IoT has seen steady adopted across the business world over the past decade. Businesses have been built or optimized using IoT devices and their data capabilities, ushering in a new era of business and consumer technology. Now the next wave is upon us as advances in AI and machine learning unleash the possibilities of IoT devices utilizing “artificial intelligence of things,” or AIoT.
Consumers, businesses, economies, and industries that adopt and invest in AIoT can leverage its power and gain competitive advantages. IoT collects the data, and AI analyzes it to simulate smart behavior and support decision-making processes with minimal human intervention.
Why IoT needs AI
IoT allows devices to communicate with each other and act on those insights. These devices are only as good as the data they provide. To be useful for decision-making, the data needs to be collected, stored, processed, and analyzed.
This creates a challenge for organizations. As IoT adoption increases, businesses are struggling to process the data efficiently and use it for real-world decision making and insights.
This is due to two problems: the cloud and data transport. The cloud can’t scale proportionately to handle all the data that comes from IoT devices, and transporting data from the IoT devices to the cloud is bandwidth-limited. No matter the size and sophistication of the communications network, the sheer volume of data collected by IoT devices leads to latency and congestion.
Several IoT applications rely on rapid, real-time decision-making such as autonomous cars. To be effective and safe, autonomous cars need to process data and make instantaneous decisions (just like a human being). They can’t be limited by latency, unreliable connectivity, and low bandwidth.
Autonomous cars are far from the only IoT applications that rely on this rapid decision making. Manufacturing already incorporates IoT devices, and delays or latency could impact the processes or limit capabilities in the event of an emergency.
In security, biometrics are often used to restrict or allow access to specific areas. Without rapid data processing, there could be delays that impact speed and performance, not to mention the risks in emergent situations. These applications require ultra-low latency and high security. Hence the processing must be done at the edge. Transferring data to the cloud and back simply isn’t viable.
Continue reading: https://www.infoworld.com/article/3663017/how-ai-is-changing-iot.html
Consumers, businesses, economies, and industries that adopt and invest in AIoT can leverage its power and gain competitive advantages. IoT collects the data, and AI analyzes it to simulate smart behavior and support decision-making processes with minimal human intervention.
Why IoT needs AI
IoT allows devices to communicate with each other and act on those insights. These devices are only as good as the data they provide. To be useful for decision-making, the data needs to be collected, stored, processed, and analyzed.
This creates a challenge for organizations. As IoT adoption increases, businesses are struggling to process the data efficiently and use it for real-world decision making and insights.
This is due to two problems: the cloud and data transport. The cloud can’t scale proportionately to handle all the data that comes from IoT devices, and transporting data from the IoT devices to the cloud is bandwidth-limited. No matter the size and sophistication of the communications network, the sheer volume of data collected by IoT devices leads to latency and congestion.
Several IoT applications rely on rapid, real-time decision-making such as autonomous cars. To be effective and safe, autonomous cars need to process data and make instantaneous decisions (just like a human being). They can’t be limited by latency, unreliable connectivity, and low bandwidth.
Autonomous cars are far from the only IoT applications that rely on this rapid decision making. Manufacturing already incorporates IoT devices, and delays or latency could impact the processes or limit capabilities in the event of an emergency.
In security, biometrics are often used to restrict or allow access to specific areas. Without rapid data processing, there could be delays that impact speed and performance, not to mention the risks in emergent situations. These applications require ultra-low latency and high security. Hence the processing must be done at the edge. Transferring data to the cloud and back simply isn’t viable.
Continue reading: https://www.infoworld.com/article/3663017/how-ai-is-changing-iot.html
