ROBIOTIC

Lexikon

Artificial Intelligence of Things

At a glance

Artificial Intelligence of Things (AIoT) is an approach that uses artificial intelligence to make networked devices from the Internet of Things (IoT) smarter and more capable of acting autonomously and performing increasingly complex tasks.

Internet of Things meets artificial intelligence

With its countless controllable devices as well as sensors and interfaces, IoT already offers a versatile basis for the automation of many different processes, e.g. in industry or in the control of building services engineering. In addition, IoT sensors collect a large amount of data (e.g. various measured values from a machine or from the environment). And artificial intelligence (AI) is specialized in learning from data to deliver increasingly precise results, e.g. on the optimal utilization of smart production lines or on the optimal temperature setting in the office depending on the number of people present.

Often IoT with its numerous sensors is seen as a kind of nervous system for the networked devices. Just like nerves in the human body, the sensors collect all the information that is important for the operation of the hardware. The AI assumes the role of a brain in which all information is collected and processed so that new instructions for the networked IoT components can be derived from it. It is therefore obvious to combine these two fields to realize a smart and highly automated control of IoT systems without human intervention.

Application example: optimized logistics through Artificial Intelligence of Things

Logistics is an important link between the extraction of raw materials, production of goods and transport to dealers and end users. The industry offers a lot of potential for optimization, e.g. for the implementation of a Smart Supply Chain in Industry 4.0 or more generally to make deliveries more predictable. Here, IoT and AIoT offer a wide range of options for automating warehouse and delivery processes as far as possible, making them more efficient and reliable. For example, DHL already uses trucks and warehouses in its SmarTrucking program that use technology to optimize supply chains. In the long term, the transport company hopes that the program will lead to significantly more reliable real-time tracking of deliveries, shorter transport times thanks to AI-supported optimized transport routes and improved working conditions for drivers (e.g. more pleasant or shorter journeys thanks to optimized routes).

Further application examples

  • Airports: Detailed tracking of baggage allows, among other things, for baggage not to be loaded if, for example, it is detected that the passenger will miss his or her flight. In addition, networked systems enable automated real-time information for passengers, airport staff and crew, so that many processes such as queuing can be optimized.
  • Smart Cameras: Smart cameras can, for example, learn to recognize trustworthy people and pets and share their knowledge with other cameras on the network so that the entire network becomes smarter and can make smart decisions on its own.
  • Retail: By identifying and analyzing customer behavior (and possibly comparing it to an existing customer profile), stores could, for example, display personalized offers per customer in real time.

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