REAL-TIME IOT DATA ANALYTICS PLATFORM

Abstract

The present invention relates to a real-time IoT data analytics platform designed to efficiently collect, process, analyze, and visualize data from diverse IoT devices across various industries. The platform integrates edge computing to process data locally, reducing latency and bandwidth consumption, while leveraging centralized stream processing for large-scale analytics. It incorporates machine learning models for predictive analytics and anomaly detection, providing real-time insights and actionable alerts. With customizable dashboards, robust security features, and scalability to accommodate growing IoT networks, the platform enables timely decision-making and enhances operational efficiency in applications ranging from industrial automation to smart city management.

Specification

Description:In the following description, for the purposes of explanation, various specific details are set forth in order to provide a thorough understanding of embodiments of the present disclosure. It will be apparent, however, that embodiments of the present disclosure may be practiced without these specific details. Several features described hereafter can each be used independently of one another or with any combination of other features. An individual feature may not address all of the problems discussed above or might address only some of the problems discussed above. Some of the problems discussed above might not be fully addressed by any of the features described herein.

The ensuing description provides exemplary embodiments only and is not intended to limit the scope, applicability, or configuration of the disclosure. Rather, the ensuing description of the exemplary embodiments will provide those skilled in the art with an enabling description for implementing an exemplary embodiment. It should be understood that various changes may be made in the function and arrangement of elements without departing from the spirit and scope of the disclosure as set forth.

Specific details are given in the following description to provide a thorough understanding of the embodiments. However, it will be understood by one of ordinary skill in the art that the embodiments may be practiced without these specific details. For example, circuits, systems, networks, processes, and other components may be shown as components in block diagram form in order not to obscure the embodiments in unnecessary detail. In other instances, well-known circuits, processes, algorithms, structures, and techniques may be shown without unnecessary detail to avoid obscuring the embodiments.

Also, it is noted that individual embodiments may be described as a process that is depicted as a flowchart, a flow diagram, a data flow diagram, a structure diagram, or a block diagram. Although a flowchart may describe the operations as a sequential process, many of the operations can be performed in parallel or concurrently. In addition, the order of the operations may be re-arranged. A process is terminated when its operations are completed but could have additional steps not included in a figure. A process may correspond to a method, a function, a procedure, a subroutine, a subprogram, etc. When a process corresponds to a function, its termination can correspond to a return of the function to the calling function or the main function.

The word "exemplary" and/or "demonstrative" is used herein to mean serving as an example, instance, or illustration. For the avoidance of doubt, the subject matter disclosed herein is not limited by such examples. In addition, any aspect or design described herein as "exemplary" and/or "demonstrative" is not necessarily to be construed as preferred or advantageous over other aspects or designs, nor is it meant to preclude equivalent exemplary structures and techniques known to those of ordinary skill in the art. Furthermore, to the extent that the terms "includes," "has," "contains," and other similar words are used in either the detailed description or the claims, such terms are intended to be inclusive in a manner similar to the term "comprising" as an open transition word without precluding any additional or other elements.

Reference throughout this specification to "one embodiment" or "an embodiment" or "an instance" or "one instance" means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the present disclosure. Thus, the appearances of the phrases "in one embodiment" or "in an embodiment" in various places throughout this specification are not necessarily all referring to the same embodiment. Furthermore, the particular features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.

The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the disclosure. As used herein, the singular forms "a", "an", and "the" are intended to include the plural forms as well, unless the context indicates otherwise. It will be further understood that the terms "comprises" and/or "comprising," when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof. As used herein, the term "and/or" includes any and all combinations of one or more of the associated listed items.

The present invention relates to a real-time IoT data analytics platform that enables the seamless collection, processing, and analysis of data from a diverse range of IoT devices. The platform incorporates advanced technologies like edge computing, stream processing, and machine learning (ML) to enable real-time analytics and decision-making. This invention is designed to overcome the challenges posed by traditional systems, which often struggle with latency, scalability, and the integration of heterogeneous devices.

The first key component of the platform is the data ingestion module. This module serves as the entry point for the IoT data, capable of ingesting data from multiple IoT devices, regardless of their protocols or data formats. The module supports a wide range of protocols, such as MQTT, HTTP, CoAP, and others, ensuring compatibility with devices commonly used in various industries. The data is normalized into a common format, allowing for seamless integration across different device types. This module ensures that all data streams are properly processed and directed to the next stage for further analysis.

To minimize data transmission time and reduce network bandwidth consumption, the edge processing module processes data closer to its source. This module performs preliminary operations, such as data filtering, aggregation, and basic analytics, to reduce the volume of data that needs to be transmitted to the centralized system. It also incorporates local machine learning models, which can perform inference directly on the device or edge gateway. This feature enables real-time decisions in time-sensitive applications, such as anomaly detection or predictive maintenance, at the edge before sending any necessary data to the central system.

