Artificial Intelligence of Things (AIoT) is the next key step for IoT – transforming the process of analyzing data and turning it into action.
IoT will help with a new generation of AI enablement due to the aggregation nature of IoT. At its core, IoT is gathering massive amounts of data. And as that data is processed through the data-hungry algorithms of AI, the analytical and action parts of IoT will be greatly enhanced.
AIoT for Intelligent Data Analytics
IoT is key for collecting relevant, intelligent data and communicating it to be processed, analyzed, and made actionable. The role of AI within IoT is to streamline making sense out of all the data collected. It will open new channels for IoT Applications, as it will be incredibly efficient to analyze data coming from thousands of endpoints.
The ability for AI to analyze vast quantities of data will lead to many benefits, including increased operational efficiency, increased safety, risk mitigation, and utility automation.
The ability to analyze vast quantities of data will lead to many benefits, including:
Increase operational efficiency: The ability of artificial intelligence to predict circumstances based on trends through historical data can increase efficiency for many verticals, including fleet, assets, logistics, and manufacturing.
Boost safety: AIoT can increase safety in several ways. For example, using computer vision on a manufacturing floor to monitor employees or using virtual or augmented reality in hazardous situations. Artificial vision is leveraged in fleet management solutions to monitor driver behavior and use real-time alerts to prevent accidents, such as falling asleep behind the wheel.
Mitigate downtime: In manufacturing, unplanned downtime due to machine or equipment failure is one of the leading causes of revenue loss. With artificial intelligence analyzing data generated through IoT sensors on machine equipment, predictive maintenance can mitigate the risk of unplanned downtime and allow manufacturers to plan for machine maintenance.
Utility automation: In homes, smart buildings, and smart cities, utilities can be managed via AIoT based on trends. Not only does this create ease for consumers and citizens, but it can also increase safety, aid in traffic management, and bolster sustainability.
Convergence of 5G, Edge, and AIoT
One of the most encouraging running themes in this new era of IoT is how emerging technologies work strongly together instead of competitively. 5G has incredible speed and low latency, but in mission-critical communications – such as robotics and autonomous vehicles – the need for lower latency is further supported through edge computing.
Artificial intelligence can run more efficiently when closer to the edge rather than being sent to the cloud for computation. Automation through AI in those mission-critical communications will be utilized to the full potential when leveraging edge computing.
Cloud Is Sticking Around
Much like how 5G, the edge, and AIoT can work in support of each other, cloud computing will not be replaced by edge computing. The cloud still provides flexible, agile, and anywhere data access for organizations big and small.
The decision between cloud and edge depends on the individual Applications. Distributed computing allows organizations to pick and choose between the different options. Some Applications might pull together a hybrid cloud approach (public and private) and tie in some edge computing while also leveraging a local data center.
Building the Right Solution
The pitfall to having so many different options in computing and analytics is that it can be difficult to decide which options are optimized for your business case. That’s why working with an expert strategic partner can not only help you make the best decisions but streamline the process to bring your solution to market faster.