Understanding IoT Communication and Architectures

Posted on May 4, 2025 in Technology

Request-Response Communication Details

Request-response communication is a fundamental interaction model in networked systems where a client sends a request to a server, and the server processes the request and sends back a response. This model is widely used in IoT, web services, APIs, and distributed computing.

How Request-Response Communication Works

1. Client Sends a Request
   • The client (such as a device, application, or user) sends a request to the server.
   • The request contains specific information such as the type of action needed (e.g., retrieving data, updating a value, or sending commands).
   • It is sent using communication protocols like HTTP, MQTT, CoAP, or WebSockets.
2. Server Processes the Request
   • The server receives the request and analyzes it.
   • It may need to fetch data from a database, compute a response, or perform an action (e.g., turning on an IoT device).
3. Server Sends a Response
   • The server generates a response and sends it back to the client.
   • The response usually contains:
     • Status Code (e.g., 200 OK, 404 Not Found).
4. Client Receives and Processes the Response The client interprets the response and takes necessary action (e.g., displaying data on a dashboard or retrying a request in case of an error).

Architecture of IoT Level-4

IoT Level-4 is designed for systems that require multiple monitoring nodes and intensive computational analysis. It combines local processing with cloud storage and analytics, making it suitable for large-scale IoT applications.

Key Components of IoT Level-4 Architecture

1. Devices & Resources: These are the physical IoT devices such as sensors, actuators, and smart devices that collect data from the environment. Each device has a resource component that processes and manages the collected data before sending it forward.
2. Controller Services: These services act as intermediaries between the devices and the cloud. They manage data flow, preprocessing, and local decision-making to reduce latency.
3. Observer Nodes: Observer nodes exist at both the local and cloud levels. They monitor the system, ensuring that data transmission and analysis are functioning properly.
4. REST/WebSocket Communication: The system uses REST APIs and WebSockets to facilitate real-time communication between local devices and the cloud.
5. Cloud Storage & Analytics: Data collected from multiple monitoring nodes is sent to centralized cloud storage. The analytics component (IoT Intelligence) processes the data, applying AI and machine learning algorithms for insights.
6. Applications: A cloud-based application allows users to access and control the IoT system remotely.

Working Mechanism

The monitoring nodes collect data and perform local analysis before sending it to the cloud. The cloud processes the data further and provides insights using AI-based analytics. Observer nodes subscribe to the processed data and deliver real-time notifications to applications.

Architecture of IoT Level-5

IoT Level-5 extends the capabilities of Level-4 by introducing a coordinator node that aggregates data from multiple end devices and routes it to the cloud. It is highly suitable for Wireless Sensor Networks (WSNs).

Key Components of IoT Level-5 Architecture

1. Endpoint Devices: Sensors and actuators that monitor environmental conditions and perform specific actions (e.g., soil moisture sensors in agriculture). These devices communicate with a coordinator node instead of directly connecting to the cloud.
2. Resource Layer: Handles data collection and initial processing at the device level. Ensures efficient communication between endpoint devices and the coordinator.
3. Coordinator Node: Acts as a central hub that collects data from multiple endpoint devices. Reduces direct cloud communication, optimizing bandwidth usage and energy efficiency.
4. Controller Services: Responsible for managing device interactions and data aggregation before sending it to the cloud.
5. REST/WebSocket Communication: Enables seamless data transmission from local devices to cloud-based storage and analytics.
6. Cloud Storage & Analytics: Stores large-scale IoT data and performs AI-driven analytics. Used for predictive insights and automated decision-making.
7. Observer Nodes & Applications: Observer nodes provide real-time updates to cloud-based applications. Users can access dashboard visualizations or receive alerts through web/mobile apps.

Working Mechanism

Multiple endpoint devices collect data and send it to a coordinator node. The coordinator node aggregates and preprocesses the data before sending it to the cloud. The cloud analyzes the data and provides actionable insights via observer nodes and applications.

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