As Internet of Things (IoT) adoption sweeps across multiple
industries, a new interest in Artificial Intelligence (AI) has emerged to
complement IoT capabilities.
We're firmly in the midst of Industry 4.0, the fourth
industrial revolution and next step in innovation. This monumental shift relies
on the collaboration between IT systems, people, and digitally controlled
production machines to reach a new level“ and it's all driven by data.
Though IoT and AI are independent technologies with separate
use cases that are revolutionary in their own right, the combination of the two
“ AioT “ can bridge the gap to unleash their true capabilities.
What Is AIoT?
IoT devices have been a boon for industry, allowing
businesses and users to leverage the internet to communicate, store, and
exchange information. We have an unprecedented volume of data, and now IoT is a
viable solution to collect and process it.
In just one day, IoT devices can gather one billion GB of
data, and by 2025, it's expected that over 42 billion IoT devices will be in
use all over the world.
Unfortunately, that data is all but useless if it can't be
processed and analyzed for actionable insights “ which is where AI comes in.
AI's strength is in its ability to analyze data and discern patterns and
anomalies much more efficiently than humans, ensuring that decisions based on
that data can happen quickly.
AIoT relies on three key technologies:
- AI, which offers programmable functions and systems to
support learning, reasoning, and processing that mimics human cognitive
processes.
- 5G networks and their high-speed, low-latency
communications for real-time data processing.
- Big data, which facilitates the collection of an
incredible wealth of data from multiple connected sources.
Together, these technologies can revolutionize the way both
users and businesses interact with the world.
Taking the Next Step with AIoT
With both AI and IoT as promising technologies seeing more
adoption, the next step is to combine the two in digital transformation.
In the history of innovation, each step involved an
evolution of existing technology to make processes more efficient. From the
assembly line to industrial machinery to digitization, industrial processes
became more and more advanced.
AI and IoT offer direct communication between machines and
human-level cognition that can make decisions using historical and real-time
data. Coupled with IoT technology, AI and machine learning allow businesses to
analyze, predict, and adjust to changing conditions to stay agile and
productive.
What Are the Core Concepts in AIoT?
IoT has five key capabilities:
- Storing data in a scalable storage system
- Collecting telemetry data from devices and sensors in a
centralized location
- Processing and analyzing data sets
- Controlling devices based on best practices
- Analyzing the insights from data for rapid decisions
AI with IoT connects the controller and the device to
identify patterns in telemetry data. Rather than gathering and transporting
data to humans “ often with delays “ to come up with decisions, AI leverages data
analytics to act independently for rapid results. AI acts as the brain or
control of the system.
Along with augmented IoT devices with smart capabilities, AI
can analyze data in batches in real time. Because this happens at the start and
end points of the IoT spectrum, it can happen much faster.
Consider a camera with an image sensor. It would send the
entire feed to humans to analyze and react to. With the help of AI, the camera
would only send the frame that contains the image, minimizing delays and data
overload.
AI is enhanced by neural networks and deep-learning models,
which can react to critical conditions in real time and avoid significant
consequences.
For example, AIoT in a manufacturing environment can
identify significant errors in the systems that can lead to accidents and
injuries. Instead of reporting this information to a human worker, AI can shut
down the system directly to prevent disaster.
What Industries Are Using AIoT?
Both AI and IoT have an abundance of use cases. Together,
they are revolutionizing several industries:
Smart Home
Smart homes are another common use case for consumers. Many
people have IoT devices in their home in the form of smart thermostats, smart lighting
systems, smart TVs, and smart appliances like refrigerators. These devices
collect data to provide a consistently better experience for their user,
whether that's a refrigerator assisting with grocery lists or a smart
thermostat adjusting the temperature based on historic patterns.
There are numerous other ways that smart home features can
enhance quality of life and optimize energy use. Lighting systems can track
wake-and-sleep cycles to optimize sleep while a smart thermostat automatically
cools to support deeper sleep.
And one of the biggest advantages is that this optimization
often translates to lower energy bills and less energy consumption. Because of
this, theglobal smart home market is expected to grow from $78,3 billion in 202 to $135.3 billion in 2025.
