Hidden Markov Model-Based Encoding for Time-Correlated IoT Sources

Research output: Contribution to journalJournal articleResearchpeer-review

Abstract

As the use of Internet of Things (IoT) devices for monitoring purposes becomes ubiquitous, the efficiency of sensor communication is a major issue for the modern Internet. Channel coding is less efficient for extremely short packets, and traditional techniques that rely on source compression require extensive signaling or pre-existing knowledge of the source dynamics. In this work, we propose an encoding and decoding scheme that learns source dynamics online using a Hidden Markov Model (HMM), puncturing a short packet code to outperform existing compression-based approaches. Our approach shows significant performance improvements for sources that are highly correlated in time, with no additional complexity on the sender side.

Original languageEnglish
JournalI E E E Communications Letters
Number of pages5
ISSN1089-7798
DOIs
Publication statusE-pub ahead of print - 2021

Keywords

  • Combined source-channel coding
  • Internet of Things
  • Markov processes

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