<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>IoT on AI VOID</title><link>https://ai-blog.noorshomelab.dev/categories/iot/</link><description>Recent content in IoT on AI VOID</description><generator>Hugo</generator><language>en</language><lastBuildDate>Mon, 26 Jan 2026 00:00:00 +0000</lastBuildDate><atom:link href="https://ai-blog.noorshomelab.dev/categories/iot/index.xml" rel="self" type="application/rss+xml"/><item><title>Compressing Time-Series Data for IoT Applications</title><link>https://ai-blog.noorshomelab.dev/openzl-mastery-2026/project-compressing-time-series-iot/</link><pubDate>Mon, 26 Jan 2026 00:00:00 +0000</pubDate><guid>https://ai-blog.noorshomelab.dev/openzl-mastery-2026/project-compressing-time-series-iot/</guid><description>&lt;h2 id="introduction-shrinking-the-iot-data-deluge"&gt;Introduction: Shrinking the IoT Data Deluge&lt;/h2&gt;
&lt;p&gt;Welcome back, intrepid data explorer! In this chapter, we&amp;rsquo;re diving into a crucial application of OpenZL: &lt;strong&gt;compressing time-series data, especially for Internet of Things (IoT) applications.&lt;/strong&gt; Imagine thousands, even millions, of sensors constantly reporting data – temperature, humidity, pressure, location. This generates an enormous volume of information, often repetitive and highly structured. Efficiently storing and transmitting this data is a monumental challenge, and that&amp;rsquo;s where OpenZL shines.&lt;/p&gt;</description></item></channel></rss>