Smart Cities
Smart cities are similar to smart homes, but they operate on
a city-wide level. With the world population continually growing, smart cities
offer one of the most practical solutions to the challenges presented by urban
population growth.
When cities are crowded, residents face issues with
crowding, safety, air quality, and use of resources while city planners
struggle with transportation, energy efficiency, waste disposal, and more. AIoT
offers solutions to optimize the way a city operates, supporting environmental
needs, public health needs, and the quality of life for individual residents.
For example, AIoT can be used to track traffic patterns and
reroute traffic to limit congestion and reduce the risk of accidents. Smart buildings accommodate high populations while conserving energy and ensuring
comfort using smart thermostats and lighting systems.
Smart Industry
Many industries are embracing new technologies to keep up
with the competitive business world “ even the traditional ones. But as more
industries realize the benefits of efficient processes and lower risk of human error, AIoT is seeing wider use cases.
AIoT can be used to tackle a range of repetitive,
time-consuming tasks to optimize production with a lower risk of errors that
can be financially damaging. With humans relieved of these tasks, they're free
to focus on tasks only humans can do “ creativity and innovation.
There are numerous ways AIoT is being used in industrial
settings, including smart devices, manufacturing automation, security,
and more.
Automotive
AIoT has use cases in both vehicle manufacturing and
directly with vehicles in the form of semi-autonomous and autonomous driving.
Automotive manufacturing is using AIoT to assist in the production of vehicles
to make it cheaper and more reliable.
For autonomous vehicles, AIoT is a necessary component of
bringing this futuristic vision to life. Vehicles like Tesla rely on AI and
IoT, with the sensors, cameras, and other technologies, to ensure that
automated driving is safe and close to the decision-making capabilities of a
human driver.
For example, human drivers take in a lot of information
while driving, from street signs to traffic signals to nearby pedestrians. With
an autonomous vehicle, there's no room for error with delays in decision making
that could lead to an accident.
Video Surveillance
AI and IoT offer promise for video surveillance and
security. In traditional video surveillance, human operators must track
multiple video feeds to ensure security, so it's easy for suspicious activities
to be overlooked or missed. Manual systems “ and security “ rely on limited attention,
inconsistent reaction times, and subjectivity.
By combining machine learning algorithms with the feed of
cameras to analyze data in real time, AIoT can detect objects, recognize
people, and flag activities automatically for more comprehensive security.
These systems are useful in industries like gaming or retail, which are more
prone to theft and similar activities.
Another application for security is weapon detection or
intruder alerts in certain environments. In this case, deep learning models enhance
security with technologies like virtual fences. These sophisticated use cases
can offer a lot of security on their own, but they also aid security personnel
in performing their duties.
Medical Wearables
At the consumer level, IoT is commonly seen with medical
wearables and similar technologies. Many people use performance trackers and
smart watches, which leverage IoT to monitor and track habits, patterns, and
user preferences to offer insights and make personalized recommendations.
In healthcare, medical wearables have greater uses. Beyond simple fitness tracking for casual
users, medical wearables for healthcare providers can be used to track patients
who need more in-depth monitoring, such as patients with chronic illnesses like
heart disease or diabetes. Physicians can keep up with their patient's health
with targeted data, rather than in-person appointments that happen every few
months.
With AIoT, the wearable device market is expected to reach $81 billion in revenue by 2023.
Looking to the Future of AIoT
AIoT is making its way into the mainstream, offering promise
for a safer, more optimized, and more connected future. By making decisions
based on data without human intervention, AIoT can streamline processes and
ensure rapid-fire reactions in a variety of situations that were once limited
to human capabilities.
Author Bio:
Guido Voigt
Guido Voigt is the Director of Engineering, at Lantronix, a global provider of turnkey
solutions and engineering services for the internet of things (IoT). Guido's
and Lantronix's goal is to enable their customers to provide intelligent,
reliable, and secure IoT and OOBM solutions while accelerating time to market.
