The Impact of AIoT on Future Industrial Innovations

The Internet of Things (IoT), which was named by Kevin Ashton in 1999 has really become a buzzword over the years. Since then, it has taken the face of solutions that we use every day from smart homes to industrial automation.

However, even though IoT is a stand-alone technology with several great use cases of its own, it does have some notable limitations – these include data management issues, security concerns, and problems with detailed analytical proficiency. All of these have contributed to the fact that for IoT to achieve its full potential there is a need for change.

Technology advancements in recent times have allowed artificial intelligence to be combined with IoT, making it even more effective. This new technology is now bringing disruption to various industries, some of which are now able to simplify their processes and create unique customer experiences while making better decisions for the business.

What are Some of the Disadvantages Associated with IoT as a Standalone Technology?

While IoT is certainly a groundbreaking technology, it also comes with its own range of drawbacks. Below are some of the limitations of traditional IoT:

Too Much Data to Process

One of the primary challenges faced by the Internet of Things (IoT) is its tendency to produce an overwhelming volume of data. With billions of interconnected devices globally, the amount of data they collectively generate is astonishing.

Although this data is very useful and informative, extracting that value is complicated. The processing and analysis necessary for massive volumes of data are incredibly time-consuming and require substantial resources. Furthermore, not all of the data collected offers equal amounts of valuable or pertinent information. This situation presents a considerable challenge for organizations as they strive to filter through this vast data pool and draw out valuable insights. As a result, this often leads to inefficiencies and the potential loss of significant opportunities.

Potential for Compliance Issues

A compliance implication of IoT is another possible drawback. IoT devices collect and forward data frequently, handling sensitive information, including personal financial details. This poses serious privacy and security questions. Poor handling of IoT can lead to breaches of data protection regulations such as General Data Protection Regulation GDPR in Europe or the California Consumer Privacy Act CCPA within the United States. This will lead to high penalties and reputational damage if not followed closely.

Poor Resource Optimization

IoT may often be innovative and impactful, but does not always optimize resources effectively. IoT devices produce a large amount of data that can often lead to excessive storage and bandwidth required to run them. This may increase costs due to malfunctioning and reduce efficiency over time.

Certain Caps on Scalability

Another possible downside of IoT is scalability. With a growing number of connected devices, the challenges involved in their management also grow. IoT systems should be capable of managing a growing number of devices and ensuring stable, secure connections at the same time. Scalability, however, becomes a big challenge if intelligent systems that can help manage these devices are not in place and businesses only rely on human operators for troubleshooting.

Not Always Affordable Long-Term

While the cost of IoT devices themselves are becoming more affordable, the long-term investment on implementing and maintaining an IoT system can be fairly expensive. These costs do not only account for the purchase and installation of the devices, but also continuous charges like data management, security improvements, and system enhancements. These costs might outweigh the benefits for many organizations, and IoT may be less appealing as an option for long-term investment.

AIoT Explained

By combining the features of AI with IoT, business owners are in a position where they can now take advantage of both technologies at once.

Differentiation Between IoT and AIoT

IoT refers to the network that includes physical devices that are interconnected or communicate with one another by exchanging data over the internet. This can be from basic appliances in the home to high-tech equipment used by commercial industries.

This initial concept evolved into AIoT, which now merges the ability of IoT devices to connect with AI’s power to make real-time improvements in processing and assist with analysis. IoT focuses on delivering the data, while AI takes this information, performs analysis to organize large amounts of information, and makes it actionable, improving the functioning of all connected devices.

The Core Principles of AIoT

The AIoT framework rests on a few key principles that ensure the smooth functioning of this technology. Here’s a look at some of these core principles:

• Data Security and Expandable Storage: AIoT systems generate vast amounts of data. To handle this, they require robust and expandable storage capacities. AIoT devices not only need to store this data securely but also retrieve it quickly for analysis and decision-making processes.
• Centralized Data Collection: AIoT systems often involve numerous devices and sensors, each generating its own data. A central repository is necessary to aggregate this data and provide a comprehensive view of the system. This centralization ensures that all data is accessible for analysis and enables more accurate decision-making.
• Deep Data Analysis: Data analysis is one of AIoT’s major strengths. AI algorithms can filter enormous amounts of data, find patterns, and make predictions, allowing devices to respond seamlessly in various situations.
• Device Monitoring and Control: AIoT systems can monitor and manage device operation. By real time data analysis, these systems can detect anomalies, pinpoint failures, and take corrective steps as soon the need arises.
• Extractable Data Insights: AIoT systems can also provide valuable insights from the data they handle. These insights can be applied successfully to enhance the decision-making process in businesses and improve innovation, which are both significant factors promoting sustainable business growth.

What Technologies Does AIoT Use?

