Energy efficient hierarchical clustering based dynamic data fusion algorithm for wireless sensor networks in smart agriculture
Abstract A potential strategy to increase agricultural yields and maximize resource use has emerged: smart agriculture. In order to monitor numerous environmental characteristics, wireless sensor networks (WSNs) are essential. Individual sensor data may be noisy, redundant, and not correctly reflect...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-02-01
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| Series: | Scientific Reports |
| Subjects: | |
| Online Access: | https://doi.org/10.1038/s41598-024-85076-7 |
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| Summary: | Abstract A potential strategy to increase agricultural yields and maximize resource use has emerged: smart agriculture. In order to monitor numerous environmental characteristics, wireless sensor networks (WSNs) are essential. Individual sensor data may be noisy, redundant, and not correctly reflect the status of the farm as a whole. The energy constraints of WSN nodes and the need for accurate event detection, however, make it difficult to develop reliable and efficient systems. This research proposes a fresh approach to these issues by using hierarchical clustering-based dynamic data fusion techniques for WSNs in smart agriculture. In order to increase energy efficiency and event detection precision in smart agriculture, this study suggests employing dynamic data fusion for WSNs that is based on hierarchical clustering. The hierarchical clustering technique is used initially in the suggested method to group sensor nodes into clusters. A dynamic data fusion method is used to collect and fuse data inside each cluster, generating indicative information about the cluster’s status. This guarantees effective network resource utilization while minimizing data redundancy. In order to classify and anticipate events, the Extreme Learning Machine (ELM) technology is also used, allowing for the real-time identification of key events. The experimental outcomes show considerable increases in energy effectiveness and event detection precision, which makes this strategy an important contribution to the field of smart agriculture. The proposed model is implemented in Python software and has an accuracy of about 99.54% which is 1.81% higher than other existing methods like CH selection, K- prediction and data aggregation. |
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| ISSN: | 2045-2322 |