IoT Architecture


IoT Architecture

There are four layers in the IoT architecture. The base layer consists of IoT devices. This includes all the components, like sensors with the ability to sense, compute, and connect other devices.

Let’s move on to the second layer, which is the IoT Gateway or the Aggregation layer. This layer significantly aggregates data from various sensors. These two layers form the definition engine and to set the rules for data aggregation.

Next layers based on the cloud. It’s called the processing engine or event processing layer. It has numerous algorithms and data processing elements that are ultimately displayed on a dashboard. This layer basically processes the data obtained from the sensor layer.

The last layer is called the application layer, or API management layer. It acts as an interface between third party applications and infrastructure. The entire landscape supported by device managers and identity, and access managers, which are useful for the security of the architecture.


IoT Reference Architecture.

Next, let’s learn about the various layers in IoT reference architecture. The device layer is the main component where there are various devices like sensors that are interconnected. Some examples are Bluetooth connected via mobile phone and Zigbee via Zigbee Gateway. The other devices include the Raspberry Pi that’s connected to ethernet via your Wi-Fi. This is directly connected to the communication layers, which are part of the second layer. The communication layer or gateway layer has REST protocols and other application-level protocols. Both layers are tightly coupled and generate an enormous amount of data.

Now, the Bus layer or Aggregation layer acts as a message broker. It forms a bridge between the data and the communication layer for the sensors. This is an important layer for three reasons. It supports an HTTP server hand or MQTT broker.

It aggregates and combines communications via Gateway and bridges and transforms data between different protocols.

The next layer is the event processing and analytics layer, which drives data and provides transformation. It provides the ability to do that processing the data is stored in the database. The client layer is used to create a Web-based engine to interact with external APIs. This can be fed into the API imagine systems. This layer helps create a dashboard and provides a view of analytics about processing.

This layer helps communicate with systems outside the network using machine communication. So we’ve seen the comprehensive IoT reference architecture with various components, rule engines, interfaces, and security systems embedded. Cross-functional architecture is possible using a device manager that provides a single platform for remote management. The device manager communicates with devices to set protocols. Device management uses device management agents and is responsible for the remote management of software.

The identity layer has capabilities of cybersecurity, including policy control and OAuth 2 Token Issuances. Other capabilities include Identity Services, XACML, PDP, and Directory of users. (ex: LDAP)


IoT Reference Frameworks

There are a lot of frameworks for IoT setup. But the most common is ISO 30141, and it provides commonly used vocabulary, reusable designs, and best practices for any developer to design an application. It also has many secure application standards that derive the maximum benefit for the organization and reduce the risks.


IoT Standardization and Design considerations.

There are a number of IoT standards and these are evolving over time. Some of the key ones are M2M. That is a machine to machine service layer that can be embedded in hardware and software to connect devices. Contiki is an open-source operating system for low cost, low power IoT microcontrollers. LiteOS is a Unix like operating system for wireless sensor networks. Random Pace Multiple Access(RPMA) also a proprietary standard for connecting IoT objects. The last one is Sigfox, a proprietary low power, low throughput technology for IoT, and M2M communications.


IoT Interoperability challenges

IoT maturity comes with several challenges. Typically pertaining to interoperability and interfacing. The reasons are the coexistence of multifarious systems, devices, sensors, equipment, etc. That interchange location, time-dependent information in very data formats, languages, data models, constructs, data quality, and complex interrelationships. Multi-version systems designed by manufacturers over time for varied application domains, making formulation of global agreements and commonly accepted specifications. New things to get introduced and that support new unanticipated structures and protocols.

Existence of low power devices, which need to exchange data over lossy networks and may have minimal likelihood or accessibility for power recharge in months or years.


IoT Design considerations

When you choose an IoT solution, you need to consider several factors like its wireless capability, functionality, interoperability, secure storage, immediate boot capacity, device categorization, bandwidth, cryptographic control, and power management. The design considerations should be a mix of the estimated average of all these components and indexed to balance the user requirements.

You also need to set up a dispute resolution mechanism in case of failure in the long run.


