Distributed Entity Recognition With Edge Caching and Spatial Indexing
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Solution Overview
Problem
Existing object recognition systems face challenges in meeting stringent latency requirements and ensuring data privacy and security due to the reliance on centralized cloud computing and high network latency, which can compromise performance and data security.
Innovation Solution
Implementing a multi-tier distributed object recognition application (DORA) that utilizes camera-proximity devices with local caching and partitioned spatial indices, allowing computations to be performed closer to the data source, reducing latency and minimizing sensitive data transfer over public networks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Power
If object recognition is performed using centralized cloud computing, then high computational power and model complexity can be achieved, but network latency increases and recognition speed decreases
Solution Approach 1:
The system segments the object recognition workload by distributing computing resources across multiple tiers: edge devices (camera-proximity devices), intermediate servers, and cloud data centers. Each tier performs recognition tasks locally when possible, reducing the need for data to traverse the entire network and thereby reducing latency while maintaining computational capability.
Solution Approach 2:
The patent introduces a spatial dimension to the computing architecture by deploying recognition models at multiple geographical locations (edge devices close to cameras, intermediate servers, and cloud data centers). This multi-dimensional deployment allows the system to process data closer to its source, reducing network travel time while preserving access to powerful centralized models when needed.
2Extent of automation
If video and image data are transferred over the public Internet to distant data centers, then centralized processing can be achieved, but data privacy and security concerns increase
Solution Approach 1:
The system segments data processing across multiple locations, allowing sensitive data to remain processed locally at edge devices and intermediate servers whenever possible. Only necessary results or aggregated data are transmitted to centralized data centers, reducing the exposure of sensitive information to network risks while maintaining centralized coordination and model management.
Solution Approach 2:
The patent introduces intermediate servers as mediators between edge devices and centralized data centers. These intermediaries perform preprocessing and initial recognition tasks, filtering and reducing the amount of sensitive data that needs to be transmitted over public networks to centralized systems, thereby protecting data privacy while enabling centralized processing capabilities.
3Loss of time
If distributed computing resources are used closer to data sources, then latency is reduced and performance improves, but system complexity increases
Solution Approach 1:
The system implements dynamic task routing and model selection capabilities that automatically determine the optimal tier for processing each recognition task based on current conditions. This dynamic approach allows the system to adapt to varying workloads and network conditions, managing complexity through intelligent automation rather than rigid static architecture.
Solution Approach 2:
The patent designs each tier of the distributed system to perform multiple functions: edge devices handle both local processing and data collection, intermediate servers provide both caching and preprocessing, and cloud systems offer both centralized coordination and heavy computation. This multi-functionality reduces overall system complexity by eliminating the need for dedicated specialized components for each function.
Data Source
AI summary
A first encoding representing a set of detected signals is obtained at a sensor-proximity resource of an object recognition application which also includes resources of an analytics service of a provider network. In response to a determination that a cache at the sensor-proximity resource does not include a second encoding which satisfies a similarity criterion with respect to the first encoding, at least a portion of a partition of a spatial index is obtained from another resource selected using an index partition map. A recognition-based action is initiated based on determining that the partition includes an encoding which satisfies the similarity criterion.