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 security due to reliance on centralized cloud computing, which can lead to network latency and potential data privacy issues during image processing.
Innovation Solution
Implementing a multi-tier distributed object recognition application (DORA) that utilizes computing resources close to the image capture location, with a spatial index partitioned across camera-proximity devices, reducing the need for extensive network transfers and enabling rapid recognition through local caching and targeted index searches.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Power
If centralized cloud computing is used for object recognition, then high computational power and model complexity are achieved, but network latency increases and data security concerns arise
Solution Approach 1:
The patent segments the object recognition system into multiple distributed edge devices, each capable of independently performing recognition tasks. This segmentation allows computational tasks to be distributed across multiple nodes rather than centralized in the cloud, thereby reducing network latency while maintaining high computational power through parallel processing capabilities across the edge device network.
2Adaptability or versatility
If centralized cloud computing is used for object recognition, then sophisticated machine learning models can be run, but data privacy and security concerns increase due to frequent data transfers
Solution Approach 1:
The patent extracts the object recognition computation from the centralized cloud environment and places it directly on edge devices located at or near the data source. This extraction eliminates the need for frequent data transfers to and from centralized cloud servers, thereby reducing data security risks and privacy concerns while still enabling sophisticated machine learning models to be executed locally on the edge devices.
3Loss of energy
If distributed edge computing is implemented, then network bandwidth usage is reduced and latency is minimized, but device complexity increases
Solution Approach 1:
The patent merges the object recognition computation capabilities directly into the edge devices, combining data capture, processing, and recognition functions in a single distributed system. This merging reduces the need for extensive network bandwidth for data transfers while the modular architecture of the distributed edge devices keeps individual device complexity manageable, with each device performing specialized recognition tasks.
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.