System for real-time data aggregation and analysis in IoT networks

Abstract

A system for real-time data aggregation and analysis within an Internet of Things (IoT) network, consisting of: a multitude of IoT sensor nodes configured to collect data from multiple sources, each sensor node comprising a data acquisition unit for capturing data in real time and a local processing unit for preprocessing the captured data by performing data filtering, normalization and compression prior to transmission; a centralized aggregation server communicating with the multitude of IoT sensor nodes, the aggregation server comprising: a data aggregation module configured to receive, integrate, and synchronize pre-processed data streams from the multitude of sensor nodes, thereby enabling consolidated data storage; a timestamped metadata indexing unit coupled with the data aggregation module and configured to add metadata to each data entry, including a unique identifier, a geographic location, and a timestamp; an analysis engine integrated into the aggregation server, which includes the following: a data processing unit configured to perform real-time analysis of the aggregated data by applying pattern recognition and anomaly detection techniques and to generate processed results based on predefined parameters; a predictive analytics module that is connected to the data processing unit and configured to perform predictive modeling tasks by extrapolating trends from historical and real-time data; a communication interface connected to the aggregation server, configured to enable bidirectional data exchange with external monitoring devices, user interfaces, or remote management systems via secure communication protocols; and a dynamic load balancing unit embedded in the aggregation server, configured to dynamically distribute computing resources between the data processing unit and the predictive analytics module depending on data volume and complexity.

Description

Field of the invention

[0001] The present invention generally relates to the field of data processing systems, and more particularly, in one example embodiment, to methods for analyzing time-series data in Internet of Things (IoT) devices. More specifically, the disclosure relates to a system and apparatus for performing real-time data aggregation and analysis using edge computing, enabling faster processing, scaling, and security of data in distributed IoT ecosystems. It is useful in multiple sectors requiring massive data processing, from smart cities and industrial automation to agriculture and healthcare, ensuring high throughput in analyzing incoming data generated in these IoT contexts to enable immediate response based on available real-time endpoint resources. Background of the invention

[0002] The Internet of Things (IoT) in a wide range of sectors, from smart cities, healthcare, and industrial automation to agriculture, has unleashed devices that now communicate with each other and form systems. With the increasing number of connected devices and their integrative approach, the data generated via connected devices is growing exponentially. This has now taken the form of massive amounts of data, and therefore there is a need for systems capable of aggregation, processing, and real-time analytics at this enormous scale. The systems in use today typically struggle with data latency, scaling issues, and the faithful reconstruction of information from disparate databases.However, these methods often require cloud-based solutions for collecting and analyzing data, which may not be equally effective in real-time processing scenarios or may raise privacy concerns due to the nature of decentralized storage and centralized connections. Consequently, there is a great need for a genuine real-time data aggregation and analysis system that can provide instant insights into the IoT network. The invention offers a solution to these problems by adding a decentralized, edge-native capability to the system that optimizes speed, scalability, and security for data processing.

[0003] The emergence of the Internet of Things (IoT) has revolutionized a variety of industries in recent years, unprecedented in their interconnectedness with exceptional levels of connectivity between devices, sensors, and systems. The large network of devices generates massive amounts of data, making live aggregation, processing, and analysis difficult. Real-time data processing is critical because most IoT applications consist of critical scenarios that require immediate action, such as (ii) automated manufacturing, (iii) health monitoring, and (iv) smart city infrastructures. Therefore, several works have been proposed to address the data aggregation and analysis requirements in IoT networks.However, these solutions struggle with scalability as the number grows exponentially, leading to challenges related to latency and secure data access in highly dynamic environments that require near-real-time processing of massive amounts of data.

[0004] Among the various systems used to process IoT data, cloud computing is the most commonly implemented architecture. Cloud platforms provide more powerful computing resources and storage than those used to build most IoT networks, so data processing is performed on this external, remote server. This data can then be processed and analyzed using advanced tools available at the cloud machine layer, deep learning models, and more. Cloud-based designs are scalable, allowing companies to add capacity as data volumes grow without spending significant sums on on-premises infrastructure. However, this method has its drawbacks. Because IoT devices must send data to and from the cloud, there is an inherent latency in cloud-based processing.For real-time applications where on-site decisions are critical, this latency can be unacceptable. Even a slight delay in ABS data processing in an industrial automation scenario can lead to operational inefficiencies or compromise safety.

