Integrating IoT and machine learning: Benefits and use cases
With applications in industries like manufacturing and healthcare, integrating IoT with machine learning offers a range of unique benefits.
The internet of things, or IoT, connects sensors and control devices, enabling computer systems to interact with and influence real-world activities.
Although real-time process control is a major use case for IoT, many such requirements can be addressed with simple programming or event-processing software. These IoT applications typically only process events in specific, predetermined ways, and they can't easily correlate multiple events or understand changes over time -- an area where machine learning excels.
Integrating ML with IoT leads to numerous benefits, such as efficiency in real-time analytics and predictive maintenance. Because ML and IoT complement each other's strengths, they've successfully been applied across many industries, including healthcare, industrials, manufacturing, utilities and business management.
IoT and machine learning
The integration of ML with IoT is driven by the growing complexity of real-time process control applications as well as the untapped value of historical IoT data. That historical data helps businesses spot trends and gain a better understanding of how their products and services are created.
Many business IoT systems produce an overwhelming volume of events with insufficient linkage to the processes they control, making practical analysis difficult. ML algorithms address this challenge by learning to identify patterns in data.
ML systems can convert raw IoT events into meaningful process conditions, which is key to analyzing and automating complex workflows. Whereas a sensor can signal an event, ML can determine what that event actually means.
By analyzing signals from multiple sensors, ML models develop a comprehensive view of the overall system: how it creates, moves and stores what a company sells. That deeper awareness enables the ML model to provide real-time insights into the system's state -- for example, normal operation or fault -- and recommendations for restoring or optimizing operations.
The role of training
ML systems require training to perform their tasks effectively. In the context of IoT, the objective of training is to transform raw sensor data into meaningful process conditions.
This can, in theory, be done by having the ML model observe humans' reactions to the conditions that sensors report. But more often, subject matter experts analyze the patterns detected by ML systems and assign corresponding conditions, which the model then uses to generate recommendations or take actions.
Unlike traditional programmatic IoT systems, which recognize only predefined scenarios, ML models adapt dynamically to new conditions. Once trained, they can analyze sensor data and provide recommendations in real time.
Historical data analysis
Historical IoT data plays a dual role in ML integration:
- Training and pattern recognition. Historical data gives ML models the foundation they need to recognize patterns that represent conditions and, with the support of experts, link those patterns to recommended actions.
- Analysis and forecasting. Analyzing historical data enables ML models to recognize trends, spot developing conditions that require intervention and compare the value of alternative actions.
Machine learning and IoT applications across industries
ML-enabled IoT applications mix real-time and historical data to inform their recommendations and analyses. How that integration is done, and which specific use cases it supports, varies by vertical market segment.
Healthcare
Healthcare is among the fastest-growing verticals for ML-enabled IoT, including both real-time and non-real-time applications -- although the latter has yielded more use cases so far. For example, ML analysis of broad medical records, combined with patient-specific data and real-time patient vital signs obtained via IoT, can alert the patient care team to trends that require intervention.
The same information, when collected for historical analysis, can drive reviews and changes to overall patient care protocols, treatment plans, medications and medical equipment. Many of these use cases dovetail with other uses of ML in healthcare, such as analyzing medical imaging, electrocardiogram data and blood test results.
Industrials and manufacturing
Industrial settings are a significant area for ML and IoT use cases and a fast-growing domain for real-time applications. ML-driven process control is more flexible than traditional fixed programming of automated systems. Because ML algorithms can adapt over time, they help improve process control systems over time by learning from and adjusting to new conditions.
In manufacturing, IoT sensor data collected over time can be combined with real-time data from production processes and logistics, such as manufacturing steps, transportation, and storage of parts and finished goods. This can improve efficiency in areas like parts delivery and traditional just-in-time manufacturing practices, reducing costs, vehicle miles traveled and carbon footprint.
Equipment and vehicle predictive maintenance is another important use case. ML models can use IoT-reported process activity data to learn long-term relationships and anticipate failures. When paired with historical cost information on past maintenance from core business applications, ML can also help determine when to retire vehicles or equipment.
Utilities management
The utility industry stands out as a committed adopter of IoT, ML and the combination of the two. This is due to the sector's multifaceted nature, which combines elements from areas including transportation, customer support, regulatory compliance and business process management.
Consequently, utilities involved in the distribution or handling of power, gas, water and wastewater are a fast-growing application for ML and IoT. These applications combine real-time missions, such as early detection of problems, with historical analysis to inform capacity planning, resource allocation and environmental impact management.
Generative AI and IoT
In addition to optimizing the value of IoT, ML can also be an on-ramp to a generative AI use case. Generative AI -- a subset of deep learning, and itself a branch of machine learning -- offers capabilities that expand the potential of IoT systems.
Because generative AI can respond to natural language questions, generate images and analyze data, it is a candidate for IoT applications that involve processing high volumes of data and supporting free-form human interactions. Many ML and IoT use cases, including business process management, are likely to evolve toward more generative AI-focused applications over time. To manage this transition, organizations should be proactive, aiming to balance advanced functionalities with cost-efficiency.
Business process management
In business process management, ML and IoT applications typically rely on non-real-time analysis. These systems often repurpose IoT data collected for other applications to track business processes in detail.
Business process management has the unique and explicit goal of assembling real-time IoT data that tracks business processes and combining it with other historical company data. Traditional business analytics focuses on accumulated transactional data about a company's operations, which often lacks any link with how the company creates, distributes and manages its products and resources. By combining IoT-derived insights with transactional data analysis, ML algorithms can give decision-makers a more complete picture of operations.
Tom Nolle is founder and principal analyst at Andover Intel, a consulting and analysis firm that looks at evolving technologies and applications first from the perspective of the buyer and the buyer's needs. By background, Nolle is a programmer, software architect, and manager of software and network products. He has provided consulting services and technology analysis for decades.