IoT Devises architecture: Network and Cloud

There are four stages of integrating the different IoT processes.

  1. Network things – wireless sensors and actuators.
  2. Sensor data aggregation systems and Analog to Digital data Conversion(ADC).
  3. Edge Systems (analytics, pro-processing).
  4. Analysis, management, and storage of data.

As these stages are evolving, the devices, the network, and the cloud application must be leveled equally in the ecosystem for better stability and security.

IoT architecture is a combination of things devices, platforms,  and sensors with data.

Stage one of an IoT architecture consists of networked things. Typically wireless sensors and extra waiters.

Stage two has Internet gateways and data acquisition systems. That includes sensor data aggregation systems and analog to digital conversion.

In stage three, edge IoT systems perform pre-processing of the data before it moves to the data center or cloud.

Finally, in stage four, the data center and the cloud are where the data is analyzed, managed, and stored on traditional backend data center systems.

Fundamentally, we need to have a functional, scalable, available, and maintainable architecture. If these are not supported, then architecture is not useful. Now let’s look at the three areas of IoT architecture.

  1. Client-side – IoT device layer
  2. Operators on the server-side – IoT Gateway Layer
  3. A pathway for connecting clients and operators – IoT platform layer.

These three layers interface with each other on a data synchronization front, and the path would generate more data from various applications.

The feasibility of the layers depends on their application plus down differentiate between centralized and decentralized IoT architectures.

The centralized architecture is a hub and is managed from one point, whereas the decentralized one is based on the use case. They do not help in the industrial Iot solution. The centralized architecture is associated with cloud architectures in which a central hub provides a series of backend services to smart devices in a decentralized architecture. There are many scenarios that require autonomous communication between smart devices and an IoT topology without the need for a central hub.

The centralized systems help an event processing and whereas decentralized systems operate more of peer to peer messaging. The decentralized auditing is one of the essential features in the decentralized architecture

Use case: IoT Smart farming with IoT design

This use case addresses the design formalities using IoT how to design is an essential part of the navigational ecosystem. Smart farming requires precise architecture and components that help bring benefits to the farmers. The various factors that determine how to design our data, cost efficiency, and product quality.

The efficiency and durability of the ecosystem helps the farmers plan their harvest. And so based on the climatic conditions using the sensors and geospatial location data. To be precise, any farmer needs to understand the fundamental design that can help run the ecosystem. Smart farming is useful as it helps the farmer to predict conditions and crops with less cost and utilized automation capabilities. To have such systems need to purchase the right hardware, which can moderate the accuracy of data and quality of the sensor.

Once you have these systems, you can benefit more from farming.

There are four things you need to integrate into smart farming.

  1. Data engine – Smart farming should have a robust data processing engine that can act as the brain and handle data processing, storage, and lead to the efficient output.
  2. Hardware – You need to ensure that the hardware is durable and easy to maintain. Hardware is self-fixing algorithms that are even better.
  3. Mobile access – This is possible using a smartphone along with offline or online mobile applications

To enable all three processes. You need a cloud infrastructure with the edge layer. This IoT system can enable smart farming for any crops in any geographical location.

Use case: Diabetic patient monitoring

Diabetes management is a recurring concept. The patient has to check the blood sugar at regular intervals. In the traditional way. yes, to go to the physician’s lab or even a home blood glucose modern system.

IoT can help create a system with blood glucose data that will be transmitted remotely via a smartphone and a SIM embedded in it. Identifying the necessary and right hardware, it’s one of the crucial steps of creating such a system. You need to choose the sensors for your device or create a custom one that fits the glucometer. This can be a SIM card. The next step is to ensure that the quality of your sensors is good and has seamless integration with the system.

Finally, make sure that data modeling happens in real-time with high accuracy. Now, when the hardware landscape of this ecosystem is ready, you need to set the software. First, it is crucial that you have software with a software algorithm for service management. To enable this process, you need cloud infrastructure with edge and finally a smartphone to work with the devices and sensors.

So these are the basic things you should know about IoT Architecture. Say in touch for more articles.


You can also read

4 Stages of IoT architecture explained in simple words by Data Driven Investor



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