Unauthorized occupation of parking spaces designated for individuals with disabilities remains a persistent challenge in urban environments, limiting accessibility and inclusive mobility. This paper proposes an integrated UAV-assisted enforcement framework that combines drone-based imaging, onboard license plate recognition (LPR), IoT connectivity, and a staged optimization strategy for energy-aware surveillance. The framework employs a two-phase approach: first, it derives energy-efficient UAV activation patterns via sleep–active scheduling, followed by coverage maximization under energy constraints. The inherently multi-objective problem—balancing energy consumption, coverage, and redundancy—is addressed via a weighted-aggregation formulation, enabling efficient optimization with classical metaheuristic algorithms. Seven algorithms—Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Simulated Annealing (SA), Ant Colony Optimization (ACO), Differential Evolution (DE), Artificial Bee Colony (ABC), and a Greedy baseline—are implemented in both conventional and staged variants to enable comprehensive evaluation. Experimental results demonstrate 32–45% reductions in energy consumption, over 95% coverage effectiveness, and 50–60% faster convergence compared to single-phase approaches, with all improvements statistically significant (p < 0.001). The proposed framework provides a scalable, practically deployable solution for intelligent enforcement of disability parking regulations while also enabling energy-efficient UAV coordination in smart urban monitoring systems.
ZainEldin et al. (Wed,) studied this question.