Published January 14, 2021 | Version v1
Journal article Open

Direct Lightweight Temporal Compression for Wearable Sensor Data

  • 1. Tampere University, Universitat Jaume I
  • 2. Tampere University
  • 3. Universitat Jaume I

Description

Emerging technologies enable massive deployment of wireless sensor networks across many industries. Internet of Things (IoT) devices are often deployed in critical infrastructure or health monitoring and require fast reaction time, reasonable accuracy, and high energy efficiency. In this letter, we introduce a lossy compression method for time-series data, named direct lightweight temporal compression (DLTC), enabling energy-efficient data transfer for power-restricted devices. Our method is based on the lightweight temporal compression method, targeting further reconstruction error minimization and complexity reduction. This letter highlights the key advantages of the proposed method and evaluates the method’s performance on several sensor-based, time-series data types. We prove that DLTC outperforms the considered benchmark methods in compression efficiency at the same reconstruction error level.

Notes

Funded by: A-WEAR - EU's Horizon 2020 Marie Sklodowska Curie Grant Agreement 813278, and Academy of Finland Grant 323244 and Grant 319994.

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Additional details

Funding

A-WEAR – A network for dynamic WEarable Applications with pRivacy constraints 813278
European Commission
Research Infrastructure for Future Wireless Communication Networks / Consortium: FUWIRI 319994
Academy of Finland