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Published July 31, 2024 | Version 1
Dataset Open

Raw Data for Energy Consumption Comparison of DMA-Based and FatFs Storage Systems on Wearable Devices

  • 1. ROR icon University of Castilla-La Mancha
  • 2. Universidad de Castilla-La Mancha
  • 3. Ulster University

Description

This dataset contains raw oscilloscope measurements comparing the energy consumption of a Direct Memory Access (DMA)-based storage system versus the FatFs file system for wearable devices. The data was collected as part of the study "Direct Memory Access-Based Data Storage for Long-Term Acquisition Using Wearables in an Energy-Efficient Manner".

The dataset includes voltage drop measurements across a 2-ohm shunt resistor, captured using an Analog Discovery 2 digital oscilloscope at a 500 kHz sampling rate. Measurements were taken under various conditions:

  • Storage systems: DMA-based (proposed) and FatFs
  • SD card capacities: 4 GB and 8 GB
  • Write frequencies: 2 Hz and 5 Hz (referring to the frequency of writing a specific data block of 15,872 bytes)
  • With and without a smoothing capacitor

Each CSV file contains 20 million samples, equivalent to 40 seconds of data acquisition. File names encode the experimental conditions, including the storage system, write frequency, number of samples, acquisition rate, acquisition time, data format, SD card size, and absence of the smoothing capacitor.

The data is organized into two main folders:

  1. "cap": Contains measurements with the smoothing capacitor
  2. "no_cap": Contains measurements without the smoothing capacitor

This raw data can be used to reproduce the energy consumption and write speed analyses presented in the article, as well as for further investigation into the performance of embedded storage systems for wearable devices.

Files

DMA-based_data_storage_for_long-term_acquisition_using wearables_in_an_energy-efficient_manner.zip

Additional details

Funding

sSITH: Self-recharging Sensorized Insoles for Continuous Long-Term Human Gait Monitoring PDC2022-133457-I00
Ministerio de Ciencia, Innovación y Universidades
Just move!': Early detection of MCI through human-movement analysis in everyday life JUST-MOVE MCIN/AEI/10.13039/501100011033
Ministerio de Ciencia, Innovación y Universidades
Plan Propio de Investigación 660787. [2022/10970]
University of Castilla-La Mancha