Edge AI is an asset to the IoT
The IoT (internet of things) can be a powerful business asset. Still, the data generated by IoT devices have a long way to travel if businesses rely solely on cloud computing and cloud-based AI.
Additionally, as companies continue to invest in the IoT, adding sensors to legacy assets, their bandwidth load will quickly become heavy.
To help alleviate these issues, machine learning algorithms can be deployed on local servers or devices.
This is known as Edge AI.
Edge AI benefits the IoT by running machine learning algorithms on locally operated computers or embedded systems instead of remote servers. This article explores the positive impacts Edge AI has on the IoT.
What is Edge AI, and how does it relate to the IoT?
Let’s explore the concept of IoT.
In the phrase “Internet of Things”, the word “things” refers to internet-connected devices. These devices are constructed with software, sensors, and other technology, which enables them to send out and pick up data from various systems and other “things.”
In general, IoT devices complete the following four functions:
1. Capturing Data
From temperature readings to gathering information from real-time video feeds, sensors enable IoT devices to capture data from the surrounding area.
2. Sharing Data
IoT devices then transmit this data to a cloud system or another device. They can also store data locally for processing at the network’s edge.
3. Processing Data
Data is processed in the cloud or at the Edge to make a decision.
4. Acting on the Data
The data amassed from an IoT device undergoes analysis. This allows significant insights to be delivered to let users know what actions to take or what the most informed business decision might be.
IoT devices that use Edge AI allow complex AI algorithms to be run at the edge of a network rather than in a crowded cloud environment. This enables future outcomes to be predicted immediately and acted upon without the help of human intervention.
These Edge AI-enabled IoT devices gather and process data and make decisions using algorithms onboard advanced Edge AI chips.
With Edge AI, the machine learning algorithms that make this decision-making possible perform right at the Edge (at the site where data is initially generated). Often, information processing can be handled on the IoT device itself.
Microcontroller units and CPUs embedded within Edge AI-enabled IoT devices help machine learning algorithms run on small, resource-constrained devices.
Demand for Edge AI technology is growing faster than anticipated because of its high-speed processing abilities, decreased latency, real-time analytics, and much more.
Edge AI brings tremendous benefits to the IoT
When computing power is nearby or directly onboard a device, it benefits the IoT. If data generated by the IoT is expected to respond quickly, the best place for AI to run is at the network’s edge.
This is because, with Edge AI, data doesn’t need to be sent away to the cloud to have anything meaningful derived from it. Instead, an AI algorithm can work at the Edge, cutting down the time it takes data to travel while freeing up bandwidth for other important tasks.
Edge AI can process information locally and function as a form of local storage. This shows that IoT devices’ data and computing needs are well suited to the Edge.
Additionally, if a device cannot connect or access a solid network signal, it might not be able to run if it relies on cloud-based AI. On the other hand, the device would be perfectly functional with Edge AI’s ability to run in low-connectivity environments.
Besides, the additional security risks of exposing data when using cloud computing can be avoided entirely when AI is run locally. This is because data is more vulnerable to hacking when in transit, and it’s more likely that a massive centralized cloud would be the target of an attack versus a home or business that performs all of its functions locally.
For this reason, businesses can be more confident when deploying their machine learning models locally on Edge devices. Doing this neutralizes latency, reliability, and security concerns while enhancing mission-critical use cases by providing real-time feedback.
To summarize, the advantages that come with using Edge AI for the IoT include the following:
- Improved operational efficiency and quicker response times
- Better network bandwidth efficiency
- Reduced latency for speedy decision making
- Systems that can continuously operate offline if the network connection is lost
IoT use cases are improved with Edge AI
Countless IoT use cases are providing benefits across numerous industries today, some of which are augmented by Edge technologies like Edge computing and Edge AI. Chief among these are use cases that require low latency, low bandwidth usage, and local data storage.
The term IIoT, which stands for ‘industrial internet of things’, refers to IoT devices for industrial settings, like machines in a factory. In fact, let’s use factory machinery as an example.
As heavy machinery is used, a lot of pressure can be put on a piece of equipment as time goes by. Inevitably, breakdowns will happen.
In cases like this, IoT sensors can be infused with parts of the equipment that are most likely to be overused and suffer a breakdown.
Predictive maintenance can be enabled through the data taken from these sensors to predict when breakdowns will happen and cut down on overall downtime. If the IIoT sensor uses Edge AI, timely and accurate updates can be given precisely when needed.
AWS is an example of one company that has released a wide variety of cloud and Edge AI products and services that have been made especially for industrial clients.
Some of these include services that allow factory cams to identify physical machine defects via computer vision and products such as machine learning-equipped sensors that detect things like vibration data and machine temperature.
Another prime example of Edge AI-enabled IoT solutions in action revolves around autonomous vehicles.
As an autonomous vehicle drives along the road, it must immediately interpret its surroundings by gathering and processing real-time data about street signs, stop signs, traffic, and where pedestrians are located.
If an autonomous vehicle, for whatever reason, had to stop or turn quickly to avoid an accident, sending data to the cloud and back would take too long, and the results could be disastrous!
With Edge AI, powerful AI abilities are brought to the vehicle. Meaning that there’s no ‘waiting period.’
Taking Edge AI out of this equation is just an accident waiting to happen.
Edge AI makes IoT devices smarter
As of now, IoT devices operate across a wide variety of industries and different use cases without Edge AI.
However, as an ever-increasing number of devices are becoming connected and more use cases are being explored, Edge AI will be necessary to consider as the IoT develops.
Ultimately, Edge AI makes IoT devices smarter by enhancing how efficiently they can handle data and how quickly they can make crucial decisions.
About the Author
