Can you imagine a vast array of intelligent devices controlled by a single “brain”? To some extent, it is possible as the Internet of Things evolves – the network of physical objects equipped with sensors and actuators, software, and network connectivity that enable these objects to gather and transmit data and perform user tasks.
The effectiveness and applicability of such a system are directly proportional to the quality of its building blocks and how they interact, and different approaches to IoT architecture exist.
IoT architecture is made up of several IoT system building blocks that are linked together to ensure that sensor-generated device data is collected, stored, and processed in a big data warehouse and that device actuators execute commands sent via a user application.
Things
A “thing” is an object that is outfitted with sensors to collect data that will be transmitted over a network and actuators to allow things to act (for example, to switch on or off the light, to open or close a door, to increase or decrease engine rotation speed and more). This concept encompasses refrigerators, streetlights, buildings, vehicles, manufacturing machinery, rehabilitation equipment, and anything imaginable.
Gateways
Through the gateways, data flows from things to the cloud and vice versa. A gateway is a component of an IoT solution that connects things to the cloud, enables data preprocessing and filtering before moving it to the cloud (to reduce the volume of data for detailed processing and storing), and transmits control commands from the cloud to things. Things then use their actuators to carry out commands.
Cloud Gateways
Cloud gateways enable secure data transmission and data compression between field gateways and cloud IoT servers. It also ensures protocol compatibility and communicates with field gateways using different protocols depending on which protocols the gateways support.
Data Processor
A streaming data processor ensures that input data is effectively transitioned to a data lake and control applications. Data can be lost or corrupted at any time.
Data Lake
The lake of data. A data lake stores data generated by connected devices in its natural format. Big data is delivered in “batch” or “stream” form. When data from a data lake is required for meaningful insights, it is extracted and loaded into a large data warehouse.
Big Data
A massive data warehouse. Data that has been filtered and preprocessed is extracted from a data lake and sent to a big data warehouse. A large data warehouse only contains data that has been cleaned, structured, and matched (compared to a data lake which contains all sorts of data generated by sensors). In addition, data warehouses store context information about objects and sensors.
Data Analytics
Data analysts can use big data warehouse data to identify trends and gain actionable insights. When analyzed (and, in many cases, visualized in schemes, diagrams, and infographics), big data can reveal information such as device performance, inefficiencies, and ways to improve an IoT system (make it more reliable, and more customer-oriented).
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