Sensor Node Selection for Wireless Network Coverage Optimization
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Solution Overview
Problem
Wireless sensor networks face challenges in optimizing sensor node placement and direction to achieve comprehensive coverage and efficient monitoring, particularly in diverse environments like art galleries, where traditional methods fail to ensure clear object recognition.
Innovation Solution
A method for selecting and arranging sensor nodes by determining their coverage levels and contribution functions, adjusting their sensing directions to maximize coverage efficiency, involving steps to identify and fix sensor nodes with the highest contribution values and ensuring all objects are covered to a default level.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multiple sensor nodes are deployed to fully cover an object, then the coverage completeness is improved, but the recognition quality of the object deteriorates because the captured images do not facilitate identification
Solution Approach 1:
The patent applies local quality by assigning different functional roles to different sensor nodes based on their contribution values. Instead of uniform deployment, sensor nodes are selectively activated and oriented based on their specific contribution to coverage and recognition quality. The system identifies which sensor nodes provide the most valuable local coverage and directs them appropriately, while other nodes remain inactive or are oriented differently.
Solution Approach 2:
The patent implements dynamics by making the sensor node configuration adaptive rather than static. The system dynamically determines contribution functions, calculates coverage levels, and adjusts the active set of sensor nodes and their orientations based on real-time requirements. This dynamic approach allows the system to optimize both coverage completeness and recognition quality by flexibly reconfiguring which nodes are active and how they are oriented.
2Measurement precision
If sensor nodes are disposed at specific points with particular included angles to capture multiple sides of an object, then the object recognition quality is improved, but the system complexity increases
Solution Approach 1:
The patent applies self-service by enabling the sensor network system to automatically determine optimal configurations without external intervention. The system autonomously calculates contribution functions for each sensor node, determines which nodes should be active, and computes the optimal orientations for these nodes. This self-service capability reduces the need for manual planning and complex pre-configuration, allowing the system to adaptively find optimal arrangements based on environmental feedback.
Solution Approach 2:
The patent utilizes parameter changes by transforming the complex arrangement problem into a mathematical optimization problem involving contribution function parameters. Instead of manually specifying complex spatial relationships and included angles, the system uses parametric models to represent sensor node contributions and orientations. By adjusting these parameters based on calculated contribution values, the system simplifies the complexity of determining optimal sensor node arrangements while maintaining high recognition quality.
3Reliability
If all sensor nodes are activated to ensure comprehensive coverage, then the coverage level is improved, but the energy consumption increases
Solution Approach 1:
The patent applies the extraction principle by selectively removing inactive sensor nodes from the operational system. Instead of keeping all sensor nodes active, the system calculates contribution functions and identifies which nodes provide the most valuable coverage. Only the necessary subset of sensor nodes with the highest contribution values is activated, while others are deactivated or placed in low-power mode. This extraction of essential nodes maintains comprehensive coverage while significantly reducing overall energy consumption.
Solution Approach 2:
The patent implements partial action by activating only the necessary portion of sensor nodes rather than all nodes. The system determines the minimum set of sensor nodes required to achieve the desired coverage level by evaluating contribution functions. This partial activation approach ensures that coverage requirements are met with the smallest possible number of active nodes, thereby optimizing energy consumption while maintaining reliable coverage.
Data Source
AI summary
The present invention relates to a method for selecting sensor nodes, the method is adopted for calculating the value of a contribution function for a plurality of objects contributed by a plurality of sensor nodes, wherein the contribution function value is calculated by way of determining a coverage level of the objects made by the sensor nodes, or by means of arranging a sub sensor node group capable of sensing covering an object group and calculating the value of the contribution function for the objects contributed by the sensor nodes; Therefore, through the method, the sensor nodes having maximum contribution to the objects can be selected and arranged in a specific environment, and the sensing direction of those sensor nodes can be adjusted for making the sensor node group performs the best efficiency.