TL;DR: Insights, summarized:
What originally started as machine-to-machine communication limited almost exclusively to the telecom industry, the Internet of Things is now everywhere. According to Statista, the number of devices connected to the internet will exceed 38 billion by 2025.
The figure is arguable though, for it is hard to draw the line as to what exactly an IoT device is. So, other reports suggest more restrained numbers. Think: around 16 billion devices in use by 2025.
The steep surge in the number of IoT devices will inevitably lead to an increase in the amounts of data collected. IDC reports that the volumes of IoT data generated globally will reach 73 Zettabytes by 2025. And that’s where it gets problematic. The collected information needs to be processed and analyzed to drive value. However, most enterprises fail at putting data to use, with between 60% and 73% of it going unused for analytics.
The good news is that enterprises can turn more of the generated data into business insights by leveraging the combined power of artificial intelligence and the Internet of Things.
In the article, we’ve covered everything you need to know about this potent mix, often referred to as the artificial intelligence of things, or AIoT. So, if you are considering jumping on an Internet of Things development bandwagon, carry on reading.
An Artificial Intelligence of Things (AIoT) system is made up of two components: the Internet of Things (IoT) and Artificial Intelligence (AI).
In this robust combination, the role of IoT is to accumulate structured and unstructured data and enable the communication between the connected things and the user.
When amplified with AI — algorithms that can find complex interdependencies in huge amounts of data and describe, predict, as well as prescribe certain actions based on that — an IoT system gains human-like intelligence and can be applied to solve a wider variety of tasks. These could span “understanding” natural language, predicting users’ needs and adjusting a connected device’s behavior accordingly, and more.
The AIoT market is currently on the rise. Recent research estimates that it will reach $102.2 billion by 2026. And it’s perfectly clear why: AI adds value to IoT through improved decision-making, while IoT provides a platform for AI to drive value through connectivity and seamless data exchange.
AIoT systems may be implemented in two ways:
The architecture of an AIoT system will vary depending on the implementation strategy.
With the cloud-based approach, basic architecture of an AIoT solution looks like this:
With edge analytics, the collected data is processed closer to the source — whether on connected devices or on field gateways.
Edge-focused implementations do not exclude the presence of the cloud, though. Cloud-based data storage can, for example, be used for collecting metadata about the system’s performance or contextual information needed for training or retraining edge AI (think: a paradigm for crafting AI workflows that involves the cloud and the edge, the latter made of devices outside the cloud that are closer to physical things.)
Driven by a number of factors, such as the availability of new software tools, the development of simplified AI solutions, the infusion of AI into legacy systems, and advances in hardware upholding AI algorithms, the Artificial Intelligence of Things is creeping in many industries. Here’s a rundown of sectors that are already leveraging the opportunities provided by AIoT — with the most promising use cases spotlighted.
AIoT can help healthcare providers make more precise diagnostic decisions. The intelligent healthcare IoT solutions take in patient data from a variety of sources — from diagnostic equipment to wearables to electronic health records — and cross-analyze this data to assist doctors in correctly diagnosing a patient.
AI-based medical solutions are already surpassing human healthcare professionals in several diagnosis fields. Radiologists around the globe are relying on AI’s assistance for cancer screenings.
In a study published by Nature Medicine, AI outperformed six radiologists in determining if patients had lung cancer. The algorithm that was trained on 42,000 patient scans from a National Institute of Health’s clinical trial data records, detected 5% more cancer cases than its human counterparts and reduced the number of false positives by 11%. It’s worth mentioning that false positives present a particular problem in diagnosing lung cancer: JAMA Internal Medicine’s study of 2,100 patients states a false positive rate of 97,5%. Thus, AI helps address one of crucial diagnosis problems.
AIoT systems perform equally well when diagnosing breast cancer, skin diseases, and skin cancer. But the possibilities of smart, connected systems extend far beyond that.
Recent studies have shown that AI can detect rare hereditary diseases in children, genetic diseases in infants, cholesterol-raising genetic diseases, neurodegenerative diseases, and predict the cognitive decline that leads to developing Alzheimer’s disease.
Following the same principle as in diagnosing patients, AIoT systems can help develop better treatment strategies and adjust them to the patient’s needs.
Combining data from treatment protocols, patient’s history, and real-time patient information from connected equipment and wearables, smart algorithms can suggest dosage adjustments, exclude the possibility of a patient developing allergies, and avoid inappropriate or over-treatment. Some of the essential areas where AIoT is facilitating treatment span:
By monitoring patients who have been diagnosed with COVID-19 via AIoT-powered wearables that record patients’ vital body signs, doctors could offer patients due suggestions, thus providing more effective outpatient care.
Connected coagulation devices help measure the pace at which blood clots form, thus helping patients make sure the measurements are within their treatment range and reducing the number of visits to the office as the measurements can be communicated to healthcare providers remotely and in real time.
