SYSTEM FOR MANAGING IOT DEVICE NETWORKS USING MACHINE LEARNING

Abstract

The invention provides a machine learning-based system for managing IoT device networks, offering real-time monitoring, predictive maintenance, anomaly detection, and dynamic optimization. It features a data collection module for gathering real-time device data, a preprocessing module for data standardization, and a machine learning engine that analyzes the data to predict failures, detect anomalies, and enhance performance. The automated response module executes predefined actions based on the analysis, while a user-friendly management dashboard provides real-time insights and control. The system's adaptive learning capabilities enable it to continuously improve, making it an efficient and scalable solution for complex IoT environments.

Specification

Description:The embodiments of the present invention generally relates to the field of Internet of Things (IoT) network management. It involves a system that leverages machine learning algorithms to optimize the monitoring, control, and maintenance of IoT device networks. The invention addresses the challenges posed by the dynamic and heterogeneous nature of IoT environments, enabling efficient operation, predictive maintenance, and enhanced security of connected devices.
BACKGROUND OF THE INVENTION
The following description of related art is intended to provide background information pertaining to the field of the disclosure. This section may include certain aspects of the art that may be related to various features of the present disclosure. However, it should be appreciated that this section be used only to enhance the understanding of the reader with respect to the present disclosure, and not as admissions of prior art.

The adoption of IoT devices has grown exponentially across various industries, including smart ho , Claims:1. A method for managing an IoT device network using machine learning, comprising:
collecting real-time data from a plurality of IoT devices;
pre-processing the data to remove noise and standardize inputs;
analyzing the preprocessed data using a machine learning engine to predict device failures and detect anomalies;
executing automated actions based on the analysis results to optimize network performance and maintain security.

2. The method of claim 1, wherein the preprocessing of data includes implementing a feature selection process to reduce dimensionality, using techniques such as Principal Component Analysis (PCA) or Recursive Feature Elimination (RFE), to enhance the efficiency and accuracy of the machine learning models.

3. The method of claim 1, further comprising a step of periodically retraining the machine learning models using updated datasets to improve predictive accuracy and adapt to changing network conditions, wherein the retraining process is triggered based on performance metrics such as

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