[0005] Cloud-based solutions not only offer a way to overcome latency issues, they also bring their own data security and privacy challenges. Sending large amounts of personal information to a number of centralized servers greatly increases your attack surface and the risk of data leaks (or worse). While encryption protocols and authentication mechanisms can mitigate these risks to some extent, the highly centralized nature of cloud-based solutions, which makes them attractive, also creates a single point of vulnerability. With increasing data privacy regulations, organizations must ensure they comply with these laws, and this can prove difficult in a cloud-based environment where data can cross multiple jurisdictions.Second, dependence on cloud service providers raises certain concerns about data ownership and dependence on a particular provider, as organizations may become dependent on specific providers to meet their processing needs.

[0006] Edge computing has recently emerged as a potential solution to these problems, helping to alleviate the burden of cloud computing. Edge computing is a distributed architecture, meaning data processing and analysis occurs close to the data source. This reduces latency because data doesn't need to be transferred to remote servers, enabling real-time processing and decision-making. Furthermore, edge computing offers better data security and privacy because it eliminates the need for centralization to store all sensitive information. However, edge computing solutions actually face few challenges despite offering many advantages. For one, edge devices typically lack computational resources and storage space, which can prevent them from processing large amounts of data or running powerful machine learning systems.This results in edge-based solutions being unable to handle the data volume and operational complexity caused by large IoT networks.

[0007] Furthermore, with the introduction of edge computing into IoT networks, infrastructure costs can increase, as companies must provision additional hardware for distributed processing. Edge computing must be supported by physical devices deployed at various points in the network, unlike the cloud, which offers scalable and configurable pricing models. This is especially challenging for large-scale IoT deployments, as all edge devices must be updated, monitored, and secured. However, even with reduced latency through edge computing, there is still a need for data aggregation and analysis further up the network. When it comes to things like long-term data storage, large-scale rapid triage, or training machine learning models, you still need a centralized server / cloud platform.The problem is that this converged infrastructure model introduces network architecture complexity—and can easily create interoperability barriers as data is transferred between multiple processing layers moving from the edge to the cloud.

[0008] Recently, data processing solutions have become increasingly popular with the integration of technologies such as machine learning (ML) and artificial intelligence (AI) into IoT to enhance data analytics. Data streamed into databases can leverage real-time machine learning models for pattern recognition and prediction from the same streaming data. Advances like these can help IoT networks deliver more accurate and effective insights, for example, to enable predictive maintenance or the identification of anomalies in industrial equipment. A future extension could be one that enables automated decision-making based on data processed by AI models. However, deploying machine learning models in IoT environments presents some technical challenges.These are: Training ML models requires significant computing power and large datasets, which may not be available at the edge of the network. Furthermore, maintaining and retraining machine learning models in a decentralized network can be complicated and expensive, as organizations need to ensure the model stays up-to-date with new data patterns. The computational costs of edge devices and fog nodes can be significant due to the inherently computationally intensive nature of model inference.

[0009] There is also already a proposed technique for using distributed ledger technologies such as blockchain to improve data security and transparency in IoT networks. Blockchain allows the creation of an immutable record of all transactions that take place on your decentralized network, thus making the data secure and verifiable. This is especially important in applications where data integrity and trustworthiness are crucial, such as supply chain management or healthcare. The problem, however, is that while blockchain seems to be a perfect key to our rules of conduct in IoT networks, its cost and energy consumption significantly limit their services. Blockchain networks can also introduce latency, as individual entered transactions must be verified and recorded on different nodes.Therefore, blockchain may not be ideal for real-time IoT use cases where large amounts of data need to be processed very quickly.

