With the right application of AI and machine learning in IoT, you can transform raw data from your connected devices into intelligence you can use to cut costs, drive innovation, and improve customer experiences.
The term IoT (Internet of Things) can refer to any network of connected data sensors. Domains under the umbrella of IoT include:
- Smart home devices for home automation
- IoT Wearables like smartwatches and fitness trackers
- Smart cities for urban infrastructure
- Industrial IoT (IIoT) for industrial applications and intelligent factories
- Internet of Medical Things (IoMT) for healthcare
- AgriTech or Agricultural IoT to enhance farming practices
- Retail IoT for improving customer experience and operations in retail
Businesses often worry about the complexity and cost of merging these technologies. The complexity of data handling and integration can be roadblocks for small or less-experienced teams, but the rewards of implementing IoT and ML far outweigh the challenges. Intellias helps by providing the technical expertise in machine learning services to get you up and running quickly. We work with you to build a custom IoT solution and prepare you to reap the benefits of IoT and machine learning projects.
This guide will show you how machine learning for IoT empowers companies to streamline and innovate like never before.
Understanding machine learning and IoT
When the Internet of Things (IoT) and Machine Learning (ML) work together, your devices not only connect to the internet but also learn and adapt to your needs.
The Internet of Things (IoT) refers to the vast network of connected devices that gather and share data about their environment over the Internet. It’s a digital universe where physical objects can “talk” to each other.
Machine Learning (ML), a type of artificial intelligence (AI), empowers computers (and IoT devices) to learn from data, identify patterns, and make predictions without explicit programming. It’s a powerful data processor that effectively learns from experience and reprograms itself.
So, what makes ML and IoT a perfect pair?
IoT devices are smart sensors that capture a lot of data. For example, your fitness watch or smart ring is a complex IoT device that collects data about your daily activity. But it’s the associated machine learning capabilities that make it a smart device. AI/ML algorithms process all that data to tell you how many calories you burned during your last run or how frequently you woke up last night.
When you consider that manufacturers shipped nearly a billion wearable devices in 2023, it’s easy to see that this category creates vast amounts of data. All that data needs to get processed somehow, and traditional data processors aren’t up to the task of analyzing and making sense of all this data. Machine learning takes care of this challenge because it scales better than any other data processing technique.
This table shows at a glance how well IoT and ML technologies work together:
| Feature | Internet of Things (IoT) | Machine Learning (ML) |
|---|---|---|
| What it does | Connects devices and collects data | Analyzes data to uncover patterns |
| Data | Generates mass amounts of data for analysis | Works best when provided with large volumes of data |
| Intelligence | No inherent intelligence | Learns and improves over time |
| Applications | Smartwatches, fitness trackers, smart hearing aids, smart glasses, smart homes, connected medical devices, connected machinery | Personalization, voice detection, activity classification, natural language processing, anomaly detection |
As we can see, the common thread between machine learning and IoT is data. IoT devices generate massive amounts of data, and ML algorithms thrive on this data. Machine learning for IoT uncovers insights and patterns that would be virtually impossible for humans to see.
You can think of it this way:
- ML technology is like a scientist that gathers data (experiments), analyzes it (hypothesis testing), and draws conclusions (new theories). The AI can learn from these conclusions to make predictions about future experiments.
- A smart device is like a well-trained technician recording data in an organized and defined manner and following instructions (programming) to perform a specific task (for example, changing the temperature). An IoT device can’t independently deviate from its instructions or learn new tasks.
Some smart devices incorporate basic ML for limited purposes. For example, some smart hearing aids include a chip with AI/ML trained to detect certain sounds, and some security systems have built-in anomaly detection intelligence. The size and power of onboard intelligence models scale with the power capacity of the IoT device.
Generally speaking, though, an IoT device’s core functionality relies on pre-programmed rules. A connection to a remote machine learning system opens doors for a much wider range of intelligent applications and adaptability for different conditions.
Since IoT devices generate massive amounts of data, machine learning is a perfectly matched technology for optimizing data handling—whether it happens at the edge or on the cloud. This synergy between IoT and ML provides numerous benefits for businesses, which we’ll explore next.
