Distributed Wireless Edge Computing Mesh for Low-Latency Processing
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Solution Overview
Problem
Existing centralized cluster computing systems face challenges in configuration, scalability, and integration with disparate systems, leading to high latency, costly infrastructure requirements, and reliance on specialized experts for maintenance, especially in remote or resource-constrained environments.
Innovation Solution
A distributed wireless computation platform that provides modular, extensible, and operating system-agnostic edge computing, enabling seamless integration, on-demand scaling, and rugged hardware for low-latency decision-making, using a mesh radio network with automatic discovery and management of computing resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Power
If centralized cluster computing systems are used, then sufficient processing power is provided, but latency increases and network infrastructure complexity increases
Solution Approach 1:
The patent segments the centralized computing system into distributed edge computing nodes deployed at multiple locations. Each edge node independently processes data locally, eliminating the need to transmit all data to a central server. This segmentation reduces latency by enabling parallel processing across distributed nodes while maintaining sufficient aggregate processing power through the collective capability of multiple edge devices.
2Power
If centralized cluster computing systems are used, then processing power is provided, but device complexity and infrastructure cost increase
Solution Approach 1:
The patent employs universal edge computing nodes that can be deployed at various locations and perform multiple functions including data processing, machine learning inference, and local data storage. These multi-functional nodes eliminate the need for separate specialized hardware components and reduce overall infrastructure complexity while providing sufficient processing power through software-defined computing capabilities.
Solution Approach 2:
The system implements self-service capabilities where edge nodes automatically discover each other, self-configure, and self-manage without requiring complex centralized administration. The nodes autonomously handle task allocation, resource management, and fault tolerance, significantly reducing the operational complexity of the infrastructure while maintaining high processing capacity.
3Loss of time
If existing edge computing systems are used, then latency is reduced, but ease of operation and scalability worsen
Solution Approach 1:
The patent implements self-service mechanisms where edge nodes automatically discover each other through peer-to-peer communication, self-configure network parameters, and self-manage computing resources without requiring manual intervention from operators. This automation maintains low latency through distributed processing while dramatically improving ease of operation by eliminating complex configuration tasks.
4Loss of time
If existing edge computing systems are used, then low-latency decision-making is enabled, but adaptability to different operating systems and hardware worsens
Solution Approach 1:
The patent employs a universal software-defined computing platform that can operate on diverse hardware architectures and operating systems through standardized virtualization and containerization technologies. This universality enables low-latency decision-making through distributed processing while maintaining high adaptability to different hardware platforms, operating systems, and hardware accelerators through abstracted, hardware-agnostic software layers.
Data Source
AI summary
A distributed computing platform that includes software solutions for managing distributed hardware devices that form a mesh communications infrastructure. Each hardware device provides an endpoint for user devices (e.g., sensors) and includes the processing power, expandable storage, and network communications capability required to perform cluster computing at the edge. The disclosed software solutions enable each hardware device to be automatically discoverable, seamlessly integrate additional hardware devices to the cluster, and enable end users to easily allocate the available computing resources. Accordingly, the disclosed distributed computing platform minimizes the need for onsite technical support while making complex preprocessing economically feasible for organizations that require distributed computing at the edge.