Deploying IoT systems often presents the challenge of integrating disparate components into a unified, functional chain. From physical sensors to data visualization on a dashboard, each stage demands a well-considered approach and appropriate technological solutions. Insufficient planning or overlooking any of these steps can lead to a fragmented system that fails to provide the necessary reliability and scalability. Therefore, understanding the complete architecture is fundamental for successful IoT implementation.
Data collection: first contact with the physical world
The first and most fundamental step in any IoT architecture is collecting data directly from the physical environment. This task is performed by sensors and actuators, which convert physical parameters (temperature, humidity, pressure, light, equipment status) into electrical signals or digital data. The choice of the correct sensor depends on the specific task, operating conditions, and required accuracy. Sensors can be autonomous or integrated into larger systems, such as programmable logic controllers (PLC) or microcontrollers. A key aspect here is the reliability of data collection and its initial validation to avoid further processing of incorrect information.
Data transmission: from periphery to center
After collection, data must be transmitted for further processing and analysis. This stage involves the use of various communication protocols and networks. The choice of protocol depends on distance, data volume, power consumption requirements, and existing infrastructure. For short distances, Wi-Fi, Bluetooth/BLE, Zigbee, Z-Wave, or Matter are often used. For longer distances and low power consumption, LoRaWAN is ideal. In industrial and building applications, Modbus, BACnet, KNX, and MQTT are common. This stage also includes the use of gateways, which aggregate data from various devices and convert it into a single format for subsequent transmission to Edge or cloud platforms.
Data processing: Edge and cloud
The collected data requires processing. This can occur at two main levels: Edge computing and cloud platforms. Edge computing involves processing data as close to the source as possible—on gateways or specialized Edge devices. This reduces latency, lowers network load, ensures rapid response to local events, and enhances privacy. For example, a decision to activate a pump based on pressure sensor readings can occur at the Edge. More complex processing, long-term storage, Big Data analytics, machine learning, and the creation of digital twins are typically implemented on cloud IoT platforms. The cloud provides scalable resources for storing, processing, and integrating data with other enterprise systems (ERP, SCADA, BMS).
Automation and control: responding to events
Processed information forms the basis for automation and control. At this stage, scenarios, rules, and triggers are developed to define system behavior in response to specific events or state changes. For example, if the temperature exceeds a set threshold, the system can automatically turn on the air conditioner or send a notification to responsible personnel. These scenarios can be simple (based on threshold values) or complex (using machine learning algorithms for prediction and optimization). Control can be exercised both locally (at the Edge) and remotely via a cloud platform, ensuring flexibility and centralized control over distributed systems.
Visualization and monitoring: from data to insights
The final, but no less important, step is data visualization and system status monitoring. Effective dashboards and interfaces allow users to see key metrics in real time, track trends, receive anomaly alerts, and interact with the system. Visualization transforms raw data into understandable insights that help make informed decisions and optimize processes. These can include graphs, charts, maps, tables, and interactive controls. Monitoring also includes an alert system via SMS, email, or push notifications, ensuring timely response to critical situations.
How AZIOT implements this
The AZIOT platform by Data Management IG implements these architectural steps as a unified whole, utilizing the flexible low-code platform Unity Base. For data collection, AZIOT supports a wide range of protocols, including MQTT, Modbus, BACnet, KNX, Zigbee, Z-Wave, LoRaWAN, Wi-Fi, Bluetooth/BLE, and Matter, ensuring integration with virtually any type of device and sensor. This allows the platform to operate effectively across 12 product lines, from Home to Industry and City. Data transmission occurs via secure channels, with the option of using Edge computing for local processing and rapid response, which is critically important for industrial and critical infrastructures.
Data processing takes place on both Edge devices and in the cloud, where AZIOT leverages the power of cloud IoT platforms for scalable analytics, digital twin creation, and long-term storage. Data and access security are ensured through encryption, access control, and device authentication. Automation in AZIOT is implemented through flexible scenarios, rules, and triggers that allow for the creation of complex control logic without operator intervention. This can include automatic climate control, lighting, energy consumption monitoring, or manufacturing process management. Data monitoring and visualization are provided through customized dashboards that deliver real-time information, as well as an alert system for timely notification of important events. The platform also provides an API for seamless integration with existing SCADA, BMS, and ERP systems, which is key for enterprise clients.
When planning your IoT project, always start by clearly defining business goals and requirements, and then progressively build the architecture, considering each of these stages. This will ensure not only technical implementation but also real value for your business.