Mesh Network Wake-Up Control Using AI and Device-Specific Profiles
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Solution Overview
Problem
Mesh networks face challenges such as communication congestion, inefficient energy consumption, and complexity in managing devices with varying roles and priorities due to traditional wake-up systems relying on pre-determined schedules or manual activation protocols, especially in dynamic environments.
Innovation Solution
Adaptive network management using AI-driven predictive algorithms, adaptive transmission protocols, multi-layer signal verification, and device-specific wake-up profiles to optimize the wake-up process, ensuring efficient and reliable network operations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If traditional pre-determined schedules or manual activation protocols are used for wake-up systems, then device activation can be controlled, but unnecessary device activations occur, causing network congestion and excessive power consumption
Solution Approach 1:
The patent implements dynamic wake-up scheduling that adapts to changing network conditions and device states. The system transitions from static pre-determined schedules to dynamic algorithms that continuously optimize wake-up times based on real-time network traffic patterns, device battery levels, and communication priorities, thereby reducing unnecessary activations and energy consumption while maintaining network efficiency
Solution Approach 2:
The patent incorporates feedback mechanisms where the network monitors actual device activation patterns, network congestion levels, and energy consumption data. This feedback is used to refine and adjust the wake-up scheduling algorithm continuously, creating a closed-loop system that learns from past performance and optimizes future wake-up decisions to minimize energy waste while ensuring reliable communication
2Adaptability or versatility
If traditional wake-up systems are used in dynamic environments, then simple activation protocols can be maintained, but they cannot adapt to changing network conditions and device roles
Solution Approach 1:
The patent implements preliminary actions by pre-configuring device wake-up profiles and priority levels during the network setup phase. Each device is assigned a profile that includes its typical activation patterns, energy constraints, and communication requirements. This preliminary configuration enables the dynamic scheduling algorithm to make intelligent decisions without requiring complex real-time analysis, thus achieving adaptability while managing complexity
Solution Approach 2:
The patent applies local quality by customizing wake-up parameters for individual devices based on their specific roles, battery levels, and communication needs. Instead of a uniform wake-up schedule for all devices, the system creates device-specific activation strategies that optimize performance for each node's unique characteristics, enabling fine-grained adaptability in dynamic environments
3Reliability
If AI-driven predictive algorithms are implemented for wake-up optimization, then energy efficiency and network reliability are improved, but system complexity and computational requirements increase
Solution Approach 1:
The patent applies partial action by implementing AI-driven predictive algorithms selectively rather than universally. The system uses machine learning models to forecast network conditions and optimize wake-up schedules for critical devices or time periods, while maintaining simpler scheduling mechanisms for less critical scenarios. This partial application of AI complexity achieves improved reliability for key communication paths without unnecessarily increasing system-wide complexity
Data Source
AI summary
This invention provides an adaptive network management system for mesh networks, utilizing advanced wake-up mechanisms that include AI-driven predictive algorithms, adaptive transmission protocols, multi-layer verification processes, and device-specific wake-up profiles. The system is designed to improve network efficiency, reduce latency, and enhance energy management by selectively waking up devices based on real-time conditions, predefined schedules, or a combination of both. This system is applicable to a variety of fields, including military communications, industrial automation, and smart grids, where reliable and efficient network management is critical.