The benefits of merging machine learning and IoT
Integrating IoT with machine learning unlocks significant business value. From the moment of deployment, IoT collects vast amounts of data, which ML algorithms transform into actionable insights.
But because machine learning models learn and improve, it’s not just a one-time improvement. IoT data helps machine learning models improve over time, feeding back into continuous operational efficiency. This feedback loop allows businesses to optimize operations continuously and automate more processes over time, ensuring ongoing efficiency gains. IoT and ML can:
- Cut costs: Predictive maintenance minimizes equipment downtime and prevents expensive repairs by spotting issues early. Automating routine tasks and optimizing resource use further trim operational expenses.
- Boost revenues: Create new revenue streams and improve existing ones. Real-time data and advanced analytics enable personalized products and services, increasing customer satisfaction and sales. Insights into market trends and consumer behavior allow smart, data-driven decisions that capitalize on new opportunities.
- Improve efficiency: This technology combo is ideal for automating tasks or business processes and optimizing resource use. ML algorithms analyze IoT data to find inefficiencies and suggest improvements, enhancing productivity across operations.
- Enhance customer experience: Start using insights from IoT data to personalize customer experiences. Machine learning in IoT provides insight into customer behaviors and preferences. This opens the door to targeted marketing and customized products, increasing customer satisfaction and loyalty.
- Drive innovation: Machine learning and IoT data make it easier to meet market demands and address pain points. If your business can pivot quickly, you’ll attract new customers and enhance your reputation as a technology leader.
- Continuously optimize: As IoT devices collect more data over time, ML models become more accurate and effective. This creates a virtuous cycle where better insights can lead to improved business operations, which in turn generate better data for further operational efficiency.
Wondering how? Let’s look at how it works through case studies demonstrating real-world applications of machine learning in IoT.
The algorithms at the heart of machine learning in IoT
At their core, machine learning algorithms are super-powered pattern recognizers. ML can sift through massive amounts of data collected by IoT devices, uncovering hidden trends and relationships that would be impossible to see with the naked eye. But these algorithms come in different flavors. Each type is suited for a specific kind of task:
Supervised learning
Imagine a teacher showing a student labeled examples. This is how supervised learning works: we train the algorithm on data that’s already been categorized. In the context of the Industrial Internet of Things (IIoT), categories could include “faulty machine” or “healthy machine.” By analyzing these examples, the algorithm learns to identify patterns and predict what labels are appropriate for new data it’s never seen before.
This type of algorithm is perfect for tasks like predictive maintenance, where IIoT sensor data can be used to predict equipment failures before they happen by recognizing patterns described by the systems.
Unsupervised learning
This is where things get interesting. Unlike supervised learning, where we feed the algorithm with both input data and the corresponding output labels, unsupervised learning takes a different approach. We do not provide pre-labeled data, so the algorithm isn’t explicitly told what patterns or features to look for. Instead, it uses its own internal logic to explore the data to uncover hidden structures, relationships, or anomalies.
This capability is particularly valuable in areas like anomaly detection in IoT. For instance, an unsupervised algorithm analyzing traffic patterns in a smart city might detect unusual congestion or traffic slowdowns, which could signal an accident, road blockage, or abnormal events. Since it doesn’t rely on pre-defined labels or patterns, the algorithm has the flexibility to detect novel or rare behaviors that might not be part of typical training data.
Reinforcement learning
This approach is like training a dog with treats. The algorithm interacts with its environment, receives rewards for desired actions, and learns to optimize its behavior over time. It’s a powerful tool for applications like smart thermostats. By analyzing energy usage patterns and receiving positive reinforcement for reducing consumption, the thermostat can autonomously adjust temperature settings for optimal comfort and efficiency.
These are just a few examples, and you have a whole toolbox of algorithms to choose from. But what makes machine learning so powerful in IoT is its ability to handle the sheer volume and variety of data generated by connected devices. Imagine a wind farm with hundreds of sensors collecting data on wind speed, turbine performance, and weather conditions. Machine learning can analyze this data to optimize energy production, predict maintenance needs, and even forecast weather patterns.
The applications are endless. From optimizing traffic flow in smart cities to enhancing security in smart homes, machine learning is making the Internet of Things connected and intelligent.
