decentralized algorithm principle to improve data trustworthiness. 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 datasets across multiple smart data acquisition devices (SDAD) connected locally or remotely. Every participating site 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 a 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 made available for analysis from sources outside the primary scientific research of a given specific site, and 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.
All research data is available on my Github here:
https://github.com/aeonSolutions/openScience-Smart-DAQ-to-Upload-Live-Experimental-Data-to-a-Data-Repository/wiki