Smart devices and sensors rapidly change our lives and industries, from healthcare facilities to automobile industries. However, the sheer amount of continuous data collection by the billions of smart devices and sensors that constitute the Internet of Things (IoT) can overwhelm the industries and businesses that rely on the traditional IoT architecture and how will AI transform IoT Architecture.
The solution to this problem in the meteoric rise in the world of technology today is the use of Artificial Intelligence in IoT architecture. The integration of AI in IoT builds systems that automatically gather and process data, enabling the extraction of actionable insights in real-time without any human intervention. As a result of AI-powered IoT advancements, we can now lower costs and improve productivity by using data-driven decision-making and smart automation.
Usually, when people think of the Internet of Things, they think of smart-home devices, cars with autopilot, or some other smart devices connected to the internet. However, IoT is a lot more than that. Those smart devices are a part of IoT, but IoT is mostly about data, management, communication, processing, and much more. IoT is a system of interrelated computers, machines, objects, and even people or animals assigned with unique identifiers (UIDs) capable of transferring data over a network without any human-to-computer interaction. IoT refers to all the objects connected to the internet and the communication through data transfer over the cloud.
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“Thing” in the IoT can be a person with a heart monitor implant or a vehicle with sensors to check tire pressure which is capable of transferring data over a network.
In today’s world of information technology, business organizations are increasingly using IoT to enhance customer service, improve decision-making and increase business value.
For example, commercial airlines use IoT to monitor the altitude, the coordinates, the airspeed, and the aircraft’s speed, identify any critical problems such as engine failure and then process and analyze the data transferred by the sensors to make better decisions to make flights safer.
Today, billions of devices are connected over the internet, and they produce and transfer trillions of bytes of data every day. To process, manage and analyze such a sheer volume of data, designing efficient IoT architecture is crucial.
Although IoT adoption is increasing rapidly, you must understand IoT architecture before deploying your network of smart devices or using AI in your existing IoT system.
We can often describe IoT architecture as a four-stage process that oversees data transfer from the “things” into a network and finally to a data center or the cloud for processing, analysis, and storage. IoT architecture is also responsible for sending data in the opposite direction to command an actuator to take action. For instance, in the example above of any commercial airline, the data relative to the event goes through processing and analysis after an engine failure detection. Afterward, the system transfers the data back to the actuators, which immediately triggers them to take necessary actions.
Let us look at the four stages of IoT architecture below.
Stage 1. Sensors and Actuators: Sensors and actuators are the devices that monitor or control “things.” Sensors collect data on the physical condition of the environment, such as temperature, pressure, chemical composition, distance, speed, the fluid level in a tank, etc. The data generated by sensors are converted into digital form and then transmitted to the internet gateway stage. Actuators perform actions as defined by instructions or commands sent to it through the cloud, such as adjusting the fluid flow rate, jumping over an obstacle by an industrial robot, etc. For an actuator to perform actions efficiently, very low latency between the sensor and the actuator is crucial.
Stage 2. Data Acquisition and Internet Gateways: A data acquisition system (DAS) receives the raw data from sensors. Such data goes through conversion into digital format from the natural form.
DAS then sends the processed data through an internet gateway via wireless WANs or wired WANs. Since there can be hundreds of sensors sending raw data simultaneously, this is the stage where the volume of information is at its maximum. Thus, for efficient transmission, the data generally goes through filtration and compression.
Stage 3. Edge or fog computing: After digitization and data aggregation, it still needs further processing to reduce data volume before sending it to the data center or cloud. Therefore, the edge device performs some analytics as a part of pre-processing. Usually, such processing will take place on a device close to the sensors because the edge stage is all about time-critical operations, which require analyzing the data as quickly as possible.
Stage 4. Cloud or Data Center: In this stage, robust IT systems are used to analyze, manage and safely store the data. It happens in the corporate data center or the cloud. Data from multitudes of sensors are aggregated, which provides a broader picture of the IoT system so that IT and business managers can have actionable insights. At this level, the company can use specific applications to perform in-depth analysis to determine whether particular action needs to be taken. This stage also includes the storage of data for documentation as well as for further research.
So, where does AI come into play? IoT is about sensors, actuators, and the data they transmit through internet connectivity. IoT architecture starts at the data collection stage and terminates at the stage of an “act.” Undeniably, the quality of “act” depends upon the data analysis. It is where AI plays a crucial role.
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IoT provides data. But it is AI that has the power to drive smart actions. Data sent from the sensors can be analyzed with AI, which enables businesses to make informed decisions. The use of AI in IoT allows for the following benefits:
1. Enhancing operational efficiency: AI can be used in detecting patterns which provides an insight into the redundant and time-consuming processes. As a result, it enhances the efficiency of the operations.
2. Risk management: It improves risk management by automating responses in case of events outside preset parameters. It allows for better handling of financial loss, safety, and cyber attacks.
3. Creation of new and enhanced products and services: IoT and AI can create new products and services to process and analyze data rapidly. Examples of the new services could be chatbots and smart assistants.
4. Increase IoT Scalability: IoT includes a massive array of sensors that gather a large volume of data. AI-powered IoT systems can analyze, filter, and compress data before transferring it to other devices.
Examples of integration of AI in IoT
1. Robots in manufacturing: Robots employed in manufacturing industries are implanted with sensors that enable data transmission. Those robots are further installed with AI algorithms. It saves time and cost in the manufacturing process.
2. Self-driving cars: Self-driving cars are the best example of the use of AI in IoT. AI used in these cars can predict the behavior of pedestrians in numerous situations. The use of AI also enables these cars to determine road conditions, appropriate speed of the vehicle according to the weather, traffic conditions, etc.
3. Smart cities: AI can build smart cities to analyze resource optimization, energy, water consumption, etc.
4. Healthcare: Currently, IoT is predominantly being used in healthcare systems to monitor the vitals of patients remotely. With AI, smart pill technologies, virtual/augmented reality tools can be implemented for better care of the patients.
5. Smart Thermostat solution: Nest’s smart thermostat solution is another example of AI-integrated IoT. With the integration of smartphones, the temperature can be checked and managed from anywhere without human interaction based on various variables such as work schedule and preferences of the user.
6. Financial Services: AI in IoT enables financial institutions to replace sensitive financial data with unique and secure digital identifiers.
Challenges
As with any technological system, the integration of AI in IoT is not without any challenges. Some of them are as follows:
1. Sensor issues which include security, power management, and heterogeneity of the sensors.
2. Lack of technical expertise regarding the extraction of value from data.
3. Networking issues including power consumption, lack of machine-to-machine communication, etc.
Conclusion
A business can significantly benefit from the integration of AI in IoT architecture. In addition to lowering the business’s production costs, it will improve service delivery, enhance the customer experience, and many more things. However, business owners must keep in mind that more data does not equate to improved business efficiency. Therefore, assessing the actual need is the initial requirement before installing new tools and devices or moving towards a particular IoT infrastructure, letting AI transform IoT Architecture. Then only can you make an informed decision on whether or not to enhance your business operations by connecting your devices to AI-powered IoT systems?
