Integrating IoT and machine learning: Benefits and use cases

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.

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.