Parity-Based Redundant Video Storage Among Networked Cameras
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Network-based video surveillance systems face challenges in reliable and cost-efficient redundant storage of video data on edge devices, as existing solutions either require doubling storage capacity or are prone to data loss due to device failures or theft, and existing methods for parity-based redundant storage are inefficient.
Innovation Solution
A system and method for parity-based redundant video storage among networked video cameras, where each camera group calculates and stores parity data, distributes backup data among cameras, and securely sends it to a storage server, utilizing variable compression and chunk synchronization to manage data efficiently.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If simple replication (doubling storage requirements) is used for redundant in-camera storage, then data reliability is improved, but storage cost and system complexity increase
Solution Approach 1:
The patent combines multiple cameras' video data into a single parity group, where parity data is calculated across all cameras in the group rather than maintaining separate redundant copies for each camera. This merging approach allows the system to achieve the same reliability as simple replication but with significantly reduced storage requirements, as the parity data serves multiple cameras simultaneously.
Solution Approach 2:
The patent changes the storage parameter from full video data copies to compressed parity data. By transforming the redundancy mechanism from storing complete video streams to storing calculated parity values, the system achieves equivalent fault tolerance with much smaller storage capacity, directly resolving the contradiction between reliability and storage quantity.
2Reliability
If in-camera storage is increased for redundant storage, then data loss due to device failure is reduced, but cost and system engineering complexity increase
Solution Approach 1:
The patent implements self-service by enabling each camera to autonomously calculate and store its own parity data locally in its non-volatile memory, rather than requiring a centralized server to manage all redundancy operations. This distributed self-service approach reduces system engineering complexity while maintaining robust data protection against device failures.
Solution Approach 2:
The patent segments the redundancy management function into individual camera operations, where each camera independently calculates and stores parity data for its own video streams. This segmentation distributes the complexity across multiple simple units rather than concentrating it in a single complex system, reducing overall system engineering complexity while achieving reliable data protection.
3Quantity of substance
If parity-based redundant storage is implemented efficiently, then storage cost is reduced, but data recovery capability must be maintained
Solution Approach 1:
The patent introduces a server as an intermediary that coordinates parity data collection and distribution among cameras. The server receives video data from multiple cameras, calculates parity data, and distributes it back to the cameras for local storage. This intermediary mechanism enables efficient space utilization while ensuring data recovery capability is maintained through proper parity data management and distribution.
Data Source
AI summary
Systems and methods for redundant storage among networked video cameras are described. Video data for a group of video cameras is received by a parity video camera. The parity video camera calculates parity across the peer video data, stores the parity data to one storage location and backup video data for the storage location to another storage location. In some examples, the storage locations are selected from among the non-volatile memory of the group of video cameras or another group of video cameras.