The Internet of Things has a remarkably wide range of use cases that can be made even wider with artificial intelligence. This article discusses how AI merges with IoT to drive value through intelligent data analytics.
The Internet of things is changing virtually all facets of daily life and business. With IoT connectivity, home users are getting more value from their appliances and fixtures. Meanwhile, organizations are automating processes through real-time IoT-enabled monitoring, translating to cost and revenue benefits.
However, IoT can offer much more when combined with other technologies. According to Visual Capitalist
, artificial intelligence is one of three key enablers of groundbreaking IoT use cases, along with Big Data and 5G networks. From basic applications like tracking fitness levels to driving efficiency in industrial processes and urban planning, the partnership between IoT and AI is accelerating the world into a smarter future.
In today’s article, we discuss how artificial intelligence integrates with IoT networks to create intelligent platforms that deliver long-standing value. We also elaborate on four practical examples of IoT and AI working together in real-life industries and applications.
The Increasing Popularity of IoT and AI
Artificial intelligence implementation has exploded in recent years. The global artificial intelligence market was valued at $35.92 billion in 2020
, registering a commendable 31.9% growth from the previous year. This growth rate is largely attributed to a ballooning demand for solutions that can improve business operations and customer experiences in increasingly competitive markets.
IoT has also enjoyed unprecedented adoption rates, with connected devices and use cases increasing exponentially across various industries. According to Mordor Intelligence
, the IoT market will be valued at $1.3 trillion by 2026, up from $761 billion in 2020. Analysts point to the advancement of wireless networks, the emergence of AI-driven analytics, reduced cost of connected devices, and cloud platform adoption as primary growth drivers.
However, although IoT and AI have experienced incredible individual growth, their potential as one unit is even more exciting. Some visionary organizations are augmenting IoT and AI to create intelligent connections that leverage data to drive improvements across business units.
A recent survey by SADA systems
places IoT and AI as the most popular technologies for modern-day businesses. Furthermore, most of the participants of IBM’s Global C-suite Study
agree that AI is critical for unleashing the full potential of IoT, and 19% are keenly focusing on exploiting the opportunities of AIoT platforms.
How Can AI unlock IoT?
At their core, all IoT solutions follow five basic steps: create, communicate, aggregate, analyze, and act. As the final step, the quality of “act” inherently depends on how well the system creates, communicates, aggregates, and analyzes data.
Artificial intelligence can play a critical role in enhancing the analytical capabilities of an IoT platform. While sensors and networks collect and transmit data, AI converts data into meaningful insights. With AI-powered analytics, an IoT solution can detect underlying patterns in the information it collects. As a result, it can gradually learn the events that precede a particular eventuality or the best responses to a given situation.
Examples of IoT and AI working together
AI-powered IoT systems can provide compelling benefits for several sectors. Below are four impressive examples of IoT and AI successfully deployed to deliver value in the field.
In smart retail, connected cameras can use facial recognition to identify customers when they enter a store and track them as they shop, collecting data like gender, product preferences, and the time spent in each aisle. This data can then be analyzed to reveal customer behavior patterns and facilitate better decision-making regarding store operations. For example, if the platform detects that most store visitors are women, it can push out advertisements or in-store specials that appeal to this demographic, driving up sales.
stores push AIoT implementation a notch higher by using computer vision and deep learning to give shoppers a user-friendly queue-free experience. Using a combination of RGB cameras, depth sensors, infrared sensors, and other devices, an Amazon Go store can identify precisely what a customer places in their cart and charge their account automatically when they walk out. Moreover, the collected data is used to define customer shopping patterns that improve the system’s accuracy over time.
2. Traffic Monitoring
Traffic congestion is among the primary problems that smart city technologies address. With connected cameras and sensors, city administrators can monitor traffic in real-time and adjust the flow automatically if a bottleneck occurs. These devices can also transmit traffic data to central hubs for AI-enabled analysis, revealing underlying trends in congestion causes. Traffic managers can then use analytics results to deploy more proactive anti-congestion measures, such as speed limit and traffic lights timing adjustments.
Alibaba’s ET City Brain
optimizes traffic control by using AIoT to detect abnormalities like slow-downs, accidents, and illegal parking and deliver automatic alerts to traffic authorities. The system can also change traffic light timings automatically to maintain efficient traffic flow.
Another area where IoT and AI intersect is in smart offices. Some companies invest in smart environmental sensors that detect conditions like temperature, lighting, humidity, and oxygen levels and trigger the appropriate corrective measures. These sensors can also detect personnel presence and adjust conditions appropriately for maximum energy efficiency.
In another use case, smart offices can control access through facial recognition. Using a combination of cameras and AI, the security system can compare real-time images against those stored in a database before granting access.
From an analytics perspective, IoT data can be processed with AI to determine attendance patterns, enabling facility managers to better plan for cleaning and maintenance activities and allocate parking spaces more effectively.
4. Fleet Management
IoT devices are widely used to monitor fleet operations and optimize fuel consumption, maintenance, and safety. For example, through connected sensors and cameras, delivery fleet managers can track vehicle location and movement and capture parameters like speed and vibrations. With this data, they can develop the best routes for quick delivery, fuel efficiency, and safety.
Information aggregated over time can also be subjected to analytics with AI to uncover underlying trends, such as the most efficient vehicle models or the most prevalent accident causes, giving fleet managers a more holistic view of operations.
The Untapped Potential of IoT and AI
Artificial intelligence and the Internet of things are tremendously impactful technologies that can achieve great things when they come together. However, despite impressive emerging solutions, AIoT still has immense potential that remains untapped.
Nevertheless, interest in AIoT is accelerating, and IoT suppliers are increasingly equipping their products with AI capabilities. Gartner estimates
that over 80% of all enterprise IoT projects will use AI by 2022. AIoT will become even more significant down the road, pushing the boundaries of data processing and system intelligence and delivering immeasurable value to the businesses that implement it.
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