Edge Framework for Distributed Task Coordination
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing automated systems face challenges in efficiently coordinating and controlling interconnected modules and hardware components, particularly in scenarios where data volume increases, leading to burdensome cloud-based processing.
Innovation Solution
A system and method that utilize a broadcasting computing entity to announce its functional capabilities and receive subscriptions from listening computing entities, allowing for cooperative performance of software solutions by distributing processing tasks across multiple computing entities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Power
If cloud-based computing entities are used to coordinate and control interconnected modules, then processing capability is improved, but network bandwidth requirements and data transmission burden increase
Solution Approach 1:
The system segments processing tasks between edge hubs and cloud-based computing entities. Each edge hub autonomously processes local sensor data and controls local actuators, while only exchanging essential information with the cloud. This segmentation reduces network bandwidth consumption by keeping routine processing local while maintaining cloud connectivity for higher-level coordination.
Solution Approach 2:
The patent introduces a hierarchical dimension to the computing architecture, with edge hubs operating at the network edge level and cloud entities operating at the central level. This dimensional organization allows processing to occur at multiple levels simultaneously, reducing the need for constant cloud-edge data transmission while maintaining overall system coordination.
2Ease of operation
If fully-cloud-based systems are used, then centralized control is improved, but local processing capability and independence deteriorate
Solution Approach 1:
The system divides control functions into local and remote segments. Edge hubs execute local control logic independently for immediate responses, while cloud entities provide centralized coordination for cross-subsystem operations. This segmentation enables both autonomous local processing and centralized control to coexist effectively.
Solution Approach 2:
Edge hubs are pre-configured with local processing capabilities and control logic before deployment. This preliminary action enables them to autonomously process sensor data and control actuators without requiring real-time cloud intervention, improving local productivity while maintaining centralized oversight.
3Adaptability or versatility
If edge hubs process all local data independently, then local autonomy is improved, but coordination with cloud services and other subsystems deteriorates
Solution Approach 1:
Edge hubs are designed with multi-functionality, combining local autonomous processing with standardized cloud communication capabilities. They can independently process local data while also serving as gateways to cloud services, eliminating the need for separate coordination mechanisms and reducing overall system complexity.
Solution Approach 2:
Edge hubs act as intermediaries between local subsystems and cloud services. They translate between local processing requirements and cloud communication protocols, simplifying coordination by providing a standardized interface that handles the complexity of cross-subsystem and cloud-edge communication.
4Adaptability or versatility
If custom code is developed for each automated system, then system-specific functionality is improved, but development time and complexity increase
Solution Approach 1:
The system employs a universal software framework that runs on edge hubs, providing common processing capabilities, communication protocols, and control logic. This framework eliminates the need to develop custom code for basic functions, reducing development time while maintaining the ability to configure system-specific behavior through parameters and configurations rather than code.
Data Source
AI summary
A build of a software solution that is cooperatively performed is automated. A broadcasting computing entity broadcasts an announcement of the functional operation of which the broadcasting computing entity is capable. Each of a plurality of listening computing entities connected to the network receives the announcement and compares the functional operation with a list of operational needs. After determining itself in need of performance of such a functional operation, a subscribing one of the plurality of listening computing entities transmits a response to the request indicating subscription to the output of the functional operation. The broadcasting computing entity then performs the functional operation, thereby generating and transmitting to the subscribing one of the plurality of listening computing entities the output of the functional operation. The subscribing one of the plurality of listening computing entities then performs an action using the output of the functional operation received.