Edge Video Search Using Distributed Caching and Selective Transfer
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Solution Overview
Problem
Internet-of-things (IoT) applications face challenges with bandwidth, backhaul, and storage limitations, making it difficult to efficiently search and retrieve data captured by connected devices, particularly in scenarios like intelligent driver monitoring systems and smart surveillance, where data accessibility and transmission are hindered by these constraints.
Innovation Solution
The implementation of distributed video storage and search systems utilizing edge computing, which involves caching data on devices, determining data relevance, and transmitting only necessary data, along with intelligent pre-selection and variable rate throughput mechanisms, to optimize bandwidth and storage usage while ensuring security and privacy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If video data is transmitted from connected devices to centralized servers, then data accessibility is improved, but bandwidth and backhaul limitations are exceeded
Solution Approach 1:
The patent segments the centralized video storage and processing system into distributed edge devices that store and process video data locally. Each edge device maintains video data in its own memory, eliminating the need to transmit all video data through centralized servers. This segmentation reduces bandwidth utilization while maintaining data accessibility through local storage and selective transmission of only relevant data portions.
Solution Approach 2:
The patent introduces a new dimension of distributed architecture by deploying edge devices across multiple locations rather than relying on a single centralized server. This dimensional shift from centralized to distributed storage enables parallel data access points, reducing the load on any single network path while improving overall data accessibility.
2Ease of operation
If all video data is stored in centralized servers, then data retrieval is simplified, but storage limitations are exceeded
Solution Approach 1:
The patent divides the storage burden across multiple edge devices, each maintaining video data locally in its own memory. This segmentation of storage responsibilities eliminates the need for a single centralized server to handle all video data, thereby overcoming storage capacity limitations while enabling data retrieval through local access at each edge device.
Solution Approach 2:
Each edge device independently stores and manages its own video data in local memory, performing self-service storage operations without requiring centralized server intervention. This self-service approach enables autonomous data retrieval at the edge level, simplifying operations while distributing storage demands across multiple devices.
3Loss of information
If video data is transmitted over the network, then data accessibility is improved, but bandwidth constraints are violated
Solution Approach 1:
The patent extracts video data from the network transmission path by storing it locally in edge device memory. Instead of transmitting all video data through the network, the system extracts and retains video data at the source edge devices, eliminating unnecessary network traffic and reducing bandwidth consumption while maintaining data accessibility through local storage.
Solution Approach 2:
The patent performs preliminary storage of video data in edge device memory before any potential retrieval or transmission needs arise. This preliminary action of local caching ensures that video data is immediately accessible without requiring network transmission, thereby reducing bandwidth consumption while maintaining rapid data accessibility when needed.
4Device complexity
If centralized processing is used, then system management is simplified, but computational resource utilization increases
Solution Approach 1:
The patent segments computational processing tasks from centralized servers to distributed edge devices. Each edge device independently performs local processing of its stored video data, eliminating the need for centralized computational resources to handle all video processing. This segmentation reduces overall computational resource utilization while distributing system management across multiple autonomous devices.
Solution Approach 2:
Each edge device autonomously processes its own stored video data locally without requiring centralized server intervention. This self-service processing approach eliminates unnecessary computational overhead at centralized servers, reducing overall power consumption and computational resource utilization while maintaining system functionality through distributed autonomous processing.
Data Source
AI summary
Systems and methods are provided for distributed video storage and search with edge computing. The method may comprise caching a first portion of data on a first device. The method may further comprise determining, at a second device, whether the first device has the first portion of data. The determining may be based on whether the first piece of data satisfies a specified criterion. The method may further comprise sending the data, or a portion of the data, and/or a representation of the data from the first device to a third device.