N-Modular Redundant Communication for Fault Tolerant Sensor Networks
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional control systems relying on central processing nodes for resolving conflicts in sensor data are prone to single-point failures and require extensive communication and complex programming, which increases overhead and vulnerability to faults and attacks.
Innovation Solution
A distributed Byzantine Fault Tolerant communication system where processing nodes within a sensor network calculate and communicate consensus values, reducing reliance on a central node and simplifying programming by using n-Module Redundancy (nMR) to generate and transmit consensus messages across a network, ensuring fault tolerance and secure data transmission.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a central processing node is used to resolve conflicts in sensor data, then fault tolerance is achieved within the scope of redundancy measurements, but the central processing node becomes a single point of failure and requires complex programming logic
Solution Approach 1:
The patent segments the conflict resolution function from the central processing node and distributes it to multiple peer nodes in the sensor network. Each peer node independently executes the conflict resolution algorithm, eliminating the need for complex centralized programming logic while maintaining fault tolerance through distributed computation.
Solution Approach 2:
The sensor network performs self-service by having peer nodes automatically resolve conflicts among sensor measurements without requiring complex centralized control. The distributed algorithm enables nodes to autonomously identify and resolve inconsistencies, reducing programming complexity while maintaining reliability.
2Reliability
If a central processing node receives and resolves all sensor data conflicts, then system reliability is maintained, but communication overhead between the sensor network and central processing node increases significantly
Solution Approach 1:
The patent segments the data processing workload by having peer nodes perform local conflict resolution before transmitting data to the central processing node. This segmentation reduces the volume of data requiring centralized processing, thereby reducing communication overhead and energy consumption while maintaining system reliability.
Solution Approach 2:
The patent extracts the conflict resolution function from the central processing node and implements it at the peer node level. This extraction eliminates unnecessary communication of already-resolved data to the central node, reducing communication overhead while preserving system reliability through distributed fault tolerance.
3Reliability
If redundant measurements are performed by multiple sensors, then fault tolerance is improved, but the amount of communication required within the control system increases
Solution Approach 1:
The patent segments redundant measurements into local processing groups at peer nodes, where conflicts are resolved locally before transmission. This segmentation maintains fault tolerance through redundant measurements while minimizing communication overhead by eliminating redundant data transmission to the central node.
Solution Approach 2:
The patent merges redundant measurements and their conflict resolution at the peer node level before transmission to the central processing node. This combining approach maintains fault tolerance through redundant sensing while reducing communication overhead by transmitting only the resolved consensus value rather than all individual measurements.
Data Source
AI summary
A fault tolerant consensus generation and communication system and method is described. Each processing node in the system receives a plurality of measurements from a sensor, calculates a consolidated value for the received plurality of measurements, transmits the consolidated value to other processing nodes, receives consolidated values from the other processing nodes, calculates a consensus value based on the calculated consolidated value and the received one or more consolidated values, transmits the calculated consensus value to the other processing nodes, receives consensus values from the other processing nodes, generates a consensus message based on the calculated consensus value, the received one or more consensus values, and a predefined criterion, and, in a case where the consensus message is not present in a consensus queue, adds the consensus message to the consensus queue.