4 IoT Architecture Layer That You Should Know

The IoT stands for the Internet of Things which is a way to interact between various sensors and devices. There are many different IoT systems out there, each one being different. And there are different ways to look at each IoT architecture.

Though many variations exist, all IoT systems follow the same basic structure. There are in total of four main IoT architectures.

The Different Elements of IoT Architectures

IoT networks comprise many different cloud computing uses and parts. However, most of these IoT solutions are founded on core ideas, and these IoT Elements form the foundation of almost all IoT systems today. They are divided into different architecture levels to enhance the entire IoT network.

Devices

We can use sensors in the IoT devices to collect the data, and after that we can transfer it to the server. In IoT devices, it’s not necessary to connect the sensors with devices. The data collection and transfer process can also be done if the sensors are remotely positioned. Some examples of IoT devices −

• Smart fire alarms

• Fitness trackers

• Smart security systems

Actuators

Devices called actuators to create motions to carry out pre-programmed activities, such as −

• The turning on or off of smart lighting

• Smart locks that open or close and thermostats that raise or lower the temperature

Gateways

Within a network, gateways act as points of entry and departure. They enable the transfer of data back and forth between devices and networks. Instances of gateways include Internet Protocols (IP).

Cloud Gateways

Cloud gateways are the solutions for IoT communication between the device and to cloud.

Data Lake

Images, videos, and audio produced by IoT devices are stored in a data lake, a location for storing data that will later be filtered and cleaned before being delivered to a data warehouse for future usage.

Data Storage

We need to transfer the data using IOT devices from the data lake to the data warehouse to gain valuable insight. In a data warehouse, information that will be used in the future is cleansed, filtered, and primarily structured.

Data Analytics

Data analytics is the process of examining the data to find some valuable insight from the data collection. And those results help to make some data-driven decisions in IoT devices.

Control Application

Control programs serve as a conduit through which pertinent commands and notifications that enable actuator operation can be sent. Using soil sensors to detect dryness in the lawns and actuators to activate the sprinklers to begin irrigation are two examples of control applications.

User Applications

IoT systems' software components (such as smartphone apps) let users manage how the IoT network operates. Using user applications enables users to access additional functionality, turn the device on or off, and send commands.

Machine Learning

We need more accurate and precise models in IoT devices, which can only be achieved by Machine learning. Using the data from data warehouse ML models gives some precise future outcomes which might come in handy. New models are adopted after being evaluated and given the thumbs up by data analysts for their applicability and effectiveness.

IoT Architecture Layers

1. Sensor Layer

The Sensor layer contains sensors and actuators, including some other devices. The sensors and actuators process the received data and transmit it over the network. The physical characteristics in the real world are captured by sensors that are part of the IoT architecture's fundamental level. Temperature, smoke, air, moisture, and other factors are examples of parameters.

2. Transport/Connectivity Layer

The Connectivity layer is the second layer between the physical devices and IoT architecture. The purpose of this layer is to transport the data from devices to the internet using gateways or edge devices. The communication could either be done using direct TCP or UDP communications, or it could be done using gateways which can work as a link between LAN and WAN.

3. Processing Layer

IoT systems are made to gather, store, and process data for this layer's higher requirements. There are two main phases in the processing layer.

Data Accumulation

Millions of data streams are sent across the IoT network by every device. Different formats, rates, and sizes of data are present here. Professionals must emphasize separating the crucial data from these massive streams as their top priority in this tier. Unstructured data in its raw forms, including photo and video streams, can be pretty large; therefore, gathering intelligence for the business requires efficiency. Professionals must have a solid understanding of corporate practices to accurately identify data requirements and certain future advantages.

Data Abstraction

After all the data is collected, some vast data is extracted to get valuable insights that can benefit businesses. The data abstraction can be done as follows −

• Collecting all information from IoT systems as well as from the non-IoT systems

• Making data available from a single location by use of data virtualization

• Managing various forms of raw data

In the processor layer, device and architectural interoperability are critical. Once the data has been accumulated and abstracted, it is simple for data analysts to use their business acumen to find intelligence elements.

4. Application Layer

This layer is located in the cloud and generates reports via web interfaces and email delivery for end users' applications.

Each IoT system has specific aims and objectives to meet business requirements. Currently, most IoT applications operate on various technology stacks and at varying levels of sophistication, carrying out particular duties for organizations.

Conclusion

The IoT architecture may differ depending on the solution. But generally speaking, these four building blocks are present. IoT solutions have enabled companies to exceed their clients by getting more value from the data and serving them accordingly. It's crucial to avoid becoming baffled by the IoT's technical lingo and to keep an eye on the countless opportunities and developments that could lead to complete automation.