Distributed Handwriting Recognition Servers for Edge Security
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Solution Overview
Problem
The increasing number of IoT devices and edge computing applications leads to increased network requirements, causing latency and higher bandwidth costs in cloud-based handwriting recognition services, which can compromise security and accuracy.
Innovation Solution
Deploying multiple handwriting recognition servers across different security levels within a computing environment, utilizing edge computing to process handwriting recognition requests closer to the device, thereby reducing network communication delays and enhancing security and accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If handwriting recognition requests are processed through centralized cloud servers, then comprehensive processing capability is achieved, but network latency and bandwidth costs increase
Solution Approach 1:
The patent segments the centralized handwriting recognition service into distributed edge computing nodes deployed across multiple locations. Each edge server locally processes handwriting recognition requests, eliminating the need for all requests to travel to a central cloud server. This segmentation reduces network latency while maintaining recognition accuracy through distributed processing capabilities.
Solution Approach 2:
The patent introduces a spatial dimension to the processing architecture by deploying servers at edge locations geographically closer to end users. This dimensional change from centralized to distributed deployment reduces the physical distance data must travel, thereby reducing network latency without compromising processing capability.
2Reliability
If multiple handwriting recognition servers are deployed across different security levels, then security and accuracy are enhanced, but system complexity increases
Solution Approach 1:
The patent applies local quality by deploying handwriting recognition servers with different security clearance levels at specific edge locations based on local security requirements. Each server is configured with appropriate security credentials for its designated environment, allowing the system to meet varying security demands without requiring all servers to have maximum complexity.
Solution Approach 2:
The patent implements dynamic server deployment where handwriting recognition servers can be selectively activated or deactivated based on security level requirements and workload demands. This dynamic approach allows the system to scale complexity only when and where needed, rather than maintaining maximum complexity across all deployed servers continuously.
3Loss of energy
If edge computing is utilized to process requests closer to devices, then network bandwidth costs are reduced, but infrastructure deployment complexity increases
Solution Approach 1:
The patent designs edge servers with multi-functionality, allowing them to handle multiple tasks including handwriting recognition, general-purpose computing, and data processing. This universal approach enables the same infrastructure to serve multiple purposes, reducing the need for specialized equipment and lowering overall infrastructure complexity while still achieving bandwidth cost savings through local processing.
Data Source
AI summary
Deploying handwriting recognition servers of different security levels is provided. The process includes establishing multiple security levels of processing handwriting recognition requests, and providing multiple handwriting recognition servers. A handwriting recognition server of the multiple handwriting recognition servers facilitates handwriting recognition analysis processing for a respective security level of the multiple security levels of processing handwriting recognition requests. The process further includes deploying the multiple handwriting recognition servers to multiple computing resources of a computing environment for processing respective security-level handwriting recognition requests.