The Internet of Things (IoT) has steadily spread throughout the business sector over the past decade. The use of IoT devices and their data capabilities to optimize business operations has ushered in a new era of business and consumer technology. And now, with the development of artificial intelligence (AI) and machine learning (ML), another wave of realizing the new possibilities of IoT devices using the ‘Artificial Intelligence of Things (AIoT)’ has emerged.
Leveraging AIoT can give businesses a new competitive advantage. IoT collects data and AI analyzes it to simulate intelligent behavior and supports decision-making processes with minimal human intervention.
Why IoT Needs Artificial Intelligence (AI)
IoT enables devices to communicate with each other and act on insights. So these devices are only as useful as the data they provide. More data needs to be collected, stored, processed, and analyzed to be used properly for decision-making. But it is not easy to do this. In fact, as the adoption of IoT increases, it is difficult to efficiently process data and derive insights to be reflected in decision-making.
This is due to two reasons: the cloud and data transfer. The cloud needs to scale proportionally to the IoT scale to handle all kinds of data coming from the IoT, but this is not an easy process. In addition, the operation of transmitting data from the IoT device to the cloud has a bandwidth limitation. Regardless of the size and sophistication of a communications network, the amount of data that IoT devices collect alone will cause delays and congestion. Some IoT applications, such as autonomous vehicles, require fast, real-time decision-making. For effective and safe driving, data must be processed and made instantaneously like a human being. There shouldn’t be any limitations of lag, unstable connections, or low bandwidth.
Autonomous cars aren’t the only IoT applications that rely on rapid decision-making. Manufacturing has already converged IoT devices, and delays or delays can affect processes or cause major problems in emergencies.
For security, biometrics are frequently used to restrict or allow access to specific areas. Failure to process data quickly introduces delays that affect speed and performance, not to mention the risk of emergencies. These applications require extremely low latency and high security, so data processing must be done at the edge. Sending and receiving data to the cloud is also essential.
Benefits of AIoT
As demand and expectations change, the existing IoT alone is no longer sufficient. As data grows, challenges arise rather than opportunities. These problems limit the possibilities and insights of all kinds of data. However, intelligent devices with AI can solve these problems and help companies discover and utilize the true potential of their internal data.
For example, AI can help IoT networks and devices learn past decisions to predict future activity. It is also possible to continue to improve performance and decision-making capabilities. AI basically makes devices “think for themselves”. Interpret data and make real-time decisions without the delays and congestion associated with data transmission. Ultimately, AIoT offers a wide range of benefits to businesses and offers a powerful solution to intelligent automation. Here are some key examples:
- Avoid Downtime : Some industries, such as the offshore oil/gas industry, are very sensitive to equipment downtime issues. Unplanned equipment failures and downtime can lead to significant costs. AIoT can help predict equipment failures and schedule maintenance before serious problems occur.
- Improving work efficiency : AI processes the bulk of data coming into the IoT and finds patterns much more efficiently than humans can. AI and ML can augment this capability while predicting the operating conditions and changes needed to improve performance.
- Support for new and improved products and services : Natural language processing continues to advance and allows machines and humans to communicate more effectively. AIoT enables advanced data processing and analytics to improve new or existing products and services.
- Improving risk management: Risk management is essential to responding to a rapidly changing market environment. By combining AI and IoT, data can be used to predict risks and respond ideally. As a result, employee safety is improved, cyber threats are mitigated and economic losses are minimized.
AIoT use cases in key industries
AIoT is already transforming several industries, such as manufacturing, automotive and retail, for example. Common applications of AIoT include:
Manufacturing: Manufacturers use IoT for equipment monitoring. Going one step further, AIoT combines data insights from IoT devices with AI to provide predictive analytics. AIoT enables manufacturers to be proactive in warehouse inventory, maintenance and production. Additionally, in manufacturing, robotics can significantly improve operations. The robot is powered by embedded sensors for data transmission and AI. Thus, they can continuously learn from the data and reduce the time and cost of the manufacturing process.
Sales and Marketing: Retail analytics collects data points from cameras and sensors to track customer movement in physical stores and predict behavior. For example, the time it takes to reach the checkout line. These forecasts can be used to determine staffing levels or improve overall customer satisfaction by making cashiers more productive. Large retailers can use AIoT solutions to increase sales through customer insights. Data such as mobile-based user behavior and proximity sensing provide valuable insights for personalized marketing while customers shop and increase customer turnover in stores.
car :AIoT can have various applications in the automotive industry, for example in maintenance and recall. AIoT can predict which parts will fail or have faulty parts, gather data from recall, warranty and safety agencies to determine which parts need to be replaced, and then notify customers of service inspections. Vehicles are more reliable and manufacturers can gain customer trust and loyalty.
One of the most famous and most exciting applications of AIoT is autonomous vehicles. Together with IoT-enabled AI, autonomous vehicles can predict the behavior of drivers and pedestrians in numerous situations, making driving safer and more efficient.
Health care: One of the universal goals of health care is to extend services to society as a whole. However, regardless of the size and sophistication of the health care system, doctors’ administrative tasks are increasing and the time they spend with patients is decreasing. The difficulties in developing high-quality medical care while undertaking administrative burdens are considerable. Today’s medical facilities generate huge amounts of data and record a lot of patient information. For example, imaging, test results, etc. This information is valuable and essential to providing quality patient care services. However, the medical facility must be able to quickly access it, diagnose it, and decide whether to treat it or not.
IoT combined with AI is a great help in solving these problems. Examples include improving diagnostic accuracy, supporting telemedicine and remote patient care, and easing the administrative burden of tracking the health of patients within health care facilities. Perhaps the most important benefit is that AIoT can identify emergency patients more quickly than humans while processing patient information. In this way, it is possible to quickly provide necessary services to urgent patients.
Prepare for the future with AIoT
AI and IoT are in many ways the perfect combination. AI powers IoT through intelligent decision-making, and IoT improves AI capabilities through data exchange. Combining the two can innovate and deliver new experiences across numerous industries. We can expect the emergence of completely new market opportunities.