ArticleScientific reports2025
Coverage and connectivity maximization for wireless sensor networks using improved chaotic grey wolf optimization.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- End-to-end IoT sensor data simulation and predictive analysis: framework implementation and experimental evaluation.Scientific reports · 2026Article
- An enhanced parrot optimizer with multiple strategies for wireless sensor network node deployment.Scientific reports · 2026Article
- Smart weather aware drone sink SWADS for reliable and energy efficient agricultural wireless sensor networks.Scientific reports · 2025Article
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Authors and funding
8 authors.
Funding
Abstract
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.
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