Optimized deployment method of three-dimensional sensor network based on improved PSO (Particle Swarm Optimization)

By improving the PSO algorithm and combining it with multi-objective fitness functions and parallel computing, the node deployment of 3D wireless sensor networks is optimized, solving the problems of low coverage efficiency, uneven energy consumption, and slow dynamic response, and realizing efficient and reliable 3D WSN deployment.

CN121013086AActive Publication Date: 2025-11-25EAST CHINA JIAOTONG UNIVERSITY
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Patent Information

Application Number
CN202511252822.3
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-09-03
Publication Date
2025-11-25
Estimated Expiration
2045-09-03

AI Technical Summary

Technical Problem

Existing technologies in 3D wireless sensor networks suffer from low coverage efficiency, uneven energy consumption, and slow dynamic response, making it difficult to achieve efficient and reliable node deployment, especially in complex terrains and dynamic environments.

Method used

Employing a multi-objective fitness function, a dynamic PSO algorithm, and a parallel computing architecture, combined with terrain elevation and obstacle distribution, the algorithm optimizes node positions by improving the PSO algorithm, thereby achieving enhanced 3D coverage and balanced energy consumption, and supporting real-time redeployment in dynamic environments.

Benefits of technology

In complex environments, it improved 3D coverage by 15%-30%, extended network lifetime by more than 20%, increased computational efficiency by 40%-60%, and shortened dynamic response time by 70%, significantly enhancing system adaptability.

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Abstract

The invention discloses a PSO-improved three-dimensional sensor network optimization deployment method. The scheme comprises the following steps: constructing a three-dimensional space monitoring model fusing terrain elevation and obstacle distribution; designing a dynamic multi-target fitness function fusing the space coverage rate, the energy consumption balance degree and the obstacle penetration loss coefficient; dynamically adjusting the search weight of the particle swarm in the Z-axis direction according to the terrain gradient; dividing a three-dimensional space into sub-regions and initializing a particle swarm; and iteratively optimizing the node position by adopting an improved PSO algorithm until a termination condition is met. The method has the advantages that the method is suitable for scenes such as mountain monitoring, unmanned aerial vehicle cluster networking and ocean three-dimensional monitoring, the three-dimensional coverage rate can be increased by 15%-30%, the network service life can be prolonged by 20% or above, real-time node redeployment in a dynamic environment is supported, and through innovative three-dimensional space modeling, a multi-target optimization algorithm and a parallel computing architecture, the real-time node redeployment can be realized. And comprehensive improvement of the deployment quality and efficiency of the sensor network in a complex environment is realized.
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