Optimization of Node Deployment for Three-dimensional Heterogeneous WSN in Elongated Structural Space

Authors

• Jiguang Yang, School of Electronic and Information Engineering, Lanzhou Jiaotong University, Lanzhou 730070, China
• Jiuyuan Huo, School of Electronic and Information Engineering, Lanzhou Jiaotong University, Lanzhou 730070, China; National Cryosphere Desert Data Center, Lanzhou 730000, China
• Fang Cao, School of Electronic and Information Engineering, Lanzhou Jiaotong University, Lanzhou 730070, China
• Cong Mu, School of Electronic and Information Engineering, Lanzhou Jiaotong University, Lanzhou 730070, China

Abstract

Abstract: To achieve effective coverage of key monitoring points in an elongated structural space, a heterogeneous wireless sensor network(HWSN) deployment optimization method combining the virtual force algorithm(VFA) and multi-strategy improved whale optimization algorithm(MSIWOA), namely HVF-MSIWOA, was proposed. A dynamic adaptive weight mechanism and a t-distribution perturbation operator with heterogeneous degrees of freedom were designed, enabling the whale optimization algorithm(WOA) to balance global exploration and local exploitation and jump out of local optima; combining the topological characteristics of the elongated space and node density distribution, an adaptive virtual force distance threshold between heterogeneous nodes was constructed; the mapping relationship between network node density and optimal virtual force threshold was established, and nodes were guided to move toward coverage gaps by virtual forces, reducing the invalid movement distance; an optimal allocation strategy was applied to nodes of the same type to reduce the total movement distance. Simulation results show that in the HWSN coverage optimization scenario of the three-dimensional elongated structural space, compared with WOA, three-dimensional virtual force algorithm(3DVFA), virtual force-directed improved sand cat swarm optimization algorithm(VF-ISCSO), and adaptive coverage-aware deployment based on deep reinforcement learning(ACDRL) algorithms, the coverage rate of this algorithm increases by 21.5%, 15.6%, 12.3%, and 5.2%, respectively. The HVF-MSIWOA algorithm shows significant advantages in redundancy rate, movement distance, and robustness.

Recommended Citation

Yang, Jiguang; Huo, Jiuyuan; Cao, Fang; and Mu, Cong (2026) "Optimization of Node Deployment for Three-dimensional Heterogeneous WSN in Elongated Structural Space," Journal of System Simulation: Vol. 38: Iss. 8, Article 19.
DOI: 10.16182/j.issn1004731x.joss.25-0902
Available at: https://dc-china-simulation.researchcommons.org/journal/vol38/iss8/19

First Page

2392

Last Page

2406

CLC

TP393

Recommended Citation

Yang Jiguang, Huo Jiuyuan, Cao Fang, et al. Optimization of Node Deployment for Three-dimensional Heterogeneous WSN in Elongated Structural Space[J]. Journal of System Simulation, 2026, 38(8): 2392-2406.

Corresponding Author

Huo Jiuyuan

DOI

10.16182/j.issn1004731x.joss.25-0902