IoT Architecture Levels-IoT Level 1, Level 2, Level 3, Level 4, Level 5

This article delves into the various levels of IoT architecture, detailing the elements and providing examples for IoT Level 1, Level 2, Level 3, Level 4, and IoT Level 5.

Introduction

The architecture of an IoT (Internet of Things) system is highly dependent on its specific application. To better categorize and understand these systems, different “levels” have been defined. Let’s explore these levels with their key elements and real-world examples.

To illustrate, we’ll use the example of an air conditioner where we need to monitor the temperature.

IoT Level 1

• Components: Air conditioner, temperature sensor, data collection & analysis (local), and a control & monitoring app.
• Data Handling: Data sensed by the temperature sensor is stored and analyzed locally.
• Control: Monitoring and control are primarily achieved using a mobile app or web application.
• Data Volume: The amount of data generated at this level is relatively small.
• Control Actions: All control actions are executed via the internet.
• Example: Monitoring the room temperature using a sensor, storing and analyzing the data locally. Based on this analysis, a control action (like adjusting the AC) can be triggered through a mobile app, or the app can simply display the current status.

IoT Level 2

• Components: Air conditioner, temperature sensor, Big Data (larger than Level 1), cloud storage, and a control & monitoring app.
• Complexity: More complex than Level 1, with a faster sensing rate.
• Data Volume: This level deals with a significantly larger volume of data.
• Storage: Cloud storage is used due to the increased data volume.
• Analysis: Data analysis is performed locally. The cloud is used for storage purposes only.
• Control: Control actions are triggered using a web app or mobile app based on the data analysis.
• Examples: Agriculture applications, or automated room freshening systems using odor sensors.

IoT Level 3

Image Courtesy from Book on Internet of Things by Shriram, Abhishek and Sundaram

• Components: Air conditioner, temperature sensor, Big Data collection (larger than Level 1), cloud (for data analysis), and a control & monitoring app.
• Data Characteristics: Deals with voluminous “Big Data.” The data sensing frequency is high, and the collected data is stored in the cloud due to its size.
• Analysis & Control: Data analysis is performed in the cloud. Based on this analysis, control actions are triggered via a mobile app or web app.
• Examples: Agriculture applications, or automated room freshening solutions based on odor sensors.

IoT Level 4

• Components: Multiple independent sensors, data collection & analysis, and a control & monitoring app.
• Sensor Setup: Employs multiple sensors that operate independently of each other.
• Data Handling: Data collected from these sensors is uploaded to the cloud separately. Cloud storage is essential due to the requirement for handling huge amounts of data.
• Analysis & Control: Data analysis is carried out in the cloud, and based on this analysis, control actions are triggered using either a web app or a mobile app.

IoT Level 5

• Components: Multiple sensors, a coordinator node, data collection & analysis, and a control & monitoring app.
• Similarity to Level 4: Similar to Level 4 in that it involves huge amounts of data sensed using multiple sensors at a high rate.
• Data Handling: Data collection and analysis are performed at the cloud level.
• Control: Based on the analysis, control actions are executed using a mobile app or a web app.

Conclusion

​The five levels of IoT architecture represent a progressive framework for designing and deploying IoT systems, each tailored to specific requirements and complexities. Starting from Level 1, which encompasses simple, localized systems, to Level 5, which involves complex networks with multiple nodes and centralized coordination, this structured approach facilitates the development of scalable and efficient IoT solutions. Understanding these levels enables system architects and developers to select and implement the most appropriate architecture based on factors such as data volume, processing needs, and deployment scale, thereby optimizing performance and resource utilization in IoT deployments.