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

TL;DR

This paper introduces an HMM-based encoding scheme for IoT sensor data that learns source dynamics online, improving communication efficiency for highly correlated sources without added sender complexity.

Contribution

It presents a novel online learning encoding method using Hidden Markov Models that outperforms traditional compression techniques for time-correlated IoT sources.

Findings

Significant performance improvements over existing methods

Effective for highly correlated time-series data

No additional complexity on sender side

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.