[0010] While current technologies such as cloud computing, edge computing, and fog computing offer very practical tools for handling IoT data, they still encounter limitations such as scalability, uncertainty in response times, and overly complex volcanic ash. This was a reminder that next-generation IoT networks require an end-to-end method for rapidly centralizing and analyzing real-time data. To address these limitations, the invention proposes a decentralized system using both edge and fog computing to combine their advantages while providing an exceptional opportunity for integrating advanced machine learning. It represents an implemented solution for scalable and secure real-time management of IoT data, achieved by optimizing edge-based processing through intermediate analysis at fog nodes with centralized coordination.This is a method for reducing latency to enable additional privatization of the information organizations need to gain useful insights from the IoT in the fastest and most efficient way. Summary of the invention

[0011] The present invention describes a system for real-time data aggregation and analysis in IoT networks. The proposed invention is a novel edge-based solution for low-cost data processing. A network of IoT devices equipped with a data acquisition module, an edge processing unit, and a real-time data analysis engine forms the system. The edge provides the necessary infrastructure and modules required to enable developers to deploy IoT applications in such a way that data can be filtered at the edge itself by collecting it from various IoT sensors and processing it locally, thereby breaking down information blocks before transmission. Data retrieved from various data sources by a real-time data analysis engine is analyzed in depth using machine learning mechanisms to obtain actionable insights.In addition, there is a central server for coordinating data processing tasks distributed across the network to improve resource utilization and ensure secure data transmission from devices in the mesh topology to the central server. Leveraging the connected edge by enabling data processing eliminates delays and reduces the transfer of large data blocks to the cloud, which acts as a distributed system. This also increases data security, as confidential information can be processed locally and not transmitted to a central server. The invention solves these problems by providing a comprehensive solution for real-time monitoring, predictive analytics, and decision automation in the context of the IoT.

[0012] The purpose of the present invention is to overcome the disadvantages of the prior art. It provides a system for real-time data acquisition during analysis in IoT networks that enables high-throughput processing, as all operations are performed on datagrams while they are being transmitted through the network. It is designed to reduce latency through internal data analysis and enable rapid decision-making for scenarios requiring real-time response. The invention also aims to protect data and user privacy through local processing on the device, thus reducing the absolute need to transmit the most sensitive information to cloud servers.

[0013] Another objective of the invention is to provide a scalable solution capable of processing large amounts of data generated by vast IoT networks. The system can effectively process data from many IoT sources, including network growth, by using edge devices and fog nodes to distribute the processing load. The invention also aims to develop advanced machine learning capabilities in the network for predictive analytics and anomaly detection, which can increase operational efficiency while reducing human intervention.

[0014] Furthermore, an objective of the invention is to provide an architecture that can be adapted to various IoT environments with a flexible configuration, such as industrial automation, smart city infrastructure, and health monitoring. To achieve this degree of flexibility, a centralized cutting and grinding server is used, enabling task coordination and collaboration among various resources in the network, thus facilitating easier data flow while ensuring optimal performance. The disclosure also provides secure communication protocols that enable data integrity while guaranteeing unhindered access to properly interlinked contexts, thus addressing the need to incorporate strong security measures into IoT networks.

[0015] The objectives of the invention are (a) reducing data latency; (b) improving security and privacy aspects; scalability in data aggregation and processing at storage nodes; and thus the ability to support advanced analytics of IoT data based on soot findings, if needed. SHORT DESCRIPTION OF THE FIGURE

[0016] These and other features, aspects, and advantages of the present invention will become more readily understood when the following detailed description is read in conjunction with the accompanying drawings, in which like characters represent like parts throughout. Fig. Figure 1 shows a block diagram of a system for real-time data aggregation and analysis in IoT networks.

