Coverage and connectivity maximization for wireless sensor networks using improved chaotic grey wolf optimization
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 deplo...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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Nature Publishing Group
2025
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| Online Access: | https://umpir.ump.edu.my/id/eprint/45404/ |
| _version_ | 1848827408416768000 |
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| author | Shaikh, Muhammad Suhail Wang, Chang Xie, Senlin Zheng, Gengzhong Dong, Xiaoqing Qiu, Shuwei Mohd Ashraf, Ahmad Raj, Saurav |
| author_facet | Shaikh, Muhammad Suhail Wang, Chang Xie, Senlin Zheng, Gengzhong Dong, Xiaoqing Qiu, Shuwei Mohd Ashraf, Ahmad Raj, Saurav |
| author_sort | Shaikh, Muhammad Suhail |
| building | UMP Institutional Repository |
| collection | Online Access |
| description | 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. |
| first_indexed | 2025-11-15T04:00:14Z |
| format | Article |
| id | ump-45404 |
| institution | Universiti Malaysia Pahang |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T04:00:14Z |
| publishDate | 2025 |
| publisher | Nature Publishing Group |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | ump-454042025-08-18T01:43:41Z https://umpir.ump.edu.my/id/eprint/45404/ Coverage and connectivity maximization for wireless sensor networks using improved chaotic grey wolf optimization Shaikh, Muhammad Suhail Wang, Chang Xie, Senlin Zheng, Gengzhong Dong, Xiaoqing Qiu, Shuwei Mohd Ashraf, Ahmad Raj, Saurav TK Electrical engineering. Electronics Nuclear engineering 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. Nature Publishing Group 2025 Article PeerReviewed pdf en cc_by_nc_nd_4 https://umpir.ump.edu.my/id/eprint/45404/1/Coverage%20and%20connectivity%20maximization%20for%20wireless%20sensor%20networks.pdf Shaikh, Muhammad Suhail and Wang, Chang and Xie, Senlin and Zheng, Gengzhong and Dong, Xiaoqing and Qiu, Shuwei and Mohd Ashraf, Ahmad and Raj, Saurav (2025) Coverage and connectivity maximization for wireless sensor networks using improved chaotic grey wolf optimization. Scientific Reports, 15 (1). pp. 1-38. ISSN 2045-2322. (Published) https://doi.org/10.1038/s41598-025-00184-2 https://doi.org/10.1038/s41598-025-00184-2 https://doi.org/10.1038/s41598-025-00184-2 |
| spellingShingle | TK Electrical engineering. Electronics Nuclear engineering Shaikh, Muhammad Suhail Wang, Chang Xie, Senlin Zheng, Gengzhong Dong, Xiaoqing Qiu, Shuwei Mohd Ashraf, Ahmad Raj, Saurav Coverage and connectivity maximization for wireless sensor networks using improved chaotic grey wolf optimization |
| title | Coverage and connectivity maximization for wireless sensor networks using improved chaotic grey wolf optimization |
| title_full | Coverage and connectivity maximization for wireless sensor networks using improved chaotic grey wolf optimization |
| title_fullStr | Coverage and connectivity maximization for wireless sensor networks using improved chaotic grey wolf optimization |
| title_full_unstemmed | Coverage and connectivity maximization for wireless sensor networks using improved chaotic grey wolf optimization |
| title_short | Coverage and connectivity maximization for wireless sensor networks using improved chaotic grey wolf optimization |
| title_sort | coverage and connectivity maximization for wireless sensor networks using improved chaotic grey wolf optimization |
| topic | TK Electrical engineering. Electronics Nuclear engineering |
| url | https://umpir.ump.edu.my/id/eprint/45404/ https://umpir.ump.edu.my/id/eprint/45404/ https://umpir.ump.edu.my/id/eprint/45404/ |