Analyst-in-Residence, Industrial IoT and IoT Technology dives in and takes a closer at, Edge Intelligence, Device Platform Convergence and Matter to look at each trend to see how smarter devices with defragmented platform software and interoperable networks remove IoT growth barriers.
Here’s how IoT solution developers leverage these technologies to build increasingly intelligent edge applications.enable on-device applications to analyze and act on sensor data locally instead of sending it to the cloud for processing. Bigger chips also reduce device development costs and TTM by supporting advanced programming methods and tools such as cloud-native development and containerized software.
When I tell the “edge intelligence” story, developers often ask about device cost. “Sounds great, but how do I justify more expensive devices?” Although complex chips are more expensive than simple ones, the benefits of new application features, efficient development, built-in security, faster TTM, and longer product life offset the higher cost. Strong business cases make edge intelligence the new “north star” driving IoT innovation across all market segments.
IoT platform OS customization isn’t going away for the foreseeable future, but new automated build systems that simplify Linux customization are now available. These new tools do not require deep Linux expertise, are not hardware dependent, and allow continuous builds with long-term support. Two open-source build systems – Buildroot and Yocto – have been around for many years.
MCU OS defragmentation is already underway, and the leading OSes have support from hyperscale cloud frameworks and chip ecosystems. FreeRTOS , Azure RTOS/ThreadX , and Zephyr are three examples, and there are many more. I expect clear winners to emerge next year due to natural selection. Although we’ll still have many RTOSes for the foreseeable future, cloud-based configuration tools and update services for MCUs can deliver the efficiencies described above for MPUs.
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