Recent research shows that the number of IoT devices used worldwide will reach 38 billion by 2025. As this number grows, so do the volumes of data collected.
IDC estimates that by the same year, the Internet of Things will generate 73 zettabytes of information. All this data has the potential to provide valuable insights. Still, many businesses struggle to use it effectively, leaving 60% to 73% of it unused.
An AI services company with a decade of experience, ITRex is convinced: combining artificial intelligence with the Internet of Things — the combination known as AIoT — can help businesses make better use of their data.
Below, we explore the innards of AIoT systems and spotlight industries benefiting from AIoT deployments.
What is AIoT?
Artificial Intelligence of Things (AIoT) combines the Internet of Things (IoT) with Artificial Intelligence (AI).
The Internet of Things collects various types of data, both structured and unstructured. Additionally, it facilitates communication between users and connected devices.
In turn, AI analyzes large volumes of data and makes decisions based on that data.
Combined together, both technologies can be used for an even wider range of tasks, such as diagnosing patients, automating workflows, or making valuable production forecasts that were unattainable with other analytics methods.
How does AIoT work?
AIoT systems may be hosted in the cloud or run on the edge — and that influences the components of an AIoT architecture.
A standard cloud-based AIoT architecture has the following layers:
- The device layer that consists of all kinds of hardware devices (think: tags, sensors, beacons, mobile devices, health and fitness wearables, production equipment and vehicles, and others)
- The connectivity layer that includes field and cloud getaways
- The cloud layer that stores, processes, and visualizes data. It also enables other apps to access both data and insights via APIs
- The user communication layer that spans web and mobile apps allowing users to interact with AIoT.
In edge-centered systems, the data stays close to the source, that is either on connected devices or field gateways. The components of an edge AIoT architecture include:
- A collection terminal layer that connects end-point AIoT devices to gateways via power lines.
- An edge layer that enables data storage, processing, and the generation of insights.
Edge-centered implementations can still feature the cloud but it is primarily used for collecting performance or contextual data for honing AI algorithms running on the edge.
Where is AIoT used today?
Improving diagnostic accuracy
AIoT systems can collect and interpret patient data from diagnostic equipment, wearables, electronic health records, and other sources, assisting doctors in making more accurate diagnostic decisions.
One example of how artificial intelligence of Things (AIoT) improves medical diagnosis is by using AI to analyze CT scans or X-rays. AI algorithms can be trained to recognize patterns and abnormalities in these images, successfully identifying cancer and other diseases. A study published in Nature Medicine found that an AI algorithm performed better than six radiologists at detecting lung cancer, detecting 5% more cancer cases. It also allowed an 11% reduction in false positives.
Enabling predictive analytics
AIoT can be used to predict the likelihood of certain health outcomes and identify potential health risks based on patient data.
For example, AIoT may analyze EHR data to identify patients at risk of developing diabetes, hypertension, and other chronic conditions. This allows doctors to implement preventative measures and lower the chance of these conditions progressing.
AIoT can also assist doctors in tailoring treatment plans to the specific needs and characteristics of patients. For instance, an AIoT system may pinpoint patients with diabetes who are at risk of developing cardiovascular disease – and even come up with a tailored prevention plan.
Remote patient monitoring
AIoT can be used to monitor patients remotely, reducing the need for in-person visits. This can be done via devices that collect data about a patient’s vitals, for instance, smartwatches, fitness trackers, and other types of wearable sensors. The data collected spans heart rate, activity levels, sleep patterns, and other metrics. An AIoT system may, for instance, identify patients whose heart rate is consistently higher than normal, indicating a potential health issue.
Streamlining clinical workflows
AIoT can be used to automate tasks and streamline clinical workflows, improving efficiency and freeing up healthcare providers to focus on more meaningful tasks. An AIoT system might be used to analyze EHRs and identify patients who are due for preventive care, say, a flu shot or a mammogram. The AIoT solution could then send automatic reminders to these patients, reminding them to schedule an appointment and to complete the necessary care.
Facilitating drug development
AIoT can be used to identify patterns and trends in patient data, thereby speeding up the process of bringing new drugs to market. For example, AI algorithms can analyze data from clinical trials, such as patient demographics, medical history, and responses to the drug, and identify patterns and trends that may inform further drug development.
AIoT can be applied to analyze the data from equipment sensors, e.g., temperature, vibration, and other metrics and identify patterns that may indicate a piece of equipment is likely to fail. Based on the analysis, an AIoT system can alert maintenance personnel and prompt them to schedule maintenance activities before a failure occurs, reducing downtime and increasing production efficiency.
AIoT can help improve the accuracy, efficiency, and speed of the quality control process. For instance, AIoT systems can use image recognition algorithms to inspect products for defects as they move along an assembly line at high speed. This is particularly useful for identifying subtle defects that might be overlooked by human inspectors.
Another example of how AIoT helps quality control is by monitoring key product quality metrics in real time and alerting factory personnel of the values falling outside of the acceptable range. The AIoT system can also suggest practical ways to improve quality by analyzing historical defect and production data.
Supply chain optimization
The Artificial Intelligence of Things is transforming supply chains in several ways. For example, by analyzing historical sales data, weather, economic indicators, and other information, the AI component of AIoT can accurately predict demand down to an SKU. This helps manufacturers keep just enough inventory on hand.
