AI-Driven Sensor Placement and Wireless Sensor Deployment: A PRISMA-Guided Systematic Review of Optimization Methods, Validation Maturity, and Benchmarking

Wireless sensor network (WSN) performance depends strongly on sensor deployment because node placement affects coverage, connectivity, energy consumption, localization, data reliability, and network lifetime. Although artificial intelligence (AI) is increasingly used for WSN deployment, the literature remains fragmented across methods, objectives, environments, metrics, and validation practices. This paper presents a PRISMA-guided systematic review and mapping of 51 primary studies on AI-driven WSN deployment, classified by AI technique, deployment objective, network environment, application domain, evaluation metric, validation method, methodological completeness, and reported limitations. Optimization-oriented AI dominates the field, particularly metaheuristics, swarm intelligence, and machine learning, while deep learning and reinforcement learning are emerging in predictive, adaptive, mobile, and application-specific settings. Swarm-intelligence methods are mainly used for coverage-centered static deployment, reinforcement learning for adaptive and mobile redeployment, and machine learning/deep learning methods for prediction-driven and application-specific placement. Coverage, connectivity, energy efficiency, and network lifetime remain the dominant objectives, whereas robustness, privacy, security, latency, edge feasibility, and lifecycle adaptation receive limited attention. Validation is a major methodological weakness, as most studies rely on simulation-only evaluation with limited use of real testbeds, field deployments, digital twins, long-term experiments, or standardized benchmarks. To address these gaps, the review introduces a validation-maturity perspective and benchmarking requirements emphasizing reproducible scenarios, explicit node assumptions, realistic sensing and communication models, common metrics, and shareable experimental artifacts. It also proposes an evidence-informed AI–Deployment–Validation Lifecycle Framework linking application requirements, environment modeling, objective formulation, AI-method selection, deployment optimization, validation, benchmarking, and lifecycle adaptation. The framework is intended as an organizing structure for future research rather than an empirically or expert-validated scoring instrument.