Sensor Network Optimization Using Simulated Annealing
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Solution Overview
Problem
Existing methods for optimizing sensor networks in surveillance systems are limited by their inability to handle non-differentiable optimization constraints and often get stuck in local minima or maxima, failing to provide a comprehensive solution for determining the optimal number, type, and position of sensors under various constraints such as budget, energy, and detection performance.
Innovation Solution
An iterative method that uses a simulated annealing algorithm to disturb sensor types and characteristics, evaluating solutions based on a combination of optimization criteria and selecting the best configurations that minimize or maximize the global cost function while respecting absolute constraints, allowing for the determination of the optimal sensor configuration for monitoring a geographical area.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional optimization methods such as gradient method are used, then the optimization process is simple and fast, but the method cannot handle non-differentiable functions and gets stuck in local minima or maxima
Solution Approach 1:
The patent transforms the optimization problem by changing the parameter representation from continuous differentiable parameters to discrete combinatorial parameters (sensor selection, positioning, and configuration). This allows the use of simulated annealing algorithm which can handle non-differentiable cost functions and avoid local minima by probabilistically accepting worse solutions during the search process.
Solution Approach 2:
The patent replaces the mechanical gradient-based optimization system with a probabilistic simulated annealing system. Instead of following the gradient direction which requires differentiability, the new system uses probabilistic transitions based on energy-like cost functions, allowing it to navigate the solution space without requiring derivative information.
2Reliability
If the number and type of sensors are increased to improve detection performance, then the detection probability and location precision improve, but the total cost and system complexity increase
Solution Approach 1:
The patent changes the optimization parameters to include discrete sensor selection, positioning coordinates, and configuration settings as part of the solution vector. The simulated annealing algorithm searches through this combinatorial space to find the optimal subset and arrangement of sensors that achieves required detection performance with minimum system complexity and cost.
Solution Approach 2:
The patent applies local quality by optimizing sensor placement and configuration specifically in high-priority monitoring zones rather than uniformly distributing sensors throughout the entire area. The cost function incorporates zone-specific detection requirements, allowing the system to concentrate sensor resources where they provide maximum value while reducing overall system complexity.
3Adaptability or versatility
If multiple optimization criteria are combined into a global cost function, then the solution balances multiple constraints, but the cost function becomes non-differentiable and complex
Solution Approach 1:
The patent replaces the traditional differentiable optimization framework with a simulated annealing framework that naturally handles non-differentiable cost functions. The global cost function combining multiple criteria (detection probability, location precision, cost constraints, energy consumption) is evaluated directly without requiring gradients, and the probabilistic acceptance mechanism handles the complexity through random sampling and energy-based filtering.
Solution Approach 2:
The patent creates a universal optimization framework that can handle multiple types of constraints and criteria simultaneously through a single global cost function. The simulated annealing algorithm serves as a multi-functional optimizer that can evaluate diverse metrics (detection performance, cost, energy) and balance them through the probabilistic acceptance criterion, making the system adaptable to various optimization scenarios.
Data Source
AI summary
The invention relates to an iterative method, to be implemented by a computer, for optimally designing a system for monitoring a geographical area comprising a plurality of sensors of different types having different characteristics, said sensors being represented by a vector S, each component of which indicates the type and characteristics of a sensor and the position thereof in said area, wherein said system has a plurality of absolute technical constraints.