Edge Computing Subsystem for Remote Worker Proctoring
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Solution Overview
Problem
Existing cloud-centric remote proctoring solutions for securing data in remote working environments require high network bandwidth, storage, and on-site personnel, making them inefficient and costly for secure data protection.
Innovation Solution
A system utilizing edge computing with a processing subsystem on a server and an edge computing subsystem on a peripheral device for real-time identity verification, activity monitoring, alert generation, and logging using Blockchain for secure data protection, which reduces bandwidth and storage needs by processing data locally and masking sensitive information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If cloud-centric remote proctoring solutions are used to monitor and secure data, then data security and compliance are improved, but network bandwidth requirements and storage demands increase significantly
Solution Approach 1:
The patent implements edge computing by deploying AI models and processing capabilities directly on the user's local device rather than relying solely on cloud infrastructure. This allows video streaming and proctoring functions to be processed locally, reducing the network bandwidth required for transmitting raw video data to centralized cloud servers while maintaining security and compliance monitoring capabilities.
2Reliability
If cloud-centric remote proctoring solutions are used to monitor and secure data, then data security and compliance are improved, but storage requirements for archiving increase significantly
Solution Approach 1:
The patent extracts and processes only the essential and relevant information from video streams locally using AI models, rather than archiving complete high-definition video feeds in the cloud. This selective extraction of critical data points and events significantly reduces the storage capacity required while maintaining the ability to audit and secure data through proctoring.
3Reliability
If on-site personnel are deployed to monitor remote workers, then data security is improved, but operational costs and system complexity increase
Solution Approach 1:
The patent replaces the mechanical system of human on-site personnel with an automated AI-based proctoring system that uses computer vision and machine learning models to monitor remote workers. This substitution eliminates the need for physical presence while maintaining security monitoring, thereby reducing operational costs and simplifying the system by removing the layer of human resource management and coordination.
4Speed
If high-speed and highly available network connections are required for cloud proctoring, then real-time monitoring capability is improved, but system accessibility and deployment flexibility are reduced
Solution Approach 1:
The patent implements preliminary action by pre-loading and caching AI models, reference data, and processing algorithms on the local device before remote work begins. This allows the system to function with minimal real-time network connectivity, as the bulk of processing occurs offline using pre-configured resources. The system can synchronize updates and archived data when connectivity is available, but does not require continuous high-speed connections for core proctoring functions.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This solution enhances data security by reducing bandwidth and storage requirements, automating verification processes, and minimizing overhead costs while maintaining employee privacy through hyper-personalized digital workspace management.
Implementation Method 1
The activity monitoring module is also configured to identify one or more suspicious activities from the plurality of activities by processing the streaming data collected on the peripheral edge computing device using one or more image processing techniques
Implementation Method 2
The edge computing subsystem includes a logging module configured to record the one or more suspicious activities and store one or more recorded suspicious activities in the server using Blockchain for audit and traceability purpose
Data Source
AI summary
A system for securing data is disclosed. The system includes a processing subsystem including a connection module to evaluate a computing device corresponding to remote workers for compatibility with a peripheral edge computing device, the computing device is enabled with an edge assisted proctoring service. The system includes an edge computing subsystem including an authentication module to verify an identity of the remote workers on the computing device using verification processes. The edge computing subsystem includes an activity monitoring module to monitor activities of the remote workers by collecting streaming data in real-time on the peripheral edge computing device. The activity monitoring module identifies suspicious activities by processing the streaming data. The edge computing subsystem includes an alert generation module to generate an alert upon identifying the suspicious activities. The edge computing subsystem includes a logging module to record the suspicious activities and store it in the server using Blockchain.