Titchy: Online Time-series Compression with Random Access for the Internet of Things

Rasmus Vestergaard, Qi Zhang, Marton Sipos, Daniel Enrique Lucani Rötter

Research output: Contribution to journal/Conference contribution in journal/Contribution to newspaperJournal articleResearchpeer-review

Abstract

We introduce Titchy, which is a compression method for time-series data generated by the Internet of Things. Our proposed method is flexible and has several advantages when applied in the IoT ecosystem: 1) it is able to compress even when only a small amount of memory can be allocated to it; 2) it compresses data in tiny chunks, so it introduces very little latency during online operation and thus, enables frequent and timely updates with compressed data; and 3) it facilitates efficient data storage and retrieval by enabling low-cost random access to the compressed data, eliminating the need to decompress large chunks when only a small amount of data is requested. To evaluate each of these advantages, we have implemented the compressor and conducted extensive experiments with long-term real-world data captured over days or weeks. We also present results for seven state-of-the-art compression methods to act as a baseline. Our evaluation shows that Titchy not only outperforms all seven on random access capability and for frequent transmissions but also provides great compression ratios as well as high compression and decompression speeds.
Original languageEnglish
JournalIEEE Internet of Things Journal
Volume8
Issue24
Pages (from-to)17568-17583
Number of pages26
DOIs
Publication statusPublished - 2021

Keywords

  • Channel coding
  • Cloud computing
  • Data compression
  • Decoding
  • Dictionaries
  • Entropy
  • Internet of Things
  • Servers
  • data storage systems
  • generalized data deduplication
  • sensor data.

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  • Scale-loT

    Lucani Rötter, D. E. (Participant)

    01/01/201831/12/2022

    Project: Research

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