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Uncertainty in Measurement: Accuracy and Precision03:37

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Scientists typically make repeated measurements of a quantity to ensure the quality of their findings and to evaluate both the precision and the accuracy of their results. Measurements are said to be precise if they yield very similar results when repeated in the same manner. A measurement is considered accurate if it yields a result that is very close to the true or the accepted value. Precise values agree with each other; accurate values agree with a true value. 
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Scientists always try their best to record measurements with the utmost accuracy and precision. However, sometimes errors do occur. These errors can be random or systematic. Random errors are observed due to the inconsistency or fluctuation in the measurement process, or variations in the quantity itself that is being measured. Such errors fluctuate from being greater than or less than the true value in repeated measurements. Consider a scientist measuring the length of an earthworm using a...
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Uncertainty in Measurement: Reading Instruments02:46

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Counting is the type of measurement that is free from uncertainty, provided the number of objects being counted does not change during the process. Such measurements result in exact numbers. By counting the eggs in a carton, for instance, one can determine exactly how many eggs are there in the carton. Similarly, the numbers of defined quantities are also exact. For example, 1 foot is exactly 12 inches, 1 inch is exactly 2.54 centimeters, and 1 gram is exactly 0.001 kilograms. Quantities...
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Accuracy and Precision01:52

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Scientists typically make repeated measurements of a quantity to ensure the quality of their findings and to evaluate both the precision and the accuracy of their results. Measurements are said to be precise if they yield very similar results when repeated in the same manner. A measurement is considered accurate if it yields a result that is very close to the true or the accepted value. Precise values agree with each other; accurate values agree with a true value.  Highly accurate...
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Statistical Analysis: Overview01:11

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When we take repeated measurements on the same or replicated samples, we will observe inconsistencies in the magnitude. These inconsistencies are called errors. To categorize and characterize these results and their errors, the researcher can use statistical analysis to determine the quality of the measurements and/or suitability of the methods.
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In any measurement, the precision of the measuring tool is an essential factor. An ordinary ruler, for example, can measure length to the closest millimeter; a caliper, on the other hand, can measure length to the nearest 0.01 mm. As a result, the caliper is a more precise measurement tool because it can measure extremely minute changes in length. The measurements will be more accurate if the measuring tool is more precise.
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Toward Sensor Measurement Reliability in Blockchains.

Ernesto Gómez-Marín1,2, Luis Parrilla2, Jose L Tejero López2

  • 1Infineon Technologies AG, 85579 Neubiberg, Germany.

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|December 23, 2023
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Summary
This summary is machine-generated.

This study introduces a secure architecture for Internet of Things (IoT) devices to send data to blockchain supply chains. It enhances data integrity and trustworthiness using Hardware Security Modules and a novel blockchain-based Public Key Infrastructure (PKI).

Keywords:
Internet of Things (IoT)blockchainhardware oraclepublic key infrastructure (PKI)smart contracttrustworthiness

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Area of Science:

  • Computer Science
  • Cryptography
  • Distributed Systems

Background:

  • Blockchains offer secure data processing, but data authenticity relies on trusted information sources (Oracles).
  • IoT devices in uncontrolled environments pose challenges for data reliability and security in blockchain applications.
  • Existing solutions lack comprehensive security for IoT Oracles transmitting sensor data to blockchains.

Purpose of the Study:

  • To present a holistic, secure architecture for IoT Oracles sending data to blockchain-based supply chains.
  • To ensure the authenticity, integrity, trustworthiness, and freshness of data from IoT devices.
  • To address the gap in secure IoT Oracle implementations for blockchain integration.

Main Methods:

  • Utilized Hardware Security Modules (HSMs) for device integrity and trustworthiness.
  • Developed a novel Public Key Infrastructure (PKI) leveraging blockchain for authenticity, traceability, and data freshness.
  • Implemented and evaluated the solution on the Ethereum blockchain.

Main Results:

  • The proposed architecture successfully addresses key security requirements for IoT Oracles.
  • Demonstrated the feasibility of using HSMs and a blockchain-based PKI for secure data transmission.
  • Evaluated performance concerning security requirements and response times on the Ethereum network.

Conclusions:

  • The presented secure architecture provides a robust solution for integrating IoT devices as Oracles in blockchain systems.
  • The combination of HSMs and a blockchain-based PKI enhances the reliability of data fed into supply chain applications.
  • Future work will focus on addressing the identified flexibility limitations of the current design.