The Internet of Things (IoT) is rapidly changing various industries by improving processes and products. With the growth of IoT devices and data transmissions, enterprises are facing challenges in managing, monitoring, and securing devices. Machine learning (ML) can help generate intelligence by working with large datasets from IoT devices. ML can create accurate models that analyze and interpret the data generated by IoT devices, identify and secure devices, detect abnormal behavior, and prevent threats. ML can also authenticate devices and improve user experiences. Other IoT applications benefiting from ML include predictive maintenance, smart homes, supply chain, and energy optimization. Building ML features on IoT edge devices is possible with TensorFlow Lite.
Building Matter Smart Home Devices 101
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Discover the Power of IoT Edge Computing
IoT edge computing is a powerful technology that enables efficient data processing and analysis on IoT devices, closer to where the data is generated or used. With IoT devices proliferating across industries, edge computing has become a crucial element of IoT infrastructure. By processing data locally, edge computing reduces latency and increases network bandwidth, making operations more efficient and enabling faster data transmission. This creates optimal scenarios for implementing IoT data analytics and machine learning models. Furthermore, edge computing enables extending cloud services to remote locations, allowing for the deployment of workloads and running services on IoT devices. The design of IoT edge architectures must be interoperable and vendor-neutral, allowing for the handling and connecting of data in various stages to support real-time edge computing applications. With the right infrastructure and tools, enterprises can develop, manage, and deploy IoT device software with ease, unlocking the full potential of IoT edge computing.
Matter Smart Home Development for Connected Experiences
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Firmware Porting Overcomes Chip Shortages
Firmware porting helps to mitigate chip shortages by modifying the firmware and adapting it to a new microcontroller (MCU) in a flexible architecture
Real Time Operating Systems Overview
Real-Time Operating Systems are designed to run on small hardware such as microcontrollers (MCUs) and to build and execute the program (code) in real time.