Optimizing wireless sensor network topology with node load consideration
Background: With the development of the Internet, the topology optimization of wireless sensor networks has received increasing attention. However, traditional optimization methods often overlook the energy imbalance caused by node loads, which affects network performance. Methods: To improve the ov...
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KeAi Communications Co., Ltd.
2025-02-01
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| Series: | Virtual Reality & Intelligent Hardware |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2096579624000500 |
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| author | Ruizhi Chen |
| author_facet | Ruizhi Chen |
| author_sort | Ruizhi Chen |
| collection | DOAJ |
| description | Background: With the development of the Internet, the topology optimization of wireless sensor networks has received increasing attention. However, traditional optimization methods often overlook the energy imbalance caused by node loads, which affects network performance. Methods: To improve the overall performance and efficiency of wireless sensor networks, a new method for optimizing the wireless sensor network topology based on K-means clustering and firefly algorithms is proposed. The K-means clustering algorithm partitions nodes by minimizing the within-cluster variance, while the firefly algorithm is an optimization algorithm based on swarm intelligence that simulates the flashing interaction between fireflies to guide the search process. The proposed method first introduces the K-means clustering algorithm to cluster nodes and then introduces a firefly algorithm to dynamically adjust the nodes. Results: The results showed that the average clustering accuracies in the Wine and Iris data sets were 86.59% and 94.55%, respectively, demonstrating good clustering performance. When calculating the node mortality rate and network load balancing standard deviation, the proposed algorithm showed dead nodes at approximately 50 iterations, with an average load balancing standard deviation of 1.7×104, proving its contribution to extending the network lifespan. Conclusions: This demonstrates the superiority of the proposed algorithm in significantly improving the energy efficiency and load balancing of wireless sensor networks to extend the network lifespan. The research results indicate that wireless sensor networks have theoretical and practical significance in fields such as monitoring, healthcare, and agriculture. |
| format | Article |
| id | doaj-art-2b7300a8f0244a44b5086b4bc7d155e6 |
| institution | OA Journals |
| issn | 2096-5796 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | KeAi Communications Co., Ltd. |
| record_format | Article |
| series | Virtual Reality & Intelligent Hardware |
| spelling | doaj-art-2b7300a8f0244a44b5086b4bc7d155e62025-08-20T01:57:35ZengKeAi Communications Co., Ltd.Virtual Reality & Intelligent Hardware2096-57962025-02-0171476110.1016/j.vrih.2024.08.003Optimizing wireless sensor network topology with node load considerationRuizhi Chen0School of Computer Engineering, Zhanjiang University of Science and Technology, Zhanjiang 524094, ChinaBackground: With the development of the Internet, the topology optimization of wireless sensor networks has received increasing attention. However, traditional optimization methods often overlook the energy imbalance caused by node loads, which affects network performance. Methods: To improve the overall performance and efficiency of wireless sensor networks, a new method for optimizing the wireless sensor network topology based on K-means clustering and firefly algorithms is proposed. The K-means clustering algorithm partitions nodes by minimizing the within-cluster variance, while the firefly algorithm is an optimization algorithm based on swarm intelligence that simulates the flashing interaction between fireflies to guide the search process. The proposed method first introduces the K-means clustering algorithm to cluster nodes and then introduces a firefly algorithm to dynamically adjust the nodes. Results: The results showed that the average clustering accuracies in the Wine and Iris data sets were 86.59% and 94.55%, respectively, demonstrating good clustering performance. When calculating the node mortality rate and network load balancing standard deviation, the proposed algorithm showed dead nodes at approximately 50 iterations, with an average load balancing standard deviation of 1.7×104, proving its contribution to extending the network lifespan. Conclusions: This demonstrates the superiority of the proposed algorithm in significantly improving the energy efficiency and load balancing of wireless sensor networks to extend the network lifespan. The research results indicate that wireless sensor networks have theoretical and practical significance in fields such as monitoring, healthcare, and agriculture.http://www.sciencedirect.com/science/article/pii/S2096579624000500Node loadWireless sensor networkK-means clusteringFirefly algorithmTopology optimization |
| spellingShingle | Ruizhi Chen Optimizing wireless sensor network topology with node load consideration Virtual Reality & Intelligent Hardware Node load Wireless sensor network K-means clustering Firefly algorithm Topology optimization |
| title | Optimizing wireless sensor network topology with node load consideration |
| title_full | Optimizing wireless sensor network topology with node load consideration |
| title_fullStr | Optimizing wireless sensor network topology with node load consideration |
| title_full_unstemmed | Optimizing wireless sensor network topology with node load consideration |
| title_short | Optimizing wireless sensor network topology with node load consideration |
| title_sort | optimizing wireless sensor network topology with node load consideration |
| topic | Node load Wireless sensor network K-means clustering Firefly algorithm Topology optimization |
| url | http://www.sciencedirect.com/science/article/pii/S2096579624000500 |
| work_keys_str_mv | AT ruizhichen optimizingwirelesssensornetworktopologywithnodeloadconsideration |