[0017] In Fig.Figure 1 shows a block diagram of a system for real-time data aggregation and analysis in IoT networks. The system 100 includes: a plurality of IoT sensor nodes configured to collect data from multiple sources, each sensor node comprising a plurality of IoT sensor nodes for real-time data collection and a local processing unit 102 for preprocessing the collected data by filtering, normalizing, and compressing the data prior to transmission; a centralized aggregation server 104 in communication with the plurality of IoT sensor nodes, the aggregation server comprising: a data aggregation module configured to receive, integrate, and synchronize preprocessed data streams from the plurality of sensor nodes, thereby enabling consolidated data storage;a time-stamped metadata indexing unit 106 connected to the data aggregation module and configured to tag each data entry with metadata, including a unique identifier, a geographic location, and a timestamp; an analytics engine 108 integrated with the aggregation server, comprising: a data processing unit configured to perform real-time analyses of the aggregated data by applying pattern recognition and anomaly detection techniques and generating processed results based on predefined parameters; a predictive analytics module 110 connected to the data processing unit and configured to perform predictive modeling tasks by extrapolating trends from the historical and real-time data;a communication interface 112 connected to the aggregation server, the communication interface configured to enable bidirectional data exchange with external monitoring devices, user interfaces, or remote management systems via secure communication protocols; and a dynamic load balancing unit 114 embedded in the aggregation server and configured to dynamically allocate computing resources between the data processing unit and the predictive analytics module based on data volume and complexity.

[0018] In one embodiment, the local processing unit 102 in each IoT sensor node includes a noise suppression module specifically configured to reduce interference from ambient noise sources through the use of adaptive filtering techniques, thereby improving the accuracy and reliability of the preprocessed data.

[0019] In one embodiment, metadata indexing unit 104 is configured to use a hierarchical tagging structure that categorizes data based on predefined metadata levels. This enables efficient querying and retrieval of data subsets by prioritizing tags associated with higher-level criteria, such as data type, source, or severity level.

[0020] In one embodiment, the data processing unit within the analytics engine 108 includes a multi-stage pattern recognition architecture, with each stage applying an increasingly refined set of rules or pattern templates calibrated based on historical data from similar IoT networks, thereby optimizing the detection of specific patterns or anomalies unique to the system's operating environment.

[0021] In one embodiment, the predictive analytics module 110 uses an adaptive learning mechanism configured to continuously update its predictive models based on new data inputs, such that it dynamically recalibrates its modeling mechanisms to maintain predictive accuracy over time, taking into account seasonal, temporal, or usage variations.

[0022] In one embodiment, the communication interface 112 further comprises an encryption module configured to secure data packets using multi-layer encryption techniques, thus ensuring data integrity and confidentiality during transmission, particularly over public or untrusted networks.

[0023] In one embodiment, the dynamic load balancer 114 includes a resource allocation mechanism configured to monitor compute load metrics in real time and perform preemptive load balancing. This ensures that processing bottlenecks are minimized and system performance is optimized even during fluctuating data inflow.

[0024] The real-time data collection and processing system for IoT sensor networks is designed to facilitate data collection, error-free transmission (data integrity), accurate representation, and up-to-date data. Essentially, each IoT sensor node has a data collection unit responsible for collecting real-time information from multiple sources. This data collection unit works in conjunction with a local processing unit that pre-filters, normalizes, and compresses the collected raw data to reduce bandwidth requirements during transmission before forwarding it to the central aggregation server. The noise reduction module of the local processing unit uses adaptive filtering techniques to reduce environmental interference.The module adjusts the filter settings in real time and eliminates these noise characteristics during data preprocessing to send highly accurate, nearly clean, and nearly perfect scripts for received wireless time streams to the central server.

[0025] Data enters the data aggregation module in the aggregation server, where it must be integrated by all sensor nodes. This unit is synchronized with a time-stamped metadata indexing module, which stamps each input data entry with information such as a unique identifier, geographic location, and timestamp. A hierarchy of tags defines how metadata is indexed into layers, allowing it to be processed first based on criteria such as data type and source content, or even processing urgency. This layered approach facilitates on-demand querying and improves the retrieval of specific subsets of data layers on demand, optimizing query speed when these critical operational parameters are needed.

[0026] On the aggregation server, integrated data (generated by integration flows from the FPGA and CPU) is further processed by the analytics engine. This is a data processing unit that performs real-time analytics using a multi-stage pattern recognition architecture. This is a tiered architecture where each tier applies the definitive rules or pattern templates for that specific data type. Tier 1 processes generic patterns based on data, while Tier 2 works with superficial and context-dependent patterns derived from IoT networks in similar environments. This allows the data processing unit to detect subtle anomalies and signals unique to the system's operating environment, resulting in higher accuracy and more meaningful analytics results.

