AtomBeam “Compaction” Software Uses AI/ML to Reduce IoT Data Footprint by 75 percent

October 17, 2019 Brandon Lewis

AtomBeam has released a data compaction software that reduces the size of data transmissions by a reported 70 to 80 percent with little to no latency. This can result in bandwidth increases of 3 to 5x and networking, cloud, and hardware cost reductions of 30 to 70 percent.

Using AI and machine learning technology, AtomBeam compacts duplicate and extraneous data common in IoT networks. Aside from the benefits mentioned above, this code reduction helps improve security (less attack surface), provided battery usage reductions of 25 percent, and has supposedly enabled cellular and LPWAN service providers to double transmission ranges.

For more information, visit AtomBeam at or at Mobile World Congress Los Angeles.

About the Author

Brandon Lewis

Brandon Lewis, Editor-in-Chief of Embedded Computing Design, is responsible for guiding the property's content strategy, editorial direction, and engineering community engagement, which includes IoT Design, Automotive Embedded Systems, the Power Page, Industrial AI & Machine Learning, and other publications. As an experienced technical journalist, editor, and reporter with an aptitude for identifying key technologies, products, and market trends in the embedded technology sector, he enjoys covering topics that range from development kits and tools to cyber security and technology business models. Brandon received a BA in English Literature from Arizona State University, where he graduated cum laude. He can be reached by email at

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