Sensor Placement Layout for Budget-Constrained Fault Identification
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Solution Overview
Problem
Existing sensor network configurations lack unique identification capability for locations where anomalous behavior is sensed, and are often constrained by budget limitations.
Innovation Solution
A system and method for budget-constrained sensor network design that utilizes an Identifying Code formulation to maximize the number of locations that can be uniquely identified, employing Integer Linear Programming and Maximum Set-Group Cover formulations to optimize sensor placement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If traditional sensor network configuration methods are used, then budget constraints are satisfied, but unique identification capability for locations is lost
Solution Approach 1:
The patent changes the fundamental parameter of sensor configuration from traditional coverage-based approaches to Identifying Code-based approaches. By assigning unique codes to sensors and using code combinations to identify locations, the system achieves unique identification capability while maintaining budget constraints through optimized sensor placement algorithms.
Solution Approach 2:
The patent performs preliminary optimization by pre-calculating the optimal sensor placement configuration using Integer Linear Programming and Maximum Set-Group Cover formulations. This preliminary action ensures that when sensors are deployed, they are already positioned to provide unique identification capability for maximum locations within the budget constraint.
2Loss of information
If more sensors are deployed to achieve unique identification, then unique fault identification signatures increase, but budget is exceeded
Solution Approach 1:
The patent applies partial action by deploying sensors only to the extent necessary to achieve unique identification capability for the maximum number of locations within the budget constraint. Rather than deploying sensors excessively, the optimization algorithms precisely calculate the minimum required sensor placement to achieve the desired identification capability.
Solution Approach 2:
The patent changes the approach from deploying more sensors to achieve better identification to optimizing sensor placement using Identifying Code theory. This parameter change allows the system to achieve maximum fault identification capability with the minimum number of sensors within the budget constraint.
3Reliability
If sensor placement is optimized for coverage, then monitoring capability improves, but unique identification for each location is not ensured
Solution Approach 1:
The patent applies local quality by ensuring that each location in the network has a unique identification signature through carefully localized sensor placement. Rather than uniform coverage, the optimization algorithms assign sensors to specific locations based on their identifying code combinations, ensuring each location has unique monitoring characteristics.
Solution Approach 2:
The patent performs preliminary optimization using Integer Linear Programming to pre-determine the optimal sensor placement configuration that simultaneously achieves both monitoring capability and unique identification. This preliminary action ensures that sensor placement satisfies both requirements from the outset rather than attempting to achieve them separately.
Data Source
AI summary
A system provides the minimum number of sensors that will be needed to uniquely identify locations where anomalous behavior is sensed that can be deployed within the specified budget. The system applies an Integer Linear Programming formulation and a Maximum Set-Group Cover (MSGC) formulation. One implementation of the system is applied to detect contaminants in a water distribution system.