Shaikh Muhammad Suhail, Wang Chang, Xie Senlin, Zheng Gengzhong, Dong Xiaoqing, Qiu Shuwei, Ahmad Mohd Ashraf, Raj Saurav
School of Physics and Electronic Engineering, Hanshan Normal University, Chaozhou, 521000, Guangdong, China.
School of Computer and Information Engineering, Hanshan Normal University, Chaozhou, 521000, Guangdong, China.
Sci Rep. 2025 May 5;15(1):15706. doi: 10.1038/s41598-025-00184-2.
Efficient network coverage and connectivity in wireless sensor networks (WSNs) is critical for modern data-driven applications requiring seamless data collection and transmission. One of the key challenges is the optimal placement of sensor nodes, which directly impacts network performance and deployment costs. This study presents an Improved Chaotic Grey Wolf Optimization (ICGWO) algorithm to enhance WSN coverage and connectivity while addressing challenges like high deployment costs, limited coverage, and insufficient connectivity. A mathematical model for the WSN coverage and connectivity optimization problem is developed as the foundation. The Grey Wolf Optimizer (GWO) is enhanced using a chaotic map, improving its ability to find the best solutions and achieve faster convergence, resulting in the ICGWO algorithm. The performance of ICGWO is evaluated using CEC_22 benchmark functions and compared with other optimization methods, demonstrating clear improvements in efficiency. In practical applications, the proposed ICGWO obtained superior results for sensor node placement. For example, with 20 sensor nodes in Case 1, the coverage rate reaches 95.9077%, while for 30 nodes in Case 2, it achieves 98.2211%. Similarly, in Case 3, with 40 sensor nodes, the coverage rate is 91.6875%, and in Case 4, with 50 sensor nodes, it is 99.4940%. In addition, in Case 5 and Case 6, with 60 and 70 sensor nodes, the coverage rates are 99.7801% and 99.7822%, respectively. These outcomes reflect average improvements of 16.41%, 5.36%, 3.45%,2.371%,2.80%, and 2.18%, respectively, compared to other state-of-the-art methods. These metrics emphasize the effectiveness of ICGWO in maximizing network coverage and connectivity. The findings confirm that ICGWO efficiently improves the coverage and connectivity, making it a reliable solution for addressing deployment challenges in diverse scenarios. By maximizing the coverage and connectivity, ICGWO significantly contributes to the advancement of WSN technology.
对于需要无缝数据收集和传输的现代数据驱动型应用而言，无线传感器网络（WSN）中的高效网络覆盖和连通性至关重要。关键挑战之一是传感器节点的最优布局，这直接影响网络性能和部署成本。本研究提出一种改进的混沌灰狼优化（ICGWO）算法，以提高WSN的覆盖范围和连通性，同时应对诸如高部署成本、覆盖范围有限和连通性不足等挑战。建立了一个用于WSN覆盖和连通性优化问题的数学模型作为基础。利用混沌映射对灰狼优化器（GWO）进行了改进，提高了其找到最优解的能力并实现了更快的收敛，从而得到了ICGWO算法。使用CEC_22基准函数对ICGWO的性能进行了评估，并与其他优化方法进行了比较，结果表明其效率有明显提高。在实际应用中，所提出的ICGWO在传感器节点布局方面取得了优异的结果。例如，在案例1中有20个传感器节点时，覆盖率达到95.9077%，而在案例2中有30个节点时，覆盖率达到98.2211%。同样，在案例3中有40个传感器节点时，覆盖率为91.6875%，在案例4中有50个传感器节点时，覆盖率为99.4940%。此外，在案例5和案例6中，分别有60个和70个传感器节点时，覆盖率分别为99.7801%和99.7822%。与其他现有方法相比，这些结果分别反映出平均提高了16.41%、5.36%、3.45%、2.371%、2.80%和2.18%。这些指标强调了ICGWO在最大化网络覆盖和连通性方面的有效性。研究结果证实，ICGWO有效地提高了覆盖范围和连通性，使其成为解决各种场景下部署挑战的可靠解决方案。通过最大化覆盖范围和连通性，ICGWO对WSN技术的发展做出了重大贡献。