Supervised, unsupervised, and reinforcement learning examples
| Scenario | Best Algorithm | Explanation |
| Predictive maintenance for industrial machines | Supervised Learning (for example, Decision Trees) | Labeled data (for example, “faulty” or “healthy”) allows the algorithm to predict when a machine will fail. |
| Energy consumption prediction in smart homes | Supervised Learning (for example, Neural Networks) | Neural networks learn from historical data (energy usage patterns) to forecast future consumption and optimize energy distribution. |
| Grouping similar pedestrian behaviors in a smart city system | Unsupervised Learning (for example, k-means Clustering) | Clustering helps identify groups of individuals with similar behaviors from unlabeled data (for example, travel patterns). |
| Anomaly detection in IoT network traffic | Unsupervised Learning (for example, Principal Component Analysis) | Detects unusual patterns in network data, identifying potential security breaches or abnormal usage without predefined categories. |
| Autonomous vehicle route optimization | Reinforcement Learning (for example, Q-Learning) | The algorithm learns the best routes by receiving rewards for more efficient paths, adjusting its behavior over time. |
| Smart thermostat energy optimization | Reinforcement Learning (for example, Deep Q-Networks) | Learns and adjusts temperature settings by receiving feedback on energy savings and user comfort, continuously optimizing based on its environment. |
5 popular use cases for machine learning in IoT
As IoT devices of all kinds continue to proliferate across industries, they generate massive amounts of data. ML techniques are becoming indispensable for extracting valuable insights and optimizing operations. By harnessing the power of machine learning algorithms, businesses can use IIoT devices to:
1. Automate data analysis
Say goodbye to manual number-crunching! Machine learning empowers IIoT solutions to automatically analyze vast amounts of sensor data, identifying trends, anomalies, and opportunities for optimization. This automation streamlines the analysis process, providing valuable information for decision-making and enabling real-time responses.
For example, predictive maintenance solutions like Senseye Predictive Maintenance used by Siemens rely on ML. They analyze sensor data from industrial equipment, predict when maintenance will be required, and schedule maintenance activities accordingly, reducing downtime and costs.
2. Deliver predictive analytics
ML models can analyze historical data from IIoT devices and identify patterns that can be used to make predictions about future events or behaviors. From predicting equipment failures to forecasting consumer demand, machine learning models can analyze IoT data and anticipate future trends, allowing businesses to stay ahead of the curve.
A good example is how Duke Energy uses ML in IIoT to predict energy consumption patterns based on weather data, customer usage patterns, and other factors. This allows them to manage resources better and reduce costs while helping speed up the company’s clean energy transition.
3. Enhance quality control
Machine learning can monitor IIoT sensor data in real-time, identifying anomalies and potential issues before they escalate. This industrial application of machine learning in IoT ensures consistent quality in manufacturing and other industries. Semiconductor manufacturers use IoT and machine learning applications to analyze sensor data from their fabrication facilities, detecting anomalies and defects in the manufacturing process before they can affect product quality.
4. Monitor patterns across a network
Machine learning can analyze IoT device data systemwide to identify patterns of potential security threats such as cyber-attacks or anomalous behavior. Many organizations apply ML system monitoring to enhance the overall security posture of IoT networks. You can also train machine learning in IoT security models to recognize and block known malware or cyber threats targeting IoT devices.
Companies, including Cisco and IBM, offer ML-powered security solutions that can analyze network traffic patterns and identify potential threats, such as distributed denial-of-service (DDoS) attacks. IBM says their security tool can “automatically escalate or close up to 85% of alerts, helping to accelerate security response timelines for clients.” That’s a considerable time and cost savings.
5. Improve response time for time-critical applications
Limited connectivity? No problem. With the ability to process data and make decisions locally, edge devices powered by machine learning can respond faster and operate more efficiently, even when not networked. Edge ML reduces the need for constant communication with the cloud and improves response times for time-critical applications, such as autonomous vehicles or industrial automation systems. This fusion of machine learning in IoT devices at the edge unlocks a new frontier of real-time intelligent applications.
