A new tree-based data aggregation method in the wireless sensor networks using Ant Colony Optimization and Cuckoo search algorithms
Abstract Prolonging wireless sensor networks (WSNs) longevity and minimizing energy expenses represent the primary considerations in transmitting sensor data. Sensor nodes typically function on restricted battery power, so overall energy consumption and network lifespan become crucial concerns. Data...
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| Format: | Article |
| Language: | English |
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SpringerOpen
2025-06-01
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| Series: | Journal of Engineering and Applied Science |
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| Online Access: | https://doi.org/10.1186/s44147-025-00652-6 |
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| author | Shuling Yin Jiahai Tu Xiaoyan Chen |
| author_facet | Shuling Yin Jiahai Tu Xiaoyan Chen |
| author_sort | Shuling Yin |
| collection | DOAJ |
| description | Abstract Prolonging wireless sensor networks (WSNs) longevity and minimizing energy expenses represent the primary considerations in transmitting sensor data. Sensor nodes typically function on restricted battery power, so overall energy consumption and network lifespan become crucial concerns. Data aggregation is key to diminishing bottlenecks, burdens, and energy usage, extending the lifetime of WSNs. This paper introduces a new tree-based data aggregation technique, TDAC, which utilizes two popular metaheuristic algorithms, Ant Colony Optimization (ACO) and Cuckoo Search (CS), to deal with the NP-hardness of the data aggregation problem. A primary constraint of the ACO algorithm is the sluggish local search procedure. To address this shortcoming, the TDAC algorithm incorporates the CS algorithm to optimize the local search of the ACO algorithm, thereby enhancing the quality of solutions obtained. A series of comprehensive tests were carried out using the MATLAB simulator to assess the effectiveness of TDAC in comparison with previous techniques. The results indicate that TDAC outperforms benchmark techniques regarding network delay, longevity, energy consumption, and overhead. The integration of ACO and CS in TDAC greatly enhances data aggregation efficiency in WSNs. |
| format | Article |
| id | doaj-art-7d74caf04d144ab09ba7a00d6028dd80 |
| institution | DOAJ |
| issn | 1110-1903 2536-9512 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Journal of Engineering and Applied Science |
| spelling | doaj-art-7d74caf04d144ab09ba7a00d6028dd802025-08-20T03:22:45ZengSpringerOpenJournal of Engineering and Applied Science1110-19032536-95122025-06-0172112910.1186/s44147-025-00652-6A new tree-based data aggregation method in the wireless sensor networks using Ant Colony Optimization and Cuckoo search algorithmsShuling Yin0Jiahai Tu1Xiaoyan Chen2Hubei Open UniversityHubei Open UniversityHubei Open UniversityAbstract Prolonging wireless sensor networks (WSNs) longevity and minimizing energy expenses represent the primary considerations in transmitting sensor data. Sensor nodes typically function on restricted battery power, so overall energy consumption and network lifespan become crucial concerns. Data aggregation is key to diminishing bottlenecks, burdens, and energy usage, extending the lifetime of WSNs. This paper introduces a new tree-based data aggregation technique, TDAC, which utilizes two popular metaheuristic algorithms, Ant Colony Optimization (ACO) and Cuckoo Search (CS), to deal with the NP-hardness of the data aggregation problem. A primary constraint of the ACO algorithm is the sluggish local search procedure. To address this shortcoming, the TDAC algorithm incorporates the CS algorithm to optimize the local search of the ACO algorithm, thereby enhancing the quality of solutions obtained. A series of comprehensive tests were carried out using the MATLAB simulator to assess the effectiveness of TDAC in comparison with previous techniques. The results indicate that TDAC outperforms benchmark techniques regarding network delay, longevity, energy consumption, and overhead. The integration of ACO and CS in TDAC greatly enhances data aggregation efficiency in WSNs.https://doi.org/10.1186/s44147-025-00652-6Wireless sensor networksNetwork lifetimeData aggregationCuckoo searchAnt Colony Optimization |
| spellingShingle | Shuling Yin Jiahai Tu Xiaoyan Chen A new tree-based data aggregation method in the wireless sensor networks using Ant Colony Optimization and Cuckoo search algorithms Journal of Engineering and Applied Science Wireless sensor networks Network lifetime Data aggregation Cuckoo search Ant Colony Optimization |
| title | A new tree-based data aggregation method in the wireless sensor networks using Ant Colony Optimization and Cuckoo search algorithms |
| title_full | A new tree-based data aggregation method in the wireless sensor networks using Ant Colony Optimization and Cuckoo search algorithms |
| title_fullStr | A new tree-based data aggregation method in the wireless sensor networks using Ant Colony Optimization and Cuckoo search algorithms |
| title_full_unstemmed | A new tree-based data aggregation method in the wireless sensor networks using Ant Colony Optimization and Cuckoo search algorithms |
| title_short | A new tree-based data aggregation method in the wireless sensor networks using Ant Colony Optimization and Cuckoo search algorithms |
| title_sort | new tree based data aggregation method in the wireless sensor networks using ant colony optimization and cuckoo search algorithms |
| topic | Wireless sensor networks Network lifetime Data aggregation Cuckoo search Ant Colony Optimization |
| url | https://doi.org/10.1186/s44147-025-00652-6 |
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