Edge Data Synchronization Using Feedback Loops and Node Dependencies
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Traditional methods for data synchronization in industrial machines, vehicles, and devices are inefficient, as they fail to provide timely and effective data updates, often discarding data points without proper synchronization.
Innovation Solution
The system employs a synchronizer that operates in real-time or near real-time to manage multiple actor nodes in a network, ensuring timely data synchronization, normalization, and dynamic updating of data sets by configuring dependencies and managing node roles and sequences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional data synchronization methods are used, then data can be collected from multiple sources, but data updates are delayed and data points are discarded without proper synchronization
Solution Approach 1:
The system performs preliminary actions by pre-configuring synchronization rules, data dependencies, and node relationships before data collection begins. The configuration manager sets up the entire synchronization framework in advance, including which nodes depend on which data sources and what actions to take when data changes occur. This preliminary setup enables real-time automated synchronization without delays during actual data updates.
Solution Approach 2:
The system implements continuous feedback mechanisms where the synchronizer monitors data changes across all actor nodes in real-time. When any node detects a data update, it immediately notifies other dependent nodes through this feedback loop, triggering automatic synchronization actions. This real-time feedback eliminates data update delays and ensures all nodes have access to the most current data without manual intervention.
2Loss of information
If data is frequently updated across multiple nodes, then data currency is improved, but system complexity and resource consumption increase
Solution Approach 1:
The system segments the synchronization functionality into distinct modular components: the configuration manager handles setup and rule definition, while the synchronizer manages real-time execution. Actor nodes are further segmented into specialized roles (producer nodes that generate data, consumer nodes that use data, and intermediary nodes that facilitate communication). This segmentation allows each component to operate independently with clearly defined responsibilities, maintaining high data currency without overwhelming system complexity.
Solution Approach 2:
The synchronizer is designed as a universal component that can handle multiple synchronization scenarios and data types through a single unified interface. It manages various node types (producers, consumers, intermediaries) and implements different synchronization strategies (push, pull, event-driven) without requiring separate systems. This multi-functionality maintains data currency across diverse nodes while avoiding the complexity of multiple specialized synchronization systems.
3Productivity
If real-time data synchronization is implemented, then system efficiency is improved, but computational overhead and resource usage increase
Solution Approach 1:
The system applies partial action by implementing selective synchronization that targets only the specific data points and nodes that require updates, rather than synchronizing all data across all nodes universally. The configuration manager enables fine-grained control over which nodes depend on which data sources, allowing the synchronizer to perform minimal necessary synchronization actions. This approach maintains system efficiency by avoiding unnecessary computational overhead while ensuring critical nodes receive timely data updates.
4Reliability
If multiple actor nodes are managed with dependencies, then data coordination is improved, but configuration and management difficulty increases
Solution Approach 1:
The system implements self-service mechanisms where actor nodes automatically discover and establish their dependencies on data sources and other nodes based on pre-configured rules. When a new node joins the network or existing nodes are added, the synchronizer automatically detects relationships and configures synchronization parameters without manual intervention. This automation maintains reliable data coordination across complex multi-node systems while significantly reducing the operational burden of manual configuration and management.
Data Source
AI summary
The present disclosure is directed to systems and methods for data synchronization by an edge computing device. The method includes (1) receiving, by a feedback loop interface, a request for an update of a data set via a data-acquisition-and-computing (DAC) engine in an edge computing device, (2) transmitting, by the feedback loop interface, the request for the update of the data set to an actor node in a network; (3) receiving, by the feedback loop interface, an indication regarding the update of the data set from the actor node; and (4) transmitting, by the feedback loop interface, the indication regarding the update of the data set via the DAC engine in the recurring cycle. The DAC engine includes a synchronizer to control sequences in a recurring cycle of the network. The synchronizer communicates with a configuration manager or receives configuration information from network nodes for managing the data set.