The smart city initiatives of Dubai provide a compelling use case for edge machine learning. They’ve deployed hundreds of edge AI boxes throughout the city to process video from traffic cameras. These edge AI devices automatically detect traffic violations, roadway hazards, and suspicious activities in the video streams. These devices help authorities respond to incidents swiftly while handling data on the edge preserves privacy by avoiding indiscriminate video upload. Dubai’s Roads and Transport Authority reports the project has so far improved incident monitoring by 63%, reduced response times by 30%, and reduced journey times by 20%.
These use cases for machine learning in IoT could apply to nearly any industry. All kinds of businesses have projects involving data analysis, predictive analytics, quality control, network monitoring, and time-critical applications. Let’s take a look at a few industries that are ahead of the curve in taking advantage of machine learning and IoT.
The industries where machine learning and IoT thrive
While the applications of machine learning and IoT are vast, certain industries are particularly well-suited for implementing this powerful combination:
- Manufacturing: From predictive maintenance to quality control and process optimization, machine learning and IIoT can streamline operations, reduce downtime, and improve overall efficiency in manufacturing facilities. According to McKinsey, “Operators that have applied AI in industrial processing plants have reported a 10 to 15 percent increase in production and a 4 to 5 percent increase in EBITA.”
- Transportation and logistics: ML can be applied to data from IoT-connected vehicles, infrastructure sensors, and supply chain systems to optimize routing, predict maintenance needs, and improve fleet management. IoT and machine learning projects result in reduced fuel consumption, lower emissions, and enhanced delivery times. For instance, Intellias helped a logistics partner in Europe customize a full-stack platform for an intelligent IoT robotics fleet.
- Agriculture: Agritech uses IoT sensors to monitor soil conditions, weather patterns, and crop health. Machine learning tapped into this data can help optimize irrigation schedules, predict yield, and enhance overall farm management practices. This can boost yields while reducing waste and environmental impact.
ML in IoT for agriculture is experiencing a bumper crop of its own, with massive investment in the last few years. “Deloitte predicts the installed base of Internet of Things endpoints for precision crop farming, livestock management, and agricultural equipment tracking will near 300 million by the end of 2024—a 50% growth over the 200 million installed base in 2022.”
Read more about AI in Agriculture — The Future of Farming. - Healthcare: The Internet of Medical Things (IoMT) devices like wearables and remote patient monitoring systems generate vast amounts of data. ML models can analyze this data to detect patterns, predict adverse events, and personalize treatment plans, ultimately improving patient outcomes.
Challenges (and solutions) of machine learning for IoT
As the Internet of Things continues its relentless march, integrating machine learning into IoT ecosystems offers immense potential and significant challenges. The ability of ML models to extract insights from IoT sensors could revolutionize everything from predictive maintenance to smart homes. However, a few hurdles must be overcome first.
- Massive datasets: IoT devices are notoriously data-hungry beasts. Given their massive data needs, cramming ML models onto small edge devices with limited computing power is effectively impossible. This restricts much of the heavy ML lifting to the cloud, which can create latency issues. Techniques like pruning, quantization, and federated learning can reduce model size and processing needs.
- Security nightmares: The diffuse nature of IoT networks substantially increases the attack surface for cyberattacks. ML models running on IoT devices could be reverse-engineered or poisoned with bad data. Federated learning techniques that keep training data local help mitigate exposure. IT security professionals are also exploring containerization, secure enclaves, and blockchain distribution of ML model updates.
- Integration headaches: IoT environments are a heterogeneous mess of devices, operating systems, network protocols, and data formats. Getting ML platforms to work robustly across this tangle is an ongoing struggle. To help wrestle IoT’s diversity under control, we’re seeing the rise of solutions like machine learning operations (MLOps) platforms and purpose-built ML tooling for IoT.
Intellias as your ML for IoT partner
The world is only going to get more connected and data-driven. As this trend continues, machine learning and the Internet of Things will continue to shape how we live, work, and interact with technology.
Intellias offers expertise in implementing machine learning IoT solutions. We tailor solutions to address specific business needs across multiple industries, including retail, financial services, healthcare, and mobility. Whether your current priority is enhanced data analysis, predictive maintenance, or improved security, we can help you explore what’s possible.
Contact Intellias to help your organization harness the promise and power of machine learning and IoT.
