The Raspberry Pi has had an undeniable impact on increasing the accessibility of cheap computing, rapid prototyping, automation and IoT experimentation for both developers and hobbyists alike.
However as IoT applications are becoming more sophisticated, the conversation is rapidly moving from simple acquisition of low-cost hardware.
The real discussion is now about where all this data is going to live: data collected from these interconnected devices.
From Connected devices to Intelligent devices
Typical IoT applications are centered around connecting physical devices to networks and simply collecting data.
These applications could include anything from a sensor measuring the temperature, motion, energy consumption or device state and transmitting it to a centralized server.
AIoT goes one step beyond connecting devices to the network and collecting data from the sensors by integrating AI into connected devices. These devices can use the data that is collected along with its intelligent capabilities to analyze the data, find patterns and make better decisions closer to the source of the data.
This allows for application such as:
Predictive Maintenance
Smart Infrastructure
Industrial Monitoring
Asset Tracking
Environmental Monitoring
Automation
Operational Analytics in Real-time
What makes edge computing important?
Itβs not always practical for all of the sensor data collected to be transmitted to the cloud. Edge computing essentially involves carrying out processing closer to the data source. This might be for the purpose of quicker response times, to avoid reliance on networks or to deal with large amounts of device data.
While inexpensive hardware such as Raspberry Pis, Espressif ESP32 and other edge devices are part of the IoT ecosystem and can all have a range of functions within it depending on a device's capability, the essential focus needs to be on the architecture as opposed to on who produces the cheapest computer.
Hardware are just one pieces of the puzzle
As the discussion of the high prices for Raspberry Pis is sparking interest in the community, it has brought the topic of the needs forIoT development to the forefront, in that a single-board-computer doesn't simply create an IoT solution.
A useful IoT solution would involve some of the following components:
Sensors and connectivity
Data collection
Edge processing
Cloud infrastructure
Data Analytics
Security
Scalability
AI and Machine Learning requirements
For a personal home project a low-cost microcontroller might be all that is required to achieve the functionality required for that project, however larger applications may indeed require a hybrid system of edge devices coupled with an intelligent data transmission network, as well as connectivity platforms and subsequent analytics andAI.
The birth of AIoT
AIoT is essentially the convergence of AI and IoT. This convergence can help transform raw data from connected devices into more meaningful actions and information.
Think of industrial machinery continuously collecting data from sensors measuring the state of the device, instead of simply displaying the raw output, the sensors can feed directly into an AI that learns what the normal and the abnormal values look like, and inform a human when a problem is about to occur. Alternatively intelligent infrastructure can use the collected data and the derived information to make autonomous decisions.
This is where IoT transitions from simple connectivity to intelligent operations.
Where are we going from here
The trajectory for edge computing isn't about a single piece of hardware replacing all others, instead the focus is shifting on having different hardware for different workloads, i.e. Cheap microcontrollers for simple, task based devices, powerful edge computers capable of performing AI and analytics in real-time.
So the current discussion about a hardware part's expense is shifting the focus on the overall IoT architecture, rather than which specific device is least expensive:
"Which is cheaper?" is becoming "Which is the most appropriate for my problem?"
As AI and IoT along with edge computing and analytics continue to merge it is important to be aware of these distinctions and apply them to IoT development. For those wanting to learn more about emerging technologies such as AI and IoT have a look at Aperture Venture Studio.
https://apertureventurestudio.com/
The shift from affordable IoT hardware and devices to intelligent edge operations is underway and the most interesting evolution likely won't come from a single device, but the combination of multiple technologies working together.
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