Distributed Edge Computing for Real-Time Cooperative AR Sensing
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Solution Overview
Problem
Challenges in deploying comprehensive cooperative augmented reality systems include complexities in data processing, communication, and system scalability, particularly in handling large volumes of data from mobile sensors, which are critical for immersive experiences.
Innovation Solution
A system and method utilizing distributed edge computing with a service-centric distributed resource-aware architecture (SCDRA) that integrates HoloLens nodes, sensor nodes, and edge servers connected via a wireless network, using Apache Storm for distributed stream processing to manage and process data streams, including object detection and 3D model reconstruction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If distributed edge computing is implemented to process large volumes of sensor data, then real-time processing capability and reduced latency are improved, but system complexity and deployment challenges increase
Solution Approach 1:
The system segments processing tasks by deploying Apache Storm distributed stream processing across multiple edge servers, dividing the computational workload into manageable micro-tasks that can be processed independently in real-time, thereby improving processing capability while maintaining manageable system complexity through modular architecture
Solution Approach 2:
The patent introduces Apache Storm as an intermediary stream processing platform between sensor nodes and edge servers, mediating the complex data flow and processing operations. This intermediary layer simplifies deployment by providing a standardized processing framework that handles data routing, task distribution, and coordination across the distributed edge computing infrastructure
2Adaptability or versatility
If cooperative AR systems integrate multiple HoloLens nodes and sensor nodes, then immersive experience and situational awareness are improved, but communication complexity and data coordination challenges increase
Solution Approach 1:
The system merges data streams from multiple HoloLens nodes and sensor nodes into a unified processing pipeline using Apache Storm. This consolidation approach improves cooperative AR experience by integrating diverse sensor inputs while simplifying communication complexity through centralized stream processing that automatically coordinates data flow between all nodes
Solution Approach 2:
The Apache Storm platform provides universal processing capabilities that handle multiple types of data streams (video, depth, inertial, environmental sensors) through a single unified framework. This multi-functional approach enhances cooperative AR versatility while reducing communication complexity by providing a standardized interface for all node interactions
3Speed
If computationally intensive tasks are offloaded to edge servers, then processing speed and reduced latency are improved, but network bandwidth usage and transmission risks increase
Solution Approach 1:
The system implements local processing at edge servers where data is received and processed in real-time using Apache Storm. This local quality approach improves processing speed by eliminating the need to transmit data to remote cloud servers, while simultaneously reducing network bandwidth consumption by keeping computational operations localized at the edge infrastructure
Data Source
AI summary
The present disclosure provides a system of distributed edge computing for cooperative augmented reality with mobile sensing capability. The system includes a plurality of nodes configured to generate a plurality of data streams; and a plurality of distributed edge servers configured to process one or more tasks using the plurality of data streams. An Apache Storm distributed stream processing platform is installed and properly configured on each distributed edge server; the plurality of distributed edge servers includes one or more service modules installed on each distributed edge server and configured to process the one or more tasks; and the plurality of distributed edge servers includes a master distributed edge server and a plurality of slave distributed edge servers; and a scheduler is installed on the master distributed edge server and configured to distribute the one or more tasks to the plurality of distributed edge servers.