Three-dimensional (3D) wireless sensor networks (WSNs) are gaining increasing significance in applications across complex environments, including underwater monitoring, mountainous terrains, and smart cities. Compared to two-dimensional (2D) WSNs, 3D WSNs introduce unique challenges in coverage, connectivity, map construction, and blind area detection. This paper provides a comprehensive survey of node deployment strategies in 3D WSNs. We summarize several key design aspects: sensing models, occlusion detection, coverage and connectivity, sensor mobility, signal and protocol effects, and simulation map construction. Deployment algorithms are categorized into six main types: classical algorithms, computational geometry algorithms, virtual force algorithms, evolutionary algorithms, swarm intelligence algorithms, and approximation algorithms. For each category, we review representative works, analyze their design principles, and evaluate their advantages and limitations. Comparative summaries are included to facilitate algorithm selection based on specific deployment requirements. Recent advancements in these strategies have led to significant improvements in network performance, with some algorithms achieving up to 12.5% lower cost and 30% higher coverage compared to earlier methods, and even reaching 100% coverage in certain cases. Thus, this survey aims to present the current research status and highlight practical improvements, offering a reference for understanding existing approaches and selecting appropriate algorithms for diverse deployment scenarios.

As an innovative application of wireless sensor networks (WSNs), the Solar Insecticidal Lamp Internet of Things (SIL-IoTs) is widely deployed for green pest control. However, existing deployment strategies commonly assume homogeneous energy-harvesting capabilities among nodes and mandate full coverage, thereby overlooking the realities of uneven solar irradiation and budget constraints in complex 3D terrains. To address these issues, this paper investigates the SIL deployment problem using real DEM data. We first introduce a 3D coverage model alongside a terrain-based solar energy weight model, and formulate the task as a partial coverage optimization problem constrained by both energy harvesting and budget limitations. To solve this problem and avoid premature convergence, we propose an Improved Jumping Particle Swarm Optimization (IJPSO) algorithm. Experimental results on benchmark datasets and DEM-based 3D terrain simulations demonstrate that IJPSO outperforms six competing algorithms, achieving average relative percentage deviations (RPD) of 0.045% and 0.140% and average 3D deployment objective values of 62.27 and 84.37 under the 80% and 90% coverage constraints, respectively. These results demonstrate the effectiveness of IJPSO in reducing deployment costs while maintaining solution consistency.