Understanding The Artificial Intelligence of Things (AIoT)

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Technology has transformed the world we live in and has made significant changes in our daily lives throughout the years, making them much better and more convenient. This includes IoT, Artificial intelligence, robotics, to name a few. 

With recent advancements in modern technology, these methods are growing more sophisticated and interconnected. When the technologies become smarter, the possibilities for how we might use them expand as well.

To begin, IoT refers to the Internet of Things. This includes a network of “things” with sensors, software, and other technologies that can link to other devices over the internet and exchange data. On the other hand, Artificial intelligence helps machines learn from their experiences, adapt to new inputs, and execute human-like jobs. 

When AI and IoT are integrated, the device is generally referred to as “smart.” Devices are “smart,” meaning they can talk with one another, share information, and collaborate on tasks

The Artificial Intelligence of Things (AIoT) blends artificial intelligence (AI) technology with the Internet of Things (IoT) infrastructure to improve IoT operations, human-machine interactions, data management, and analytics. AI enables IoT devices to learn, evaluate, create insights, and make decisions based on the data acquired without the need for human intervention. AIoT and its applications help broaden the scope of IoT and its devices. It also aids in the improvement of existing processes and the development of new features.

AIoT technology generates data “learning machines” in terms of data analytics. This can be used by enterprises to automate processes in a connected workplace. All AIoT use cases and solutions rely heavily on real-time data. For instance, it can reduce road congestion by monitoring and notifying traffic flow based on real-time data.

AI is embedded in infrastructure components such as programs, chipsets, and edge computing, all connected to IoT networks through AIoT. APIs are then utilized to increase component compatibility at the device, software, and platform levels. These divisions will concentrate on improving the system and network operations as well as extracting value from data.

Developing AIoT involves the following three stages: 

  1. Connecting two devices and allowing them to be operated via remote control. 
  2. Connecting to the cloud to give AI inference automatically. 
  3. Peer-to-peer device communication is the final stage.

The main aspects of building AIoT solutions thus include data collection, training, and inferencing. 

AIoT offers many benefits, and a few of them are mentioned below: 

  1. Operational decision-making in real-time: IoT devices capture a lot of data. AIoT allows using this data for real-time decision-making.
  2. Reduced data transfer costs: AI systems present at central locations necessitates a considerable amount of data transfer from edge devices to central servers. AIOT solutions reduce data transfer by bringing analytics to edge devices.
  3. Improves risk management strategy: Predictive analytics is being used to assess an organization’s likely hazards and take preventative efforts to mitigate them. Further, connected rapid response processes can be executed to deal with the scenario efficiently and automatically.

While the notion of AIoT is still relatively new, it offers several opportunities to improve industry verticals such as enterprise, industrial, and consumer product, and service sectors. These opportunities will grow further as technology matures.

Sensor technologies are used in many office buildings to help save energy and money. In addition to helping with office security, sensors and smart cameras can be employed in the workplace. A simple IoT camera would simply send video data to a security center, where security professionals would view it. Trespassers can be detected by an AIoT camera, which will automatically activate a noise alarm to deter the trespasser and alert the security personnel. As a result, AIOT technology shifts decision-making from humans to IoT devices, resulting in labor savings and increased compliance.

Many AIoT applications are currently focused on installing cognitive computing in consumer appliances, and many are retail goods centered. Smart home technology, for example, would be classified AIoT since smart equipment learns from human contact and response. Personal preferences collected by smart home devices can improve machine learning models. AIoT devices can learn from user preferences and improve their decisions using techniques such as federated learning.

Autonomous vehicles take advantage of AIoT. To collect data about driving conditions, barriers, and other drivers’ behavior, the AIoT uses a series of radar sensors, both within the car and in roadside infrastructure beyond the vehicle, GPS, and cameras. After that, the AI system can make decisions depending on the data it has received from the sensors.

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