Sensor Scheduling via Fuzzy Cognitive Maps and Options Pricing
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Solution Overview
Problem
Current sensor scheduling methods for intelligence, surveillance, and reconnaissance (ISR) missions are inefficient in dynamic and uncertain environments, as they fail to prioritize tasks effectively based on sensor availability and mission importance, leading to suboptimal task allocation and scheduling.
Innovation Solution
A system and method that utilizes a processor to select and schedule sensors for ISR missions by applying an options pricing model and market-based auctions, combining fuzzy logic with real option theory to determine task values and prioritize tasks based on predicted effectiveness, availability, and mission importance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional sensor scheduling methods are used, then the system is simple to operate, but the task allocation efficiency deteriorates in dynamic and uncertain environments
Solution Approach 1:
The patent implements dynamic sensor scheduling by continuously updating task values based on changing environmental conditions and sensor availability. The system transitions from static scheduling to dynamic adaptation through real-time revaluation of tasks using fuzzy cognitive maps, allowing the scheduling system to respond flexibly to uncertain and changing mission environments.
Solution Approach 2:
The system changes the parameter of task valuation by introducing fuzzy logic variables and uncertainty parameters. Task values are no longer fixed but are dynamically adjusted based on fuzzy assessments of sensor effectiveness, environmental conditions, and mission priorities. This parameter transformation enables the system to handle uncertainty while improving allocation efficiency.
2Reliability
If sensor tasks are prioritized based on fixed criteria, then the scheduling process is straightforward, but the ability to account for risk and uncertainty deteriorates
Solution Approach 1:
The patent introduces fuzzy cognitive maps as an intermediary layer between sensor data and scheduling decisions. These maps serve as mediators that process uncertain information about environmental conditions and sensor effectiveness, transforming vague inputs into quantifiable task values. This intermediary structure enables the system to account for risk and uncertainty without requiring direct complex probabilistic calculations in the scheduling algorithm.
Solution Approach 2:
The system replaces traditional deterministic scheduling mechanics with fuzzy logic-based valuation. Instead of using fixed mathematical rules for task prioritization, the patent substitutes a fuzzy cognitive modeling approach that can handle imprecise and uncertain information. This substitution allows the system to incorporate risk assessment while maintaining computational feasibility.
3Reliability
If multiple sensors are allocated to high-priority tasks, then the importance of critical tasks is emphasized, but the availability of sensors for other tasks deteriorates
Solution Approach 1:
The patent applies partial action by allocating sensor resources proportionally rather than exclusively to high-priority tasks. The fuzzy cognitive map evaluates the degree of task criticality and distributes sensor availability accordingly, allowing partial resource allocation to multiple tasks based on their relative importance. This prevents complete resource concentration on single tasks while maintaining adequate support for critical operations.
Solution Approach 2:
The system dynamically adjusts sensor allocation based on real-time task valuation. Rather than fixed allocation, sensors can be reassigned between tasks as conditions change. The dynamic revaluation process allows the system to respond to changing mission priorities and environmental conditions, optimizing the balance between critical task assurance and overall mission throughput.
Data Source
AI summary
A system for sensor scheduling includes a plurality of sensors operable to perform one or more tasks and a processor operable to receive one or more missions and one or more environmental conditions associated with a respective mission. Each mission may include one or more tasks to be performed by one or more of the plurality of sensors. The processor is further operable to select one or more of the plurality of sensors to perform a respective task associated with the respective mission. The processor may also schedule the respective task to be performed by the selected one or more sensors. The scheduling is based at least on a task value that is determined based on an options pricing model. The options pricing model is based at least on the importance of the respective task to the success of the respective mission and one or more scheduling demands.