The term "Internet of Things (IoT)" has already confused enough people, making it difficult to see the bigger picture of IoT architecture. Understanding the broader view of how a real time monitoring system that collects data from sensors works will help demystify various IoT terminology.
Here's a look at how sensor-driven architecture can modernize and streamline a business.
Defining IoT Architecture
There are various components of IoT architecture that enable a business to collect valuable data in real time. Data insights captured from IoT sensors alone do not tell the full story of what IoT is about. In order for a business to justify an IoT investment, the company needs to understand the various layers and deployment stages of IoT architecture. Mixing them up can lead to expensive mistakes.
The architecture layers for IoT are the sensing, network, processing and application layers. First, a physical layer within the environment must be established through connected devices and objects. The Internet of Things would not exist without smart technology such as smartphones or technology that converts analog information into digital data.
Layers of IoT Architecture
Part of the confusion over IoT is that it's not just one technology or process. It's a collection of technologies and processes categorized in layers. Each software architecture layer simply encompasses different types of devices, apps and methods that can be grouped together to serve a certain purpose. Here's a deeper look at what these layers mean:
- Perception/Sensing Layer: Wireless sensors and actuators make up the perception layer. While a sensor collects and delivers data, an actuator measures changes recorded by the sensors. These sensors and actuators can be connected through Local Area Networks (LANs) and Personal Area Networks (PANs).
- Network Layer: This layer encompasses the platform that facilitates data transmission through various connected devices. It allows devices to communicate with other servers and devices. The network layer allows you to monitor how data moves throughout a software application. It includes Data Acquiring Systems (DAS) and Internet/Network gateways. A DAS is responsible for the functions of data aggregation and data conversion from analog to digital.
- Processing Layer: The analytics gathered from an IoT ecosystem are made possible due to data processing. IoT data is typically pre-processed and stored before it is delivered to a data center. Edge computing is becoming an important part of the processing layer since it involves IoT devices with data processing capabilities.
- Application Layer: When users interact with IoT software, they are operating in the application layer. This layer includes smart apps that are used to access IoT data.
Each of these software layers works together to make IoT technology possible. In order to call a system IoT-based, it needs devices that run on a network, where data processing is possible, then data can be accessed through smart apps.
When you put all the IoT layers together, it creates an architectural system that reflects how successful communication works. Sensors act as the senders of information, while data is the message. The data moves through channels such as edge networks where it's processed. End users receive the data through their smart devices. The main noise in the system that can disrupt data transmission is the latency from sending too much data to the cloud.
Planning for the IoT Architecture Model
Before investing in an IoT architecture, it's best to get a broad view of the different stages involved with deploying it. Understanding how IoT architecture is broken down into technology categories will help clarify how the components interact with each other. Here are the sequential stages of deploying sensor-based architecture:
Connected Objects and Devices
All the devices that connect with an IoT network are considered part of the first layer, which must be set up first before any other deployment. Connected objects can be anything generating information that can be converted into digital form via electronic technology. The sensors and actuators in the perception layer are among the first to be deployed when setting up an IoT system.
There are various types of IoT sensors now that can be used for different applications. Farmers use IoT throughout their properties to monitor environmental elements such as the quality of soil, air and water. The most advanced agricultural companies use drone equipment with sensors to take aerial photos of crop fields.
Many smart homes contain electronic devices equipped with IoT sensors, such as smart thermometers. Some sensors can detect proximity, such as when a pedestrian walks near home. Proximity sensors are used by physical retail stores to detect when customers come near the door or a certain section of the store layout. Other sensors can measure pressure, chemicals and gases.
Smoke sensors are found in modern smoke and fire alarm systems. While these sensors have existed for decades, they are much more reliable and effective in an IoT infrastructure. IoT-based smoke detectors have the capability of sending immediate alerts to homeowners on their smartphones when fire, gas, or smoke is detected on their property.
Once IoT objects and devices are determined, the next stage in deploying this powerful interactive system is to set up an internet gateway. A gateway is a device that converts analog data to digital data to allow data processing over the internet. Today's internet gateways collect raw data for IoT sensors before it is sent to the cloud or a nearby server. After the internet gateway converts analog information to digital form, it aggregates and protects the data.
The next features to deploy in an IoT system are analytics and security. Analytics software presents gathered data from various sources in a prioritized way to reveal system insights. Ultimately, analytics is the end product of an IoT system that allows business leaders to make quick improvements.
Data security is no longer an option, as it must be viewed as a requirement. Many early IoT systems lack the sufficient protection of a zero trust environment in which all devices connected to a network must be protected. Not only do internet gateways help protect digital assets, but they can also improve a company's performance and efficiency.
The third stage in the deployment of IoT architecture deals with the pre-processing phase and methods that enhance analytics. Edge computing is the use of network devices that have data processing capabilities, so that data does not need to be transmitted long distances. Sending big data to the cloud creates network congestion that can lead to latency and higher costs for bandwidth expansion.
So far edge computing has proven to be more efficient than cloud computing for the processing of IoT data. Edge IT systems often incorporate machine learning technology in which data analysis of patterns can be accelerated to reach quick conclusions and solutions. By using self-processing IoT devices, an edge IT system reduces strain on your core IT infrastructure.
Another advantage of edge computing is that it allows for data visualization techniques to deliver compelling system insights. In other words, it's a system that can that easily facilitates graphic displays, which are powerful tools that accelerate learning curves. Studies show people can grasp information faster from viewing an infographic or a video than from reading several paragraphs of text.
Many cloud providers operate from large data centers where processed IoT data ends up. In the cloud, the data can be evaluated further for final analysis and reporting. Through data centers and cloud service providers company data can be easily managed and made available to customers. The cloud is often used as a place where customer data can be transferred through applications.
Private clouds offer a much safer and more secure haven for confidential data than public clouds. But public clouds often allow for free storage, which is why many companies use them. Choosing a hybrid approach is sustainable because private clouds make critical data safer while public clouds help cut IT costs for storing less important big data.
The combination of the cloud and IoT is a powerful solution for any business that generates a wealth of data. As long as the bulk of remote IoT data doesn't have to travel to the cloud, the cloud can be utilized as a hub for an entire business. Making sure you keep big data under control from IoT sensors is an essential piece of the sustainability puzzle.
People and organizations can connect with each other more swiftly and seamlessly through IoT architecture. This system of data-collecting sensors helps businesses detect system problems in real time while offering immediate solutions through the use of machine learning software. A lot of IT companies are already emerging around the globe that offer services to design your business’ systems that are aligned with IoT architecture. So, this shouldn’t be a problem once you decide of doing so.