As IoT has been around the block for a little while, an inevitable question has begun to circulate. Is IoT dead?
The answer is a definitive no, however, one thing that is changing about IoT is its role. As technology advances, connected physical systems aren’t just acting as passive observers of the physical world, we now have more ability than ever to make those devices intelligent. This new approach is being called AIoT.
What is AIoT?
AIoT stands for Artificial Intelligence of Things, and it essentially involves adding a layer of artificial intelligence and machine learning onto an established IoT system. A traditional IoT setup might involve sending data such as temperature, vibration, energy usage, equipment status, location, motion or atmospheric conditions to some form of dashboard or dashboard for monitoring.
With AIoT, however, an entire intelligence layer is layered over this IoT data, which allows systems to recognize patterns, detect anomalies, identify irregularities and offer predictive analytics or make decisions on their own.
In short: IoT means connecting and gathering data, while AIoT is about gathering data, understanding it, acting on it.
The question shifts from “What’s happening?” to “What happens next?”
This move fundamentally shifts the type of questions an IoT infrastructure can answer. A standard IoT system might provide you with information such as: "The average machine temp over last 2 hours is 78 degrees C." AIoT, however, might be able to discern: "The temperature has been steadily climbing above typical levels based on that machine’s history and usage for the past 2 hours."
This is an incredibly important distinction. The first provides basic information; the latter provides useful context. This value becomes even more obvious when you’re using thousands of IoT sensors reporting constantly.
Why AIoT needs good IoT infrastructure
There are no shortcuts around getting IoT right. AIoT doesn't negate the need for a strong foundation for connected devices, data capture, and management tools. In a sense,AIoT is completely reliant on good IoT infra--otherwise, there's simply no useful information for your AI to analyze.
The information path looks something like this: Sensors --> IoT Connectivity --> Data Collection --> Edge/Cloud Processing --> AI/ML Analysis --> Insights --> Action. As you can see, the final output relies heavily on the quality of the information that feeds into the input.
AIoT and edge computing
Another factor in the AIoT equation that is quickly developing, is edge computing. The premise here is the ability to process a significant portion of your AI data locally, at the device itself, rather than always pushing information back to the cloud for processing. This can be particularly useful in cases where rapid decision-making, real-time feedback or the minimization of network latency and data transmission costs is paramount - such as when monitoring industrial processes with a need for immediate anomaly detection and response. Edge systems are ideal when some intelligent tasks can be performed locally, with a broader, more analytical role carried out back at a centralized data center or cloud infrastructure.
AIoT use cases are exploding
There is a wide range of possible use cases for AIoT.
- Manufacturing: Analyzing machine sensor data helps optimize preventative and predictive maintenance.
- Smart Buildings: sensors on everything from usage to temperature provide patterns for the AI to improve efficiency.
- Logistics: Connected trucks, fleet data, cargo visibility information contribute to a dynamic and insightful transportation network.
- Energy: Consumption metrics from connected smart meters give a real-time view of individual patterns to more efficiently supply power and detect anomalies.
- Infrastructure: sensors within bridges, tunnels and public works that track a range of condition variables are turning proactive monitoring into predictive maintenance.
The key takeaway
At its core, AIoT is more than just slapping an AI model on top of an existing IoT infrastructure; rather, it is about weaving intelligence into the complete cycle – data, connection, intelligence, action. And before jumping into building a more sophisticated AIoT solution, ask this critical question: "Can intelligence improve the outcome of the decision being made based on this data?" If not, simply focusing on gathering better, more relevant IoT data is probably your best bet.
So, Is IoT dead? No. Instead, it's the underlying technology of the intelligent connected systems of tomorrow and beyond. IoT is still, in its current iterations, leading to breakthroughs from increased automation and more effective monitoring and to far more impactful and intelligent decision support systems in a wider array of applications. Visit at: https://apertureventurestudio.com/
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