Zhixin Qi, Haoze Gu, Zemin Chao, Zejiao Dong, Hongzhi Wang, Abaho G. Gershome
As the cornerstone of the Internet of Things, the deployment of wireless sensor networks remains a challenging problem, as it is difficult to balance coverage quality and computational efficiency, particularly when dealing with large-scale node deployments. To solve this problem, we propose a coverage optimization algorithm that integrates max-heap-based priority maintenance with resultant vector-driven dispersion metrics. Our approach organizes sensor nodes via a max-heap structure and introduces a novel resultant vector-based metric to evaluate coverage potential; the key optimization parameter α for this metric was calibrated through theoretical derivation based on optimal hexagonal geometry. This method implicitly transforms the Unit Disk Cover (UDC for brief) problem into a Discrete Unit Disk Cover (DUDC for brief), achieving real-time performance and high-quality coverage with reduced redundancy. Experimental results demonstrate that our method reaches the best coverage quality compared to baseline methods while maintaining almost the same computational complexity.