[0027] The predictive analytics module integrated into the data processing unit forecasts trends based on historical and real-time data. The dynamic learning module is driven by an adaptive learning mechanism and continuously updates predictive models as new data arrives. This mechanism tunes the model parameters over time to account for seasonality, usage patterns, and changing timescales. "Lifetime autonomous" means that the detection system continues to deliver accurate predictions over time thanks to an adaptive approach that takes fluctuations within the IoT network into account.

[0028] An aggregation server also features a communication interface, allowing it to support secure mechanisms for bidirectional data transfer to and from external monitoring devices or client applications, as well as remote management systems. This device uses an interface supported by a multi-layer encryption module that provides packet-level security to ensure end-to-end integrity and confidentiality, especially when connecting to public or unsecured networks. The encryption module uses key rotation and is combined with multiple layers of encryption protocols to enhance transmission security.

[0029] The end system also includes a dynamic load balancer in the Atom aggregation server, which tracks computational load in real time and reallocates resources accordingly. This unit is based on a resource allocation mechanism that evaluates processing demands between data processing and predictive analytics modules that require compute capacity. The load balancer proactively reallocates compute resources to avoid bottlenecks and ensure optimal system performance under variable data input load. Load balancing allows the server to manage large amounts of data concurrently without slowing down analysis (or compromising timeliness or quality), thus scaling for the real-time performance required by the IoT.

[0030] Overall, the described framework provides an effective solution for on-device real-time data acquisition and flow analysis in IoT networks by ensuring a scalable, holistic system responsible for cost-conscious bidirectional data transmission from resource-constrained edge devices to cloud backends while providing efficient, local, software-based data processing modules over peripheral network interfaces.

[0031] In an IoT network, the real-time data aggregation and analysis system consists of many interconnected IoT devices, each containing multiple elements suitable for processing and analyzing real-time data. At the heart of each device are data acquisition modules connected to multiple sensors present in the IoT network. The module can acquire many types of data, such as temperature, humidity, pressure, or whatever you deem necessary. The data acquisition unit uses an analog-to-digital converter to enable digital systems with compatibility, as well as a signal conditioning circuit that processes the acquired signals by filtering out noise and changing their levels.

[0032] Each unit has an edge processing unit containing the distributed computing module, which filters data (and preprocesses or compresses it if necessary). This function is controlled by a microcontroller with an integrated FPGA-based accelerator to increase data processing speed. The edge processing unit uses a data reduction mechanism to filter out redundant or irrelevant data, thus reducing the amount of information transmitted and stored, resulting in memory savings. This function is important for conserving network bandwidth (especially in scenarios where large amounts of data are generated) and reducing / increasing latency.

[0033] Each IoT device also has a data analytics engine for real-time data analysis. This engine can run machine learning models on the edge device to find patterns, trends, and anomalies in the data. The data analytics engine features a neural network processing unit to accelerate model inference and can work with various models. The engine also has a self-learning function that recalibrates the model parameters, thus providing increasingly accurate and specific insights over time.

[0034] Such a system includes the function of a central server to control the IoT network. This server is responsible for batch allocation and distributes processing loads among devices based on current workload. It also controls when the model is executed based on average load. It has a resource management unit that tracks the performance of each device and adjusts its operation to maintain load balance.

[0035] The central server also contains a data aggregation module that aggregates refined data from edge devices for in-depth analysis and long-term secure storage. This module integrates with the cloud and allows for the allocation of additional computing resources when computationally more cost-effective analyses are required.

[0036] To ensure data security, the communication module uses the AES-256 encryption protocol to encrypt all transmissions between devices and the server. This module features an authentication key exchange mechanism to ensure security and prevent unauthorized data access. Furthermore, this module features multi-channel communication, enabling simultaneous data transmission over multiple network paths to improve fault tolerance and reliability.

