Direct Lightweight Temporal Compression for Wearable Sensor Data

Research output: Contribution to journalArticleScientificpeer-review

11 Downloads (Pure)


Emerging technologies enable massive deployment of wireless sensor networks across many industries. Internet of Things devices are often deployed in critical infrastructure or health monitoring and require fast reaction time, reasonable accuracy, and high energy efficiency. In this work 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 (LTC) method, targeting further reconstruction error minimization and complexity reduction. This work 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.

Original languageEnglish
Article number7000404
Number of pages4
JournalIEEE Sensors Letters
Issue number2
Publication statusPublished - 2021
Publication typeA1 Journal article-refereed


  • Benchmark testing
  • Complexity theory
  • Data compression
  • Direct LTC (DLTC)
  • Internet of Things
  • IoT
  • Lightweight Temporal Compression (LTC)
  • Performance evaluation
  • redundancy reduction
  • Sensor phenomena and characterization
  • time series
  • Upper bound

Publication forum classification

  • Publication forum level 1

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Instrumentation


Dive into the research topics of 'Direct Lightweight Temporal Compression for Wearable Sensor Data'. Together they form a unique fingerprint.

Cite this