{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,3]],"date-time":"2026-07-03T15:28:42Z","timestamp":1783092522945,"version":"3.54.6"},"reference-count":120,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2025,9,24]],"date-time":"2025-09-24T00:00:00Z","timestamp":1758672000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China","award":["62102170"],"award-info":[{"award-number":["62102170"]}]},{"name":"National Natural Science Foundation of China","award":["62072248"],"award-info":[{"award-number":["62072248"]}]},{"name":"National Natural Science Foundation of China","award":["22XFRS033"],"award-info":[{"award-number":["22XFRS033"]}]},{"name":"Research Start-Up Fund for Talent Introduction of Jiangsu Normal University","award":["62102170"],"award-info":[{"award-number":["62102170"]}]},{"name":"Research Start-Up Fund for Talent Introduction of Jiangsu Normal University","award":["62072248"],"award-info":[{"award-number":["62072248"]}]},{"name":"Research Start-Up Fund for Talent Introduction of Jiangsu Normal University","award":["22XFRS033"],"award-info":[{"award-number":["22XFRS033"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JSAN"],"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.3390\/jsan14050094","type":"journal-article","created":{"date-parts":[[2025,9,25]],"date-time":"2025-09-25T08:56:44Z","timestamp":1758790604000},"page":"94","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["A Survey of Three-Dimensional Wireless Sensor Networks Deployment Techniques"],"prefix":"10.3390","volume":"14","author":[{"given":"Tingting","family":"Cao","sequence":"first","affiliation":[{"name":"School of Electrical Engineering and Automation, Jiangsu Normal University, Xuzhou 221116, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0365-710X","authenticated-orcid":false,"given":"Fan","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Electrical Engineering and Automation, Jiangsu Normal University, Xuzhou 221116, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chensiyu","family":"Fan","sequence":"additional","affiliation":[{"name":"School of Electrical Engineering and Automation, Jiangsu Normal University, Xuzhou 221116, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ru","family":"Han","sequence":"additional","affiliation":[{"name":"School of Smart Agriculture (Artificial Intelligence), Nanjing Agricultural University, Nanjing 210031, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7818-1030","authenticated-orcid":false,"given":"Xing","family":"Yang","sequence":"additional","affiliation":[{"name":"College of Intelligent Manufacturing, Anhui Science and Technology University, Chuzhou 233100, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6700-9347","authenticated-orcid":false,"given":"Lei","family":"Shu","sequence":"additional","affiliation":[{"name":"School of Smart Agriculture (Artificial Intelligence), Nanjing Agricultural University, Nanjing 210031, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,9,24]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Lanzolla, A., and Spadavecchia, M. 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