[0037] The system can be used in real-time mode, as data transport can be accelerated and only one communication protocol (low latency) is required, completely minimizing transmission delays. It integrates priority-based routing so that time-critical information can be processed and sent quickly. It also features dynamic data routing, which allows the system to automatically adapt to network conditions to ensure optimal paths are selected for data transmission.

[0038] The invention discloses a well-rounded system for integrating real-time data aggregation and analytics into IoT networks. The system combines edge processing, adaptive machine learning, and secure communications to create a powerful platform that is efficiently scalable and cost-effective for managing IoT data across multiple use cases. It is designed so that data doesn't need to travel far, and data protection is provided directly from the network edge. Ingesting insights saves you latency because not everything needs to be processed in some cloud or whatever. The platform is designed for use cases with low-latency, high-security data solutions, such as smart cities, industrial automation, and healthcare.

[0039] The system implements advanced anomaly detection and quantification techniques, relying on multi-stage stochastic pattern recognition (SPR) and adaptive predictive modeling to deliver reliable insights in IoT environments. It can be used in various fields, such as industrial automation, environmental monitoring, smart cities, and healthcare (where you get complete visibility into what's happening at all times, along with real-time data analytics to help us make a difference). REFERENCES 100 A system for real-time data aggregation and analysis in IoT networks is presented. 102 Local processing unit 104 Centralized Aggregation Server 106 Timestamped metadata indexing unit 108 Analysis Engine 110 Module for Predictive Analytics 112 Communication interface 114 Dynamic Load Balancing Unit

Claims

[1] A system for real-time data aggregation and analysis within an Internet of Things (IoT) network, consisting of: a plurality of IoT sensor nodes configured to collect data from multiple sources, each sensor node comprising a data acquisition unit for collecting data in real time and a local processing unit for pre-processing the collected data by performing data filtering, normalization, and compression prior to transmission; a centralized aggregation server in communication with the plurality of IoT sensor nodes, the aggregation server comprising: a data aggregation module configured to receive, integrate, and synchronize preprocessed data streams from the plurality of sensor nodes, thereby enabling consolidated data storage; a timestamped metadata indexing unit coupled to the data aggregation module and configured to annotate each data entry with metadata, including a unique identifier, a geographic location, and a timestamp; an analytics engine integrated into the aggregation server, which includes: a data processing unit configured to perform real-time analysis of the aggregated data by applying pattern recognition and anomaly detection techniques and to generate processed results based on predefined parameters; a predictive analytics module connected to the data processing unit and configured to perform predictive modeling tasks by extrapolating trends from the historical and real-time data; a communication interface connected to the aggregation server and configured to enable bidirectional data exchange with external monitoring devices, user interfaces, or remote management systems via secure communication protocols; and a dynamic load balancing unit embedded in the aggregation server, configured to dynamically distribute computing resources between the data processing unit and the predictive analytics module depending on the data volume and complexity. [2] The system of claim 1, wherein the local processing unit in each IoT sensor node comprises a noise reduction module specifically configured to reduce interference from ambient noise sources through the use of adaptive filtering techniques. [3] The system of claim 1, wherein the metadata indexing unit is configured to use a hierarchical tagging structure that categorizes data based on predefined metadata levels, enabling efficient querying and retrieval of data subsets by prioritizing tags associated with higher-level criteria, such as data type, source, or severity level. [4] The system of claim 1, wherein the data processing unit within the analytics engine comprises a multi-stage pattern recognition architecture, each stage applying an increasingly refined set of rules or pattern templates calibrated based on historical data from similar IoT networks, thereby optimizing the detection of specific patterns or anomalies unique to the operating environment of the system. [5] The system of claim 1, wherein the predictive analytics module uses an adaptive learning mechanism configured to continuously update its predictive models based on new data inputs, such that it dynamically recalibrates its modeling to maintain prediction accuracy over time, taking into account seasonal, temporal, or usage variations. [6] The system of claim 1, wherein the communication interface further comprises an encryption module configured to secure data packets using multi-layer encryption techniques, thereby ensuring data integrity and confidentiality during transmission. [7] The system of claim 1, wherein the dynamic load balancing unit is configured to monitor compute load metrics in real time and perform preemptive load rebalancing.

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