Chronic respiratory diseases (COPDs) affect around 500 million patients worldwide. To mitigate the severity of these conditions, patients have to stick to a thorough routine, and using inhalers is an essential part of it. Still, many patients fail to adhere to the recommended treatment plans. AIoT-enabled inhalers that are bound to a mobile app help avert that, recording the time, date, and location of each use. The collected data can be used to set up automatic reminders for the next usage, predict asthma attacks, and identify trigger factors.
In the US alone, 30 million people are affected by diabetes. And for them, regular glucose measurements have always been a concern. AIoT-enabled wireless implantable glucose meters alleviate those concerns by notifying patients — and doctors — of changes in patients’ glucose levels.
AIoT can transform the way hospitals are run, improving daily workflows in the following key areas:
Automated bed tracking systems powered by AIoT can help hospital workers admit emergency patients as quickly as possible by notifying them when a bed is free. The experience of early adopters, such as Mt. Sinai Medical Center in New York, proves that technology can help reduce wait times for 50% of emergency department patients.
Identifying patients in need of immediate attention is critical in providing quality care. To make the right decision, doctors need to analyze large amounts of information, while being under significant pressure. AIoT can give the medical staff a helping hand in prioritizing their efforts. Connected systems may analyze patients’ vitals and alert doctors of patients whose condition is deteriorating.
Several similar systems were tested out in intensive care units. For instance, the University of San Francisco piloted an AI solution that is able to detect early signs of sepsis, a deadly blood infection. The research results showed that patients whose treatments involved AI were 58% less likely to develop the infection; and the death rate was reduced by 12%.
With AIoT-enabled equipment tracking, hospitals can reduce the risk of losing critical medical equipment and make more informed equipment management decisions, thus tapping into $12,000 of savings per bed annually. Critical medical equipment can be tracked via RFID or GPS systems in and out of the hospital, while medical and administrative staff can use web and mobile applications to quickly locate the needed equipment.
With machinery equipped with AIoT sensors measuring a variety of parameters, including temperature, pressure, vibration, rotation speed, and more, manufacturers can get real-time insights into the health of their assets and schedule maintenance according to the actual need.
While basic analytics is often enough to detect equipment that is approaching a critical operating threshold, AI can predict anomalies in advance based on historical maintenance and repair data. As a result of predictive maintenance, according to a PwC report, manufacturers can improve equipment uptime by 9%, reduce costs by 12%, reduce safety risks by 14%, and extend the lifetime of their assets by 20%.
With an AIoT system in place, manufacturers can get regular updates about how well their assets are performing and drill down the reasons for performance changes. The majority of IoT-based asset performance management systems allow getting automated alerts whenever a piece of equipment is deviating from the set KPIs.
The AI engine, in turn, helps dig into the reasons for performance deterioration, if there is any, and identify whether the measured KPIs are reasonable to track in each individual setting. Using performance management software, manufacturers optimize equipment utilization and improve the overall equipment effectiveness.
According to Gartner, digital twins can help manufacturers gain a minimum of 10% improvement in production effectiveness. A digital copy of an asset, system, or process, an industrial, AIoT-enabled digital twin can help manufacturers gain an end-to-end visibility into the shop floor operations and help timely spot and even predict inefficiencies.
Manufacturing enterprises using digital twins state they could achieve lasting improvements, including an increase in reliability from 93% to 99.49% over two years, reducing receive maintenance by 40%, and saving $360,000 having predicted a power outage.
Industrial robots have been a part of the shop floor for a long time. With manufacturing IoT solutions getting more accessible, robots are becoming smarter and more independent. Equipped with sensors and relying on AI, industrial robotics is now capable of making well-informed production decisions on the go, thus increasing the effectiveness of manufacturing units.
AIoT can be used to alleviate traffic congestion and improve transportation quality. Taipei City, for instance, tapped into AIoT to monitor and control signaling equipment at 25 conjunctions. In this system, smart sensors and video cameras gathered real-time data on traffic and human flow and road occupancy, while AI algorithms analyzed this data and applied appropriate control logic.
The approach helped the city administration optimize traffic flow and ensure a safe and smooth driving experience.
Self-driving vehicles and advanced driver assistance systems (ADAS) are notable examples of AI algorithms interpreting and acting on real-time IoT data.
Self-driving, or autonomous cars create a map of their surroundings based on the data from a variety of sensors. Radar sensors, for example, monitor the position of nearby vehicles; video cameras detect traffic lights, road signs, other vehicles, and pedestrians; lidar sensors measure distances, detect road edges, and identify lane markings.
AI software then processes the sensor data, plots an optimal path, and sends instructions to the car’s actuators, which control acceleration, braking, and steering. Hard-coded rules, obstacle avoidance algorithms, predictive modeling, and object recognition help the software follow traffic rules and navigate obstacles.
Out of all IoT projects implemented globally, 76% fail, with 30% of them failing as early as in the Proof of Concept phase. To avoid directing investments into initiatives that are doomed to collapse, companies testing out AIoT waters should be aware of the common challenges that may hinder their AIoT implementations. The obstacles businesses encounter most often span:
If you have unanswered questions about the Artificial Intelligence of Things or are already considering embarking on an AIoT implementation journey, contact our experts.