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Conference Papers Year : 2023

DCH: A Deep Learning Approach To Universal Header Compression For The Internet of Things

Abstract

Header compression is an essential tool for the proper functioning of certain sensor networks. Their energy and throughput constraints have necessitated the adaptation of existing technologies. Thanks to compression, current or future protocols can run on Internet of Things (IoT) networks. However, this requires a human expertise to adapt to each protocol. The deep learning toolbox is now mature enough to provide the right tools to perform compression on any type of data. In this paper, we propose a new architecture, called Deep Compression Header (DCH), based on deep learning models for the compression of different header protocols. Based on a training period, DCH can perform compression and decompression directly on the IoT device. Our innovative approach allows for fine-grained understanding and compression without making prior assumptions about the traffic. A comparison with current network compression methods allows us to show that our approach provides promising results.
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Dates and versions

hal-04297973 , version 1 (21-11-2023)

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Fabien Meslet-Millet, Sandrine Mouysset, Emmanuel Chaput. DCH: A Deep Learning Approach To Universal Header Compression For The Internet of Things. International ACM Conference on Modeling Analysis and Simulation of Wireless and Mobile Systems (MSWiM 2023), ACM, Oct 2023, Montréal, Québec, Canada. pp.269-276, ⟨10.1145/3616388.3617522⟩. ⟨hal-04297973⟩
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