In this article, we aim to examine the concept of IoT architecture, explain the difference between IoT ecosystem and IoT architecture, demonstrate its ten different components, and finally provide a real-life example for contextualization.
What is IoT architecture?
IoT architecture comprises several IoT building blocks connected to ensure that sensor-generated data is collected, transferred, stored, and processed in order for the actuators to perform their designated tasks.
What is the difference between IoT ecosystem and IoT architecture?
IoT architecture is the breakdown of the inner workings of these building blocks to make the ecosystem function.
What are the different elements of IoT architecture?
For the sake of brevity, we will only explore the ten most important parts of an IoT architecture.
IoT devices are equipped with sensors that gather the data, which will be transferred over a network. The sensors do not necessarily need to be physically attached to the equipment. In some instances, they are remotely positioned to gather data about the closest environment to the IoT device. Some examples of IoT devices include:
- Temperature detectors
- Smoke detectors
- Cameras and CCTVs
Actuators are devices that produce motions with the aim of carrying out preprogrammed tasks, for example:
- Smart lights turning on or off
- Smart locks opening or closing
- Thermostat increasing or decreasing the temperature
Gateways serve as entry and exit points within a network. They allow for the movement of data from devices to a network and vice versa. Internet Protocols (IP) are an example of gateways.
4- Cloud gateways
Cloud gateways are a specific type of gateways, solely made for data compression and entry from field gateways to the cloud.
5- Data lake
A data lake is a data storage space that stores all sorts of structured and non-structured data such as images, videos, and audio, generated by IoT devices, which will then be filtered and cleaned to be sent to a data warehouse for further use.
6- Data warehouse
For meaningful insight, data should be extracted from the data lake to the data warehouse, either manually, or by using data warehouse automation tools. A data warehouse contains cleaned, filtered, and mostly structured information, which is all destined for further use.
To learn more about data lakes and how they are different than data warehouses, click here.
7- Data analytics
Data analytics is the practice of finding trends and patterns within a data warehouse in order to gain actionable insights and make data-driven decisions about business processes. After having been laid out and visualized, data and IoT analytics tools help identify inefficiencies and work out ways to improve the IoT ecosystem.
8- Control applications
Previously, we mentioned how actuators make “actions” happen. Control applications are a medium which, through them, it’s possible to send out the relevant commands and alerts which will make actuators function. An example of a control application could be soil sensors signaling a dryness in the lawns, and consequently, the actuators turning on the sprinkles to start irrigation.
9- User applications
They are software components (e.g. smartphone apps) of an IoT system that allow users to control the functioning of the IoT network. User applications allow the user to send commands, turn the device on or off, or access other features.
10- Machine learning
Machine learning, if available, gives the opportunity to create more precise and efficient models for control applications. ML models pick up on patterns in order to predict future outcomes, processes, and behavior by making use of historical data that’s accumulated in the data warehouse. Once the applicability and efficiency of the new models are tested and approved by data analysts, new models are adopted.
What is a real-life example IoT architecture?
The sensors take relevant data, such as daylight or people’s movement. The lamps on the other end, are equipped with actuators to switch the light on and off. The data lake stores these raw data coming from the sensors, while a data warehouse houses the inhabitants’ behavior on various days of the week, energy costs, and more. All these data, through field and cloud gateways, are transferred to computing databases (on-premise or cloud).
The users have access to the user application through an app. The app allows them to see which lights are on and off, or it gives them the ability to pass on commands to the control applications. If there is a gap in algorithms, such as when the system mistakenly switches off the lights and the user has to switch it on manually, data analytics can help address these problems at its core.
When daylights get lower than the established threshold, it’s the control applications commanding the actuators to turn the lights on. At other times, if the lights are on power-saving mode and would only be turned on if a user walks past the lawn, it’s the cloud that receives the data of a passerby walking and after identification, alerts the actuators to turn the lights on. This makes sure that false alarms are detected and the power is conserved.
But the control application does not only function with already-established commands. By leveraging machine learning, algorithms would learn more about usage patterns and customize the functionality accordingly. For example, if the inhabitants leave home at 7 am and come back at 5 pm, after some time, the lights would turn off and on in between this interval autonomously. These smart adjustments would, furthermore, reduce the need for human intervention and make for seamless continuity.
For more on the internet of things
To learn more about the technical side of internet of things, read:
- Top 10 IoT Communication Protocols
- IoT Cloud: Accessible and Scalable
- Edge Computing: A Better Alternative Than Cloud for IoT
Finally, If you believe your business will benefit from an IoT solution, feel free to check our data-driven hub of IoT solutions and tools.
And we can guide you through the process:
Cem has been the principal analyst at AIMultiple since 2017. AIMultiple informs hundreds of thousands of businesses (as per similarWeb) including 60% of Fortune 500 every month.
Cem's work has been cited by leading global publications including Business Insider, Forbes, Washington Post, global firms like Deloitte, HPE, NGOs like World Economic Forum and supranational organizations like European Commission. You can see more reputable companies and media that referenced AIMultiple.
Throughout his career, Cem served as a tech consultant, tech buyer and tech entrepreneur. He advised businesses on their enterprise software, automation, cloud, AI / ML and other technology related decisions at McKinsey & Company and Altman Solon for more than a decade. He also published a McKinsey report on digitalization.
He led technology strategy and procurement of a telco while reporting to the CEO. He has also led commercial growth of deep tech company Hypatos that reached a 7 digit annual recurring revenue and a 9 digit valuation from 0 within 2 years. Cem's work in Hypatos was covered by leading technology publications like TechCrunch and Business Insider.
Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.
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