Description:1
TITLE: AI-Based Network Optimization
FIELD OF INVENTION: The present invention relates to Computer Science Engineering, specifically AI-based network optimization. It addresses the challenge of enhancing network performance by optimizing resources, reducing latency, and improving bandwidth utilization. This solution provides adaptive frameworks for efficient data flow in dynamic networks, applicable to telecommunications and IT infrastructure.
BACKGROUND OF THE INVENTION: In the modern era, network systems serve as the backbone of communication, data exchange, and digital operations across industries. The increasing reliance on interconnected devices, cloud computing, and real-time applications has created an unprecedented demand for high-speed, reliable, and secure network infrastructures. As technology evolves, so too do the challenges faced by network administrators, including traffic congestion, resource inefficiency, and the growing threat of cyberattacks.
EXISTING CHALLENGES IN NETWORK MANAGEMENT
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Traffic Overload and Congestion: With the exponential growth of data-driven applications and the Internet of Things (IoT), networks often face significant strain during peak usage periods. Traditional static traffic management systems lack the flexibility to adapt dynamically, leading to bottlenecks and degraded performance.
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Resource Allocation Issues: Fixed resource allocation techniques are inefficient in modern heterogeneous networks. These methods fail to cater to varying demands, wasting resources during low-usage periods while leaving critical systems underpowered during surges.
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Downtime and Maintenance Overheads: Unplanned network outages can disrupt operations, leading to financial losses and decreased user satisfaction. Conventional reactive maintenance approaches, which rely on addressing problems after they occur, are inadequate in preventing such disruptions.
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Evolving Cybersecurity Threats: The complexity and scale of modern networks make them prime targets for sophisticated cyberattacks. Static security measures are no longer sufficient to counteract evolving threats, including Distributed Denial of Service (DDoS) attacks, malware, and unauthorized access.
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PRIOR ART:
The advancements in automation, artificial intelligence, and system optimization have been widely applied across various domains, including agriculture, research, and biotechnology. Several patents illustrate innovations in these fields that indirectly or partially align with the principles of intelligent systems and optimization methodologies. However, their scope, objectives, and application differ significantly from the proposed AI-Based Network Optimization system.
1. US20220319165A1: This patent focuses on an advanced agricultural method leveraging a positive air pressure chamber for optimal plant growth conditions. While it employs automation and control mechanisms for managing environmental parameters like temperature, humidity, and light, its application is limited to agriculture. Unlike the proposed invention, it does not involve real-time network analytics, traffic balancing, or AI-driven optimization. Its focus on environmental control and crop management is distinct from network optimization.
2. US10311442B1: This invention addresses automation-assisted research by integrating software modules with domain-specific knowledge bases and automated laboratories. Although it employs automation and AI for enhancing research efficiency, its application is confined to iterative experimentation and data-driven decision-making in research contexts. The proposed invention’s real-time network performance optimization and predictive maintenance capabilities are beyond the scope of this prior art.
3. AU2018336128B2: This patent pertains to genetic modification techniques in plant cells using RNA silencing molecules and DNA editing agents. While it showcases precision and control over gene expression, it is highly specialized in agricultural biotechnology and unrelated to the optimization of data networks or resource allocation.
4. US11692989B2: This invention integrates machine learning and AI for microbiome classification and agricultural recommendations based on DNA sequencing and environmental data. Its focus on agricultural productivity and sustainability differs fundamentally from the proposed system’s objective of optimizing network performance, balancing traffic, and enhancing security.
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OBJECTS OF THE PRESENT INVENTION:
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It is a primary object of the present invention to provide an AI-based network optimization system capable of continuously monitoring and dynamically managing network performance to enhance efficiency and reliability without requiring constant human intervention.
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It is another object of the present invention to employ advanced machine learning algorithms to analyze network traffic patterns, detect inefficiencies, and predict potential performance bottlenecks or failures in real time.
It is another object of the present invention to offer intelligent traffic load balancing mechanisms that optimize data flow, prevent congestion, and ensure consistent high-speed connectivity.
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It is another object of the present invention to incorporate predictive maintenance features, enabling the system to foresee potential failures and proactively implement corrective actions, thereby minimizing downtime.
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It is another object of the present invention to enhance network security by monitoring for unusual activity, detecting potential threats in real time, and deploying automated mitigation strategies to safeguard sensitive data.
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It is another object of the present invention to provide seamless adaptability to existing network infrastructures, ensuring smooth integration and scalability for diverse applications, including telecommunications, cloud computing, and IoT ecosystems.
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It is another object of the present invention to empower stakeholders with real-time analytics and actionable insights, enabling informed decision-making and effective resource management.
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It is another object of the present invention to promote sustainability by optimizing resource utilization within network systems, reducing energy consumption, and supporting environmentally responsible practices.
