Adaptive Smart Mesh Topology for Client-Aware Network Reconfiguration
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Solution Overview
Problem
Existing network technologies fail to effectively manage mesh topologies without detailed client or device information, leading to inefficiencies in network performance and scalability.
Innovation Solution
A decision intelligence-based framework for smart mesh topology management that dynamically determines and implements network configurations based on learned traffic patterns, device capabilities, and environmental conditions, ensuring robust, scalable, and adaptive network performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If mesh topology is used to provide robust fault tolerance and redundancy, then network reliability is improved, but network management complexity increases due to lack of detailed client or device information
Solution Approach 1:
The system enables autonomous network nodes to self-configure and self-optimize by collecting local operational data and making independent decisions about routing and topology adjustments. The decision intelligence framework allows nodes to autonomously learn traffic patterns and adapt configurations without requiring centralized manual management, thereby reducing management complexity while maintaining reliability.
Solution Approach 2:
The system implements continuous feedback loops where network nodes collect operational data, analyze performance metrics, and adjust their configurations based on observed traffic patterns and network conditions. This feedback mechanism enables dynamic adaptation to changing conditions, allowing the network to maintain optimal performance and reliability while automatically managing complexity through data-driven decisions.
2Reliability
If nodes are placed physically proximate to maintain robust communication links, then connectivity is improved, but network scalability deteriorates as the area covered by each node is limited
Solution Approach 1:
The system dynamically adjusts network topology and routing configurations based on real-time conditions rather than relying on static physical placements. Nodes can dynamically establish and reestablish connections, adjust transmission parameters, and reconfigure paths to maintain connectivity as the network scales, allowing the network to adapt to growing areas without requiring nodes to be permanently fixed in optimal positions.
Solution Approach 2:
The system transitions from purely physical proximity-based connectivity to a multi-dimensional approach that includes logical routing, protocol-level optimization, and intelligent traffic management. By adding these dimensional layers of control, the network can maintain robust connectivity over extended areas through sophisticated routing decisions rather than relying solely on physical node proximity.
3Productivity
If detailed client or device information is collected to optimize network performance, then network efficiency is improved, but data processing complexity increases
Solution Approach 1:
The system extracts only the essential operational data needed for optimization from the vast amount of available network information. By identifying and collecting only critical parameters such as traffic patterns, connection status, and basic device capabilities, the system reduces data processing complexity while still achieving effective network optimization without needing to process every detail of client or device information.
Solution Approach 2:
The system implements partial data collection and processing, focusing on the most impactful parameters for network optimization rather than attempting to process all available information. This selective approach to data handling maintains network efficiency by processing only necessary information while avoiding the excessive complexity that would result from comprehensive data analysis of all clients and devices.
4Adaptability or versatility
If the network is designed to scale up to accommodate growing nodes, then adaptability is improved, but initial network configuration complexity increases
Solution Approach 1:
The system performs preliminary configuration of scalable networking capabilities during the initial deployment phase, establishing the decision intelligence framework and automated configuration mechanisms before the network fully operates. By pre-configuring these foundational elements, the network is prepared for future growth without requiring complex reconfiguration later, as the scalability infrastructure is already in place and operational.
Data Source
AI summary
Disclosed are systems and methods that provide a computerized network management framework that provides functionality for devices connected to and/or operating in proximity to WiFi networks. The framework can determine and implement functionalities for the design of a network topology, whereby redundant pathways can be ensured by providing multiple paths for data between nodes, enhancing reliability and load balancing. The disclosed framework can enable automatic reconfiguration if a node fails or new nodes are added, such that an automatic topology adaptation to current environments/conditions is provided. The framework provides a mesh solution that is dependent on each location's unique client landscape that provides functionality for customized mechanisms to inject device information into the mesh topology optimization.