Heterogeneous charger deployment in wireless sensor networks using range-based and domination set partitioning with enhanced Chameleon Swarm optimization

• Formulation of a Range-Based and Domination Set Partitioning model to estimate the minimum number of heterogeneous chargers ( C R 1 and C R 2 ) required for full network coverage. • Development of an Enhanced Chameleon Swarm Algorithm incorporating adaptive parameter control and balanced exploration–exploitation for effective charger placement optimization. • Introduction of a charger repositioning mechanism to eliminate redundancy and enhance overall charging efficiency. In wireless sensor networks, efficient recharging of sensor nodes is crucial to maintaining network longevity and reliability. The recharging efficiency significantly depends on both the range and placement of static chargers. While wide range chargers can simultaneously serve multiple sensors, deploying them inefficiently can lead to redundant energy usage. Conversely, using narrow range chargers closer to individual sensors enhances energy utilization but may increase the number of chargers required. To balance these trade offs, this study proposes a heterogeneous charging framework employing two types of chargers with different charging ranges, denoted as C R 1 and C R 2 . The Range-Based and Domination Set Partitioning (RBDP) method is utilized to determine the minimum number of heterogeneous chargers required to ensure full network coverage. Subsequently, an Enhanced Chameleon Swarm Algorithm (ECSA) is developed to optimize charger placement. The performance of the proposed method is evaluated using the Coverage ratio . In the experimental setup, the network area ranges from 100 to 1500 larger deployment regions with sensor counts varying between 50 and 1500 nodes. Each experiment is repeated over multiple independent runs, and the reported results correspond to the average QoC values along with statistical measures. The proposed ECSA achieves an average coverage ratio of 97.91%, outperforming several metaheuristic algorithms, including the Chameleon Swarm Algorithm (93.36%), Gorilla Troops Optimizer (91.86%), Black Hole Algorithm (89.91%), and Quokka Optimization Algorithm (84.93%). The results demonstrate that the proposed framework provides improved charger deployment efficiency and competitive performance in heterogeneous wireless sensor network environments.