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SUMMARY OF THE INVENTION :
The following presents a simplified summary of the invention to provide a basic understanding of some aspects of the invention. This summary is not an extensive overview of the present invention and is not intended to identify the key/critical elements or delineate the scope of the invention. Its sole purpose is to present some concepts of the invention in a simplified form as a prelude to a more detailed description presented later.
According to the basic aspect of the present invention, an AI-driven network optimization system is provided to revolutionize the management of modern network infrastructures by leveraging advanced technologies for proactive monitoring, resource allocation, and security enhancement.
According to one aspect of the present invention, the system employs advanced machine learning algorithms to continuously analyze network traffic patterns, identify inefficiencies, and dynamically optimize resource usage for maximum performance.
According to another aspect of the present invention, the system provides intelligent traffic load balancing capabilities, redistributing data flow in real-time to prevent congestion and ensure high-speed, low-latency connectivity.
According to a further aspect of the present invention, the system incorporates predictive maintenance features to foresee potential failures and implement corrective measures proactively, minimizing network downtime and enhancing reliability.
According to another aspect of the present invention, the system enhances security by detecting unusual network behavior, identifying threats, and deploying automated responses to safeguard sensitive data and maintain uninterrupted operations.
According to a further aspect of the present invention, the system integrates seamlessly with existing network infrastructures, offering adaptability and scalability for diverse applications such as telecommunications, cloud computing, and IoT ecosystems.
According to another aspect of the present invention, the system provides stakeholders with real-time data analytics and actionable insights, enabling informed decision-making and efficient network management.
This invention offers a transformative solution for optimizing, securing, and managing modern network systems, ensuring sustainable, reliable, and efficient operations.
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BRIEF DESCRIPTION OF THE ACCOMPANYING DRAWING :
The embodiment of the present invention is illustrated with the help of an accompanying drawing.
Figure 1: This figure illustrates a block diagram of the working modules of the AI-Based Network Optimization system. The diagram includes the following key components:
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Data Collection Module: Responsible for gathering real-time network performance metrics, traffic data, and security logs from various nodes across the network.
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Analytics and Machine Learning Engine: Processes the collected data, identifies patterns, predicts inefficiencies, and optimizes traffic flow using advanced algorithms.
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Traffic Load Balancer: Dynamically redistributes data flow across the network to prevent congestion and ensure optimal performance.
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Predictive Maintenance Module: Monitors system health and forecasts potential failures, triggering preemptive maintenance actions to minimize downtime.
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Security Monitoring Module: Detects unusual activity and potential threats, deploying automated mitigation strategies to protect the network from cyberattacks.
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Stakeholder Dashboard: Provides real-time analytics, actionable insights, and notifications to network administrators, enabling effective management and decision-making.
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Integration Interface: Ensures seamless compatibility with existing network infrastructures and scalability for diverse applications.
Fig 1
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DETAILED DESCRIPTION OF THE INVENTION WITH REFERENCE TO THE ACCOMPANYING DRAWINGS :
The following description is of exemplary embodiments only and is not intended to limit the scope, applicability, or configuration of the invention in any way. Rather, it provides an illustrative framework for implementing the invention. Various modifications to the described embodiments may be made without departing from the principles of the invention.
System Overview The AI-Based Network Optimization system is designed to enhance the efficiency, reliability, and security of modern network infrastructures. Its architecture consists of interconnected modules that dynamically analyze and manage network operations. The system’s modular design facilitates scalability, adaptability, and integration with existing infrastructures.
Figure 1: Block Diagram The accompanying block diagram (Figure 1) provides a visual representation of the system architecture, showcasing the interaction among its key components, including data collection, analytics, traffic balancing, predictive maintenance, security monitoring, and the stakeholder dashboard.
EXEMPLARY EMBODIMENTS:
1. Real-Time Data Collection and Analysis The system’s Data Collection Module gathers performance metrics, traffic logs, and security data from network nodes. These inputs are processed in the Analytics and Machine Learning Engine, which identifies patterns, predicts inefficiencies, and provides actionable insights in real time.
2. Traffic Load Balancing The Traffic Load Balancer dynamically redistributes data flow across the network. By analyzing traffic density and node performance, it prevents congestion and ensures high-speed, low-latency connectivity. This module adapts to varying traffic conditions, optimizing resource allocation.
3. Predictive Maintenance The Predictive Maintenance Module monitors the health of network components. By leveraging machine learning models, it forecasts potential failures based on historical and real-time data. This allows proactive interventions, reducing downtime and maintenance costs.
4. Security Monitoring and Mitigation The Security Monitoring Module continuously evaluates network activity to detect unusual patterns or threats. It employs anomaly detection algorithms to identify potential cyberattacks and deploys automated mitigation strategies, safeguarding sensitive data.
