How AI is changing IoT

IoT has seen steady adopted across the business world over the past decade. Businesses have been built or optimized using IoT devices and their data capabilities, ushering in a new era of business and consumer technology. Now the next wave is upon us as advances in AI and machine learning unleash the possibilities of IoT devices utilizing “artificial intelligence of things,” or AIoT.

Consumers, businesses, economies, and industries that adopt and invest in AIoT can leverage its power and gain competitive advantages. IoT collects the data, and AI analyzes it to simulate smart behavior and support decision-making processes with minimal human intervention.

IoT allows devices to communicate with each other and act on those insights. These devices are only as good as the data they provide. To be useful for decision-making, the data needs to be collected, stored, processed, and analyzed.

This creates a challenge for organizations. As IoT adoption increases, businesses are struggling to process the data efficiently and use it for real-world decision making and insights.

This is due to two problems: the cloud and data transport. The cloud can’t scale proportionately to handle all the data that comes from IoT devices, and transporting data from the IoT devices to the cloud is bandwidth-limited. No matter the size and sophistication of the communications network, the sheer volume of data collected by IoT devices leads to latency and congestion.

Several IoT applications rely on rapid, real-time decision-making such as autonomous cars. To be effective and safe, autonomous cars need to process data and make instantaneous decisions (just like a human being). They can’t be limited by latency, unreliable connectivity, and low bandwidth.

Autonomous cars are far from the only IoT applications that rely on this rapid decision making. Manufacturing already incorporates IoT devices, and delays or latency could impact the processes or limit capabilities in the event of an emergency.

In security, biometrics are often used to restrict or allow access to specific areas. Without rapid data processing, there could be delays that impact speed and performance, not to mention the risks in emergent situations. These applications require ultra-low latency and high security. Hence the processing must be done at the edge. Transferring data to the cloud and back simply isn’t viable. 

Every day, IoT devices generate around one billion gigabytes of data. By 2025, theprojection for IoT-connected devices globally is 42 billion. As the networks grow, the data does too.

As demands and expectations change, IoT is not enough. Data is increasing, creating more challenges than opportunities. The obstacles are limiting the insights and possibilities of all that data, but intelligent devices can change that and allow organizations to unlock the true potential of their organizational data.

With AI, IoT networks and devices can learn from past decisions, predict future activity, and continuously improve performance and decision-making capabilities. AI allows the devices to “think for themselves,” interpreting data and making real-time decisions without the delays and congestion that occur from data transfers.

AIoT has a wide range of benefits for organizations and offers a powerful solution to intelligent automation.  

Some industries are hampered by downtime, such as the offshore oil and gas industry. Unexpected equipment breakdown can cost a fortune in downtime. To prevent that, AIoT can predict equipment failures in advance and schedule maintenance before the equipment experiences severe issues.

AI processes the huge volumes of data coming into IoT devices and detects underlying patterns much more efficiently than humans can.AI with machine learning can enhance this capability by predicting the operational conditions and modifications necessary for improved outcomes.

Natural language processing is constantly improving, allowing devices and humans to communicate more effectively. AIoT can enhance new or existing products and services by allowing for better data processing and analytics.

Risk management is necessary to adapt to a rapidly changing market landscape. AI with IoT can use data to predict risks and prioritize the ideal response, improving employee safety, mitigating cyber threats, and minimizing financial losses.

AIoT is already revolutionizing many industries, including manufacturing, automotive, and retail. Here are some common applications for AIoT in different industries.

Manufacturers have been leveraging IoT for equipment monitoring. Taking it a step further, AIoT combines the data insights from IoT devices with AI capabilities to offer predictive analysis. With AIoT, manufacturers can take a proactive role with warehouse inventory, maintenance, and production.

Robotics in manufacturing can significantly improve operations. Robots are enabled with implanted sensors for data transmission and AI, so they can continually learn from data and save time and reduce costs in the manufacturing process.

Retail analytics takes data points from cameras and sensors to track customer movements and predict their behaviors in a physical store, such as the time it takes to reach the checkout line. This can be used to suggest staffing levels and make cashiers more productive, improving overall customer satisfaction.

Major retailers can use AIoT solutions to grow sales through customer insights. Data such as mobile-based user behavior and proximity detection offer valuable insights to deliver personalized marketing campaigns to customers while they shop, increasing traffic in brick-and-mortar locations.

AIoT has numerous applications in the automotive industry, including maintenance and recalls. AIoT can predict failing or defective parts, and can combine the data from recalls, warranties, and safety agencies to see which parts may need to be replaced and provide service checks to customers. Vehicles end up with a better reputation for reliability, and the manufacturer gains customer trust and loyalty.

One of the best-known, and possibly most exciting, applications for AIoT is autonomous vehicles. With AI enabling intelligence to IoT, autonomous vehicles can predict driver and pedestrian behavior in a multitude of circumstances to make driving safer and more efficient.

One of the prevailing goals of quality healthcare is extending it to all communities. Regardless of the size and sophistication of healthcare systems, physicians are under increasing time and workload pressures and spending less time with patients. The challenge to deliver high-quality healthcare against administrative burdens is intense. 

Healthcare facilities also produce vast amounts of data and record high volumes of patient information, including imaging and test results. This information is valuable and necessary to quality patient care, but only if healthcare facilities can access it quickly to inform diagnostic and treatment decisions.

IoT combined with AI has numerous benefits for these hurdles, including improving diagnostic accuracy, enabling telemedicine and remote patient care, and reducing the administrative burden of tracking patient health in the facility. And perhaps most importantly, AIoT can identify critical patients faster than humans by processing patient information, ensuring that patients are triaged effectively.

AI and IoT is the perfect marriage of capabilities. AI enhances IoT through smart decision making, and IoT facilitates AI capability through data exchange. Ultimately, the two combined will pave the way to a new era of solutions and experiences that transform businesses across numerous industries, creating new opportunities altogether. 

Xavier Dupont is the senior director of product line at Lantronix, a global provider of turn-key solutions and engineering services for the internet of things (IoT). Xavier’s and Lantronix’s goal is to enable IoT and their clients digital transformation by providing technology from sensing to data collection and visualization.

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