The convergence of AI and IoT can dramatically change business solutions, leading to efficient, scalable, and highly intelligent systems. Technologies that improve the functions of AIoT include:

Artificial Intelligence (AI)

AI allows machines to mimic human intelligence and enables the hardware to learn from history, react to new information, and perform tasks that people can. Integrating AI with IoT makes it possible to analyze data, identify trends in them, and decide what should be done based on these findings. This capability allows everyday IoT devices to turn into smart systems operating autonomously and getting smarter with time.

5G Networks

5G networks are integral to the effectiveness of AIoT because 5g can transmit data at faster speeds and with more reliability. 5G networks are built to support the high-speed, low latency communication necessary for AIoT devices to communicate properly and make sure that data sharing between devices runs efficiently.

Edge Computing

Another important technology in the AIoT ecosystem is edge computing. This means processing data near its source of generation rather than forwarding it to a centralized cloud or remote data center. AIoT devices can respond in real time to changes found at the very edge of a network when analyzing data. This capability is especially useful in applications that need an immediate response, such as autonomous vehicles or industrial automation systems.

Big Data

Last but not least, Big Data plays an essential role in AIoT. IoT devices produce enormous quantities of data that can be used as raw material for AI algorithms. This data can be processed with sophisticated analytics tools that are capable of operating on large sets of data. Big data technologies, in particular, unlock valuable information from large quantities of raw, unorganized data and can transform it into actionable intel.

What Industrial Innovations Is AIoT Currently Driving?

AIoT is aiding in the transformation and development of industrial innovations across various industries. These innovations are providing companies with new opportunities to optimize their processes, improve efficiency and reduce costs.

Predictive Maintenance Capabilities

One of the major challenges industrial environments can face is equipment failures across large production lines. Unexpected breakdowns cause a dangerous ripple effect across supply chains that directly impact production runs and distribution schedules. This can have major financial consequences and can cause significant delays when getting systems back up and running.

AIoT allows for a much more proactive approach to maintenance operations. Sensors that are equipped directly into machines are able to collect real-time metrics of important equipment health parameters, including temperature, vibration anomalies, and energy consumption trends. This allows engineers to prioritize where an how they spend their resources to reduce the likelihood for equipment breakdowns.

Advanced Process Optimization

Manufacturing assembly lines and production facilities all operate using highly complex, interconnected systems and processes. However, managing and maintaining each of these manually can lead to a number of inefficiencies, including material waste and poor sustainability practices. However, having a solution in place to automate and improve these efficiencies in real time, without direct oversight from engineers or administrators, greatly reduces the likelihood of time-consuming bottlenecks.

AIoT acts like a digital nervous system on factory floors, comprehensively monitoring and optimizing how processes are being managed. Using advanced AI algorithms to simplify the collection of machine performance and material flow data, AIoT gives engineers the insights they need to avoid suboptimal resource allocations and customize their processes so that they’re able to maximize output while reducing production costs.

Quality Control and Inspection

Quality control can be an expensive, time-consuming process. However, the benefits far outweigh the risks of sending out inferior products into the market. Still, it can be very difficult for organizations to financially justify regularly using human inspectors to manage their quality control initiatives – unless regulated by industry compliance standards.

AIoT is helping to make quality control processes much more accessible to various organizations using automated processes designed to significantly improve the accuracy and consistency of facility operations. These automated systems can be calibrated to quickly identify or reject inferior products on an assembly line while providing the necessary recommendations to address production inefficiencies.

Supply Chain Intelligence

Global supply chains are a critical component of moving industries forward. There is an intricate balance when it comes to acquiring raw materials and components while manufacturing essential products and distributing them to users. But acquiring necessary resources from suppliers and coordinating production plans can be highly demanding on engineers and production planners.

With AIoT solutions in place, production facilities can achieve full end-to-end supply chain transparency. Using sensors like RFID tags, GPS trackers, and temperature control units, it’s possible to know exactly where production materials or finished products are in the pipe.

This transparency also allows for direct human oversight and can provide decision-makers with the unified view they need to make important shifts in delivery timetables or know if and where there is a problem within the distribution network.

Traffic Monitoring

AIoT has made monitoring traffic conditions across cities much more efficient. In usual traffic monitoring environments, cameras and sensors gather data on vehicle movements and this information is processed at a central location.

Intelligent systems provided by AIoT are able to gather and analyze massive data sets related to traffic in real-time, which can provide information about congestion trends, the flow of traffic, and possible incidents.

With the help of AI-powered tools, detecting and diagnosing network performance issues can occur much more quickly.

Watching the Future of AIoT

AI plays a very important role in the popularity of IoT devices and their possible applications. With devices that can identify, predict and respond to events without any human involvement, AIoT delivers a higher level of abilities as we progress toward an increasingly smarter and connected future.

Author – 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.