The combination of technologies enhances supply chain visibility as well. IoT sensors are used to track the movement and condition of goods throughout the supply chain, providing a real-time look into the whereabouts and quality of products.
Finally, AIoT systems can analyze data from sensors attached to transportation vehicles to optimize routes and reduce fuel consumption. The technologies can also help schedule and dispatch vehicles more effectively.
IoT sensors can monitor energy consumption in real-time, providing a look at how energy is being used within a facility. AI algorithms can analyze this data to identify opportunities for energy savings or even take corrective actions, for instance, adjust heating, ventilation, or air conditioning based on real-time needs.
As mentioned earlier, AIoT systems can use data from IoT sensors to predict when equipment is going to fail and schedule preventive maintenance. This helps reduce energy waste due to equipment breakdowns.
AIoT systems can also be used to ease the integration of renewable energy sources into the manufacturing process. This way, they can modify energy consumption patterns to take advantage of renewable energy at the times when it’s most available.
Moreover, AIoT systems can forecast energy demand based on weather and production data, thus, optimizing energy procurement. This can help manufacturers save money by purchasing energy at times when prices are lower.
Safety and compliance
There are several ways that AIoT can be used to improve safety and compliance in manufacturing. Monitoring hazardous conditions is one of them. IoT sensors can track temperature, humidity, air quality, and other data and alert personnel if the metrics exceed safe levels.
Another example is improving emergency responses. AIoT systems can analyze data from IoT sensors to detect fires, gas leaks, and other emergency situations, alerting appropriate personnel. An AIoT system can also provide real-time data on the location of employees, helping emergency responders to react faster.
AIoT systems can also help manufacturers keep track of and comply with safety standards and environmental regulations. For example, they can monitor and report on the use of hazardous materials or track the disposal of waste products.
Automotive and transportation
AIoT systems can analyze footage from traffic cameras, GPS, weather, and other data to predict the flow of traffic and identify potential bottlenecks. The insights gained can be used to optimize routes.
AIoT is being used to optimize the routing and scheduling of public transportation, too. By analyzing data on passenger counts and traffic conditions, AI algorithms identify opportunities for enhancing public transport efficiency and reducing commute times.
In addition, IoT and AI can also detect traffic accidents and road closures and timely alert authorities to minimize disruptions.
Intelligent algorithms analyze and respond to IoT data fetched in real time to power advanced driver assistance systems and autonomous cars. These systems gather data from radar and lidar sensors, as well as built-in cameras, which are used to map the surrounding environment.
AI software processes this data, calculates the best route for the vehicle, and controls acceleration, braking, and steering via the car’s actuators. Obstacle avoidance algorithms, predictive modeling, and object recognition enable the software to avoid obstacles and follow traffic rules.
The challenges of AIoT adoption, and how to overcome them
Globally, 76% of all IoT projects do not end up a success, and 30% of them fail even before they reach the Proof of Concept stage. You must be aware of the challenges that may hinder AIoT implementations to avoid investing in initiatives doomed to failure. These challenges include:
- Diving into AIoT development with no strategic goal. Adopting innovative technologies, it is easy to get distracted by the novelty and forgo a comprehensive assessment of the solution’s feasibility. Ultimately, this may lead to uncontrolled cost creep and failing to achieve the set goals. To ensure your project is on the right track, we recommend turning to an AI or IoT consultancy and starting your initiative with a discovery phase. As part of the discovery phase, you can evaluate your idea against business goals, stakeholder expectations, and organizational capabilities.
- Going the wrong development path. As mentioned before, AIoT can be realized as an edge, cloud, or hybrid system. Consider latency, bandwidth, and speed requirements for the future solution while weighing them against resource limitations. A general rule is to deploy time-critical systems at the edge. Solutions with less stringent requirements for latency and bandwidth can be deployed in the cloud.
- Slow development cycles and uncertain costs. AIoT projects are effort-intensive and long. Depending on a specific use case, it may take several months or even years to roll out a fully functional AIoT system. And with the technology market evolving quickly, the risk of a solution becoming obsolete shortly after it’s launched is quite high. Implementation costs may spin out of control, too. To prevent that, your team should be capable of introducing changes quickly. We also recommend opting for iterative development, starting with an MVP with a set of core features. Once you get the first ROI, you can keep evolving the solution, adding new features.
- Connecting heterogeneous legacy systems. To get maximum value from AIoT systems, you may have to integrate highly heterogeneous legacy equipment to AIoT as well. This is a challenging task that requires thorough planning. The most common ways to go span: attaching sensors directly to legacy equipment, connecting older machines via gateways, or even replacing them altogether. Regardless of the method, design a feasible digitization strategy early in the project planning process.
- Failing to reach AI prediction accuracy. To generate reliable insights, AI algorithms need a lot of data. In the absence of enough data points (or if the data is on hand but cannot be used for privacy concerns), you’ll have to rely on other strategies to balance the deficit. For instance, you can go for transfer learning (think: tuning an existing neural network to solve a similar problem), augment the available data by modifying it to get new data points, or generate the needed volumes synthetically.
- Failing to address software and firmware vulnerabilities. Security of data, devices, and servers may be overlooked during project planning. Hybrid deployments can help prevent security incidents, especially when dealing with sensitive data. This reduces the risk of data being compromised during transit or in the cloud – since the data is processed closer to the source.