LEACH Protocol Optimization Based on Weighting Strategy and the Improved Ant Colony Algorithm
This article aims to address problems in the current clustering process of low-energy adaptive clustering hierarchy (LEACH) in the wireless sensor networks, such as strong randomness and local optimum in the path optimization. This article proposes an optimal combined weighting (OCW) and improved an...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2022-03-01
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| Series: | Frontiers in Neurorobotics |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fnbot.2022.840332/full |
| _version_ | 1818338391401431040 |
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| author | Xuezhen Cheng Chuannuo Xu Xiaoqing Liu Xiaoqing Liu Jiming Li Junming Zhang |
| author_facet | Xuezhen Cheng Chuannuo Xu Xiaoqing Liu Xiaoqing Liu Jiming Li Junming Zhang |
| author_sort | Xuezhen Cheng |
| collection | DOAJ |
| description | This article aims to address problems in the current clustering process of low-energy adaptive clustering hierarchy (LEACH) in the wireless sensor networks, such as strong randomness and local optimum in the path optimization. This article proposes an optimal combined weighting (OCW) and improved ant colony optimization (IACO) algorithm for the LEACH protocol optimization. First, cluster head nodes are updated via a dynamic replacement mechanism of the whole network cluster head nodes to reduce the network energy consumption. In order to improve the quality of the selected cluster head nodes, this article proposes the OCW method to dynamically change the weight according to the importance of the cluster head node in different regions, in accordance with the three impact factors of the node residual energy, density, and distance between the node and the sink node in different regions. Second, the network is partitioned and the transmission path among the clusters can be optimized by the transfer probability in IACO with combined local and global pheromone update mechanism. The efficacy of the proposed LEACH protocol optimization method has been verified with MATLAB simulation experiments. |
| first_indexed | 2024-12-13T15:10:22Z |
| format | Article |
| id | doaj.art-b4499543155a46d79e003ff7a21fb78a |
| institution | Directory Open Access Journal |
| issn | 1662-5218 |
| language | English |
| last_indexed | 2024-12-13T15:10:22Z |
| publishDate | 2022-03-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Neurorobotics |
| spelling | doaj.art-b4499543155a46d79e003ff7a21fb78a2022-12-21T23:40:54ZengFrontiers Media S.A.Frontiers in Neurorobotics1662-52182022-03-011610.3389/fnbot.2022.840332840332LEACH Protocol Optimization Based on Weighting Strategy and the Improved Ant Colony AlgorithmXuezhen Cheng0Chuannuo Xu1Xiaoqing Liu2Xiaoqing Liu3Jiming Li4Junming Zhang5College of Electrical Engineering and Automation, Shandong University of Science and Technology, Qingdao, ChinaCollege of Electrical Engineering and Automation, Shandong University of Science and Technology, Qingdao, ChinaCollege of Electrical Engineering and Automation, Shandong University of Science and Technology, Qingdao, ChinaShandong Senter Electronic Co, Zibo, ChinaCollege of Electrical Engineering and Automation, Shandong University of Science and Technology, Qingdao, ChinaCollege of Energy and Mining Engineering, Shandong University of Science and Technology, Qingdao, ChinaThis article aims to address problems in the current clustering process of low-energy adaptive clustering hierarchy (LEACH) in the wireless sensor networks, such as strong randomness and local optimum in the path optimization. This article proposes an optimal combined weighting (OCW) and improved ant colony optimization (IACO) algorithm for the LEACH protocol optimization. First, cluster head nodes are updated via a dynamic replacement mechanism of the whole network cluster head nodes to reduce the network energy consumption. In order to improve the quality of the selected cluster head nodes, this article proposes the OCW method to dynamically change the weight according to the importance of the cluster head node in different regions, in accordance with the three impact factors of the node residual energy, density, and distance between the node and the sink node in different regions. Second, the network is partitioned and the transmission path among the clusters can be optimized by the transfer probability in IACO with combined local and global pheromone update mechanism. The efficacy of the proposed LEACH protocol optimization method has been verified with MATLAB simulation experiments.https://www.frontiersin.org/articles/10.3389/fnbot.2022.840332/fulloptimal combination weightingimproved ant colony optimizationpath superiorityLEACH optimizationrouting protocol |
| spellingShingle | Xuezhen Cheng Chuannuo Xu Xiaoqing Liu Xiaoqing Liu Jiming Li Junming Zhang LEACH Protocol Optimization Based on Weighting Strategy and the Improved Ant Colony Algorithm Frontiers in Neurorobotics optimal combination weighting improved ant colony optimization path superiority LEACH optimization routing protocol |
| title | LEACH Protocol Optimization Based on Weighting Strategy and the Improved Ant Colony Algorithm |
| title_full | LEACH Protocol Optimization Based on Weighting Strategy and the Improved Ant Colony Algorithm |
| title_fullStr | LEACH Protocol Optimization Based on Weighting Strategy and the Improved Ant Colony Algorithm |
| title_full_unstemmed | LEACH Protocol Optimization Based on Weighting Strategy and the Improved Ant Colony Algorithm |
| title_short | LEACH Protocol Optimization Based on Weighting Strategy and the Improved Ant Colony Algorithm |
| title_sort | leach protocol optimization based on weighting strategy and the improved ant colony algorithm |
| topic | optimal combination weighting improved ant colony optimization path superiority LEACH optimization routing protocol |
| url | https://www.frontiersin.org/articles/10.3389/fnbot.2022.840332/full |
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