How do IoT systems plan to collect and use data? Are sensors alone sufficient, or are there other components involved? What methods are preferred for real-time data processing?
What is IoT and how does it work?
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In the IoT (Internet of Things) system we refer to, sensors are just the first step. Yes, they measure data like temperature, humidity, speed, and convert it into digital signals, but the real magic happens here. It's not enough to just collect this data; to make it meaningful, store it, and take immediate action when necessary, the IoT ecosystem consists of multiple layers. First, edge devices (gateways, microcontrollers) that filter the raw data coming from sensors come into play. Here, a device like a Raspberry Pi, for example, can optimize the data received from sensors and send it to the cloud or a local database. This way, we filter out unnecessary noise; otherwise, if a sensor sends 1000 data points but only 10 are useful, what's the point?
For real-time data processing, two main strategies stand out. In the first, edge computing is used, especially in systems with low latency tolerance like production lines and smart cities. Here, part of the data is processed directly on the IoT device or a nearby server — for example, an alarm system. The second strategy is cloud-based stream processing. With platforms like Kafka, AWS IoT Core, or Google Cloud IoT, you can analyze continuously flowing data instantly and make predictions using machine learning models. Which one you choose depends on your system's requirements and data volume; you shape the architecture based on whether it needs to work in real-time or be analysis-focused.