The centralized data processing module is responsible for handling large-scale data streams in real time. Using distributed stream processing frameworks like Apache Kafka and Apache Flink, this module ensures low-latency processing of high-throughput data. It integrates various data sources, handles data transformation, and runs advanced analytics algorithms for more complex use cases. These algorithms include anomaly detection, pattern recognition, and predictive analysis using machine learning models. The centralized module is designed to handle vast amounts of incoming data, ensuring scalability without compromising performance.

The machine learning and analytics module is integrated throughout the system, both at the edge and at the central processing level. At the edge, lightweight machine learning models can detect patterns and anomalies, triggering alerts for further investigation. At the central level, more sophisticated models can be employed to forecast trends, optimize operations, and predict system failures. The module is designed for continuous learning, allowing the platform to adapt to new data and changing environments without requiring downtime. The system can update models and push them to edge devices or cloud infrastructure in real time.

The visualization and alerting module presents the processed data in the form of customizable dashboards, enabling users to monitor key performance indicators (KPIs) and other metrics in real time. The dashboard allows users to view live data, historical trends, and predictions generated by the analytics module. Alerts are triggered when specific conditions are met, such as data anomalies, threshold breaches, or system failures. The alerting system can be configured to send notifications to users or automated systems, ensuring timely intervention when critical events are detected.

Security and data privacy are critical components of the platform. The system includes multiple layers of security to ensure the integrity of data and prevent unauthorized access. Encryption protocols, such as TLS/SSL, are employed for secure data transmission, while access control mechanisms ensure that only authorized personnel can access sensitive information. Additionally, the platform supports secure storage practices, ensuring that data is encrypted both in transit and at rest. By providing these features, the platform ensures that all IoT data is protected throughout its lifecycle.

The platform is designed for scalability, capable of handling increasing data volumes as more IoT devices are added to the system. By leveraging distributed computing frameworks and edge processing, the platform can maintain high performance even as the number of connected devices grows. The system automatically scales up or down depending on the workload, ensuring consistent performance without manual intervention. This is particularly important in industries where real-time analytics are required to support a large and growing network of IoT devices, such as in smart cities or large-scale industrial operations.

In a typical industrial IoT scenario, the platform is deployed to monitor the performance of machines and equipment in a manufacturing plant. IoT sensors collect real-time data, such as temperature, pressure, vibration, and other critical metrics from the machines. This data is ingested by the platform's data ingestion module, which normalizes and forwards it to the edge processing module. Here, preliminary data filtering and analysis take place, such as identifying abnormal temperature readings or vibrations that could signal potential failures.

If the edge processing identifies a potential issue, an alert is triggered for immediate action. Meanwhile, the data is sent to the centralized data processing module for deeper analytics, where machine learning models predict future equipment failures and suggest optimal maintenance schedules. The dashboard displays real-time data, historical trends, and predicted maintenance needs, allowing plant managers to optimize operations and reduce unplanned downtime.

In a smart city environment, the platform is used to monitor and manage a variety of systems, including traffic lights, air quality sensors, streetlights, and waste management systems. Each of these IoT devices generates large amounts of real-time data, which is ingested into the platform's data ingestion module. At the edge, basic processing is performed to handle time-sensitive tasks, such as adjusting traffic signals based on vehicle flow or alerting maintenance crews to a streetlight outage.

More complex data analytics, such as predicting traffic congestion patterns and optimizing waste collection routes, are handled at the central processing level. The system's real-time visualization dashboard provides city officials with a comprehensive view of operations, while the alerting system ensures that critical events, such as elevated pollution levels or traffic accidents, are immediately flagged for attention. This allows for efficient resource management, enhanced public safety, and improved urban living conditions.

While considerable emphasis has been placed herein on the preferred embodiments, it will be appreciated that many embodiments can be made and that many changes can be made in the preferred embodiments without departing from the principles of the invention. These and other changes in the preferred embodiments of the invention will be apparent to those skilled in the art from the disclosure herein, whereby it is to be distinctly understood that the foregoing descriptive matter to be implemented merely as illustrative of the invention and not as limitation. , Claims:1.A Real-time IoT Data Analytics Platform comprising:
A data ingestion module configured to collect data from multiple heterogeneous IoT devices using standardized protocols;
An edge processing module designed to pre-process the collected data, including data filtering and aggregation;
A centralized data processing module implementing real-time stream processing for low-latency analytics;
An analytics module integrating machine learning models for predictive analysis and anomaly detection;
A visualization and alerting module for real-time data display and alert generation.

2.The platform of claim 1, wherein the data ingestion module supports protocols including but not limited to MQTT, HTTP, and CoAP, enabling seamless integration with diverse IoT devices.

3.The platform of claim 1, wherein the edge processing module performs preliminary data analysis and local inference using pre-trained machine learning models to reduce data transfer latency.

4.The platform of claim 1, wherein the centralized data processing module employs a distributed stream processing framework such as Apache Kafka or Apache Flink to handle high-throughput, low-latency data streams.

5.The platform of claim 1, wherein the analytics module allows for real-time updates to the machine learning models without interrupting the ongoing data analytics process.

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