Machine Learning in IoT: Advancements and Applications

by May 30, 2023#MachineLearning, #HomePage, #IoT

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Table of Content

  1. Machine Learning in IoT Helps to Discover and Secure Devices
  2. Other IoT Use Cases Benefiting from Machine Learning
  3. Building Machine Learning Features on IoT Edge Devices

IoT is evolving and making impactful changes in virtually all industries by improving processes and products. 

IoT devices and data transmissions are growing exponentially, creating many opportunities and challenges for enterprises. 

IoT devices generate data in real time that is pushed to the cloud and can be used effectively with machine learning to generate intelligence. Machine learning in IoT is about working with large datasets to create accurate machine learning models trained to analyze and interpret data generated by IoT devices.

Machine Learning in IoT Helps to Discover and Secure Devices

IoT systems usually incorporate many IoT devices with different hardware, software, operating systems, and functions that must interoperate, lack self-protection, and have long-life cycles— creating a large network attack surface. These aspects create challenges in device management, visibility, monitoring, scalability, and security.

IoT devices are usually designed with fixed functionalities that provide data patterns suitable for training machine learning models and implement specific machine learning features that use data insights to trigger responses and alerts. 

Machine learning for IoT can be applied to create models that learn from the device type (ID), specific instances, and behavior patterns for identifying and securing devices, detecting abnormal behavior, quarantining devices, improving perimeter defense, and preventing threats.

Machine learning for IoT can also provide a method for authenticating devices and improving user experiences by protecting devices with other methods besides signature-based methods.

Other IoT Use Cases Benefiting from Machine Learning

Machine learning can be used in many other IoT applications as well, such as predictive maintenance, smart homes, IoT supply chain, energy usage optimization, air quality analysis, temperature, task automation, etc.

Building Machine Learning Features on IoT Edge Devices

You can create machine learning capabilities in IoT edge devices using TensorFlow Lite—an open-source set of tools and libraries with models for many domains (including object detection, image classification, pose estimation, video, and audio classification).

IoT engineers can start a project using pre-trained machine learning models to deploy on embedded devices and extend or customize models according to the specific domain.

At Krasamo, we have worked with embedded software and created machine learning software for more than a decade, giving us the expertise to support projects for new partners.

About Us: Krasamo is a mobile-first Machine Learning and consulting company focused on the Internet-of-Things and Digital Transformation.

Click here to learn more about our machine learning services.


IIoT-Driven Transformation: Boosting Industrial Efficiency & Innovation

IIoT-Driven Transformation: Boosting Industrial Efficiency & Innovation

This paper discusses the transformative potential of the Industrial Internet of Things (IIoT) in enhancing operational efficiency and reducing expenses in plants and buildings. By leveraging wireless sensors, data collection, analytics, and machine learning, IIoT systems create a competitive advantage through improved interoperability and connectivity. We explore the factors driving IIoT adoption, the benefits it offers, and the different types of IIoT software. The paper also highlights Krasamo’s expertise in IoT consulting services and their comprehensive range of IoT offerings to help enterprises implement and benefit from IIoT systems.

Creating a Machine Learning Use Case: Steps and Considerations

Creating a Machine Learning Use Case: Steps and Considerations

This article discusses the steps and considerations for creating a machine learning use case to improve business processes. It explains the concept of machine learning and the importance of data quality and volume in creating accurate predictions. The article outlines the steps in creating an ML use case, including defining the problem, collecting and preparing data, defining product objectives and metrics, training and evaluating the model, and deploying the model. The article also discusses the types of ML problems and how to discover ML use cases in existing business processes. Overall, the article emphasizes the importance of understanding business problems and identifying opportunities to use ML to create enhanced solutions.

AI Consulting: Accelerating Adoption Across Business Functions

AI Consulting: Accelerating Adoption Across Business Functions

In today’s digital age, adopting AI solutions is crucial for businesses to gain a competitive advantage. However, many organizations lack the necessary data and machine learning (ML) skill set to create valuable AI solutions. This is where AI consultants play a key role, bridging the skill set gap and accelerating the adoption of AI across business functions. AI consultants help assess an organization’s maturity level and design a transformation approach that fits the client’s goals. They also promote the creation of collaborative, cross-functional teams with analytical and ML skills, and work on creating consistency in tools, techniques, and data management practices to enable successful AI adoption.

Building Machine Learning Features on IoT Edge Devices

Building Machine Learning Features on IoT Edge Devices

Enhance IoT edge devices with machine learning using TensorFlow Lite, enabling businesses to create intelligent solutions for appliances, toys, smart sensors, and more. Leverage pretrained models for object detection, image classification, and other applications. TensorFlow Lite supports iOS, Android, Embedded Linux, and Microcontrollers, offering optimized performance for low latency, connectivity, privacy, and power consumption. Equip your IoT products with cutting-edge machine learning capabilities to solve new problems and deliver innovative, cost-effective solutions for a variety of industries.

Feature Engineering for Machine Learning

Feature Engineering for Machine Learning

Feature engineering is a crucial aspect when it comes to designing machine learning models, and it plays a big role in creating top-notch AI systems. Features are attributes that represent the problem of the machine learning use case and contribute to the model’s prediction. The process of feature engineering involves creating relevant and useful features from raw data combined with existing features, adding more variables and signals to improve the model’s accuracy and performance. It starts manually and can be accelerated by adding automated feature engineering tools and techniques. Follow the steps of feature engineering to optimize your machine learning models and create innovative products.