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5. Stakeholder Dashboard and Insights The Stakeholder Dashboard consolidates system analytics, presenting them as real-time visualizations and actionable recommendations. It provides stakeholders with detailed insights into network performance, traffic trends, and security events, empowering informed decision-making.
6. Scalability and Adaptability The system’s Integration Interface ensures compatibility with diverse network infrastructures, including telecommunications, cloud computing, and IoT ecosystems. Its modular design allows for the addition of new functionalities, enabling seamless scalability.
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CLAIMS I/WE CLAIM:
A system for AI-based network optimization, the system comprising:
A central processing unit configured to process real-time network data and control system components.
Data collection modules operatively connected to the system for gathering performance metrics, traffic logs, and security data from network nodes.
An advanced machine learning engine implemented on the system to analyze data, predict inefficiencies, and optimize network performance dynamically.
A traffic load balancer for redistributing data flow across the network to prevent congestion and ensure high-speed, low-latency performance.
A security monitoring module for detecting unusual activity and deploying automated mitigation strategies against cyber threats.
Communication modules for transmitting data to cloud servers for long-term storage and advanced analysis.
Characterized in that the system provides real-time monitoring, automated optimization, and enhanced security, enabling proactive network management with minimal human intervention.
The system as claimed in claim 1, wherein the machine learning engine uses predictive algorithms to analyze traffic patterns, resource utilization, and potential inefficiencies, enabling adaptive optimization of network performance.
The system as claimed in claim 1, wherein the traffic load balancer dynamically allocates resources across the network based on real-time data to minimize latency and maximize throughput.
The system as claimed in claim 1, wherein the predictive maintenance module processes historical and real-time data to identify potential component failures and triggers alerts or actions to prevent network downtime.
The system as claimed in claim 1, wherein the security monitoring module uses anomaly detection techniques to identify and mitigate potential cyber threats in real time.
The system as claimed in claim 1, wherein the modular architecture allows for scalability and integration with additional functionalities or devices, ensuring adaptability to evolving network requirements.
The system as claimed in claim 1, wherein the stakeholder dashboard displays key performance indicators, trends, and alerts, providing actionable insights to network administrators for enhanced management.
The system as claimed in claim 1, wherein the communication modules facilitate secure data transmission between the system and cloud servers, enabling remote access, storage, and advanced analytics.
ABSTRACT OF THE INVENTION TITLE: AI-BASED NETWORK OPTIMIZATION
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The present invention introduces an advanced system designed to revolutionize network management using artificial intelligence (AI). This system leverages real-time data analytics and machine learning to monitor network performance continuously, detect inefficiencies, and proactively optimize operations. By analyzing traffic patterns, resource utilization, and potential bottlenecks, the system dynamically balances traffic loads, allocates resources efficiently, and reroutes data to prevent congestion, ensuring high-speed, low-latency performance.
Additionally, the system integrates predictive capabilities to foresee potential failures and initiate preemptive maintenance, minimizing downtime and enhancing operational resilience. It also includes robust security monitoring, capable of identifying and mitigating threats in real time through anomaly detection. , Claims:A system for AI-based network optimization, the system comprising:
A central processing unit configured to process real-time network data and control system components.
Data collection modules operatively connected to the system for gathering performance metrics, traffic logs, and security data from network nodes.
An advanced machine learning engine implemented on the system to analyze data, predict inefficiencies, and optimize network performance dynamically.
A traffic load balancer for redistributing data flow across the network to prevent congestion and ensure high-speed, low-latency performance.
A security monitoring module for detecting unusual activity and deploying automated mitigation strategies against cyber threats.
Communication modules for transmitting data to cloud servers for long-term storage and advanced analysis.
Characterized in that the system provides real-time monitoring, automated optimization, and enhanced security, enabling proactive network management with minimal human intervention.
The system as claimed in claim 1, wherein the machine learning engine uses predictive algorithms to analyze traffic patterns, resource utilization, and potential inefficiencies, enabling adaptive optimization of network performance.
The system as claimed in claim 1, wherein the traffic load balancer dynamically allocates resources across the network based on real-time data to minimize latency and maximize throughput.
The system as claimed in claim 1, wherein the predictive maintenance module processes historical and real-time data to identify potential component failures and triggers alerts or actions to prevent network downtime.
The system as claimed in claim 1, wherein the security monitoring module uses anomaly detection techniques to identify and mitigate potential cyber threats in real time.
The system as claimed in claim 1, wherein the modular architecture allows for scalability and integration with additional functionalities or devices, ensuring adaptability to evolving network requirements.
The system as claimed in claim 1, wherein the stakeholder dashboard displays key performance indicators, trends, and alerts, providing actionable insights to network administrators for enhanced management.
The system as claimed in claim 1, wherein the communication modules facilitate secure data transmission between the system and cloud servers, enabling remote access, storage, and advanced analytics.