Divisive hierarchical clustering for energy saving and latency reduction in UAV-assisted WSANs
Abstract In response to the harsh and limited conditions prevalent in remote areas, wireless sensor and actuator networks (WSANs) play an essential role in Internet-of-Things systems by monitoring and interacting with unattended environments. However, the sensors employed by the majority of WSANs ar...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
SpringerOpen
2025-01-01
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| Series: | EURASIP Journal on Wireless Communications and Networking |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s13638-024-02425-w |
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| Summary: | Abstract In response to the harsh and limited conditions prevalent in remote areas, wireless sensor and actuator networks (WSANs) play an essential role in Internet-of-Things systems by monitoring and interacting with unattended environments. However, the sensors employed by the majority of WSANs are powered by batteries, ensuring the efficient use and conservation of energy is vital for guaranteeing network connectivity and efficiency. To address this challenge, we proposed a divisive hierarchical clustering method based on K-means++ to organize the sensors. The intra-class distance of the cluster is fully taken into account to achieve the balance and full utilization of node energy. Furthermore, we utilize unmanned aerial vehicles (UAVs) for simultaneous data collection and develop a modified improved partheno genetic algorithm incorporating the Davies–Bouldin index for UAV scheduling. This approach effectively reduces network delay and balances network load. Numerical simulations demonstrate that our proposed method not only extends network lifetime but also balances energy savings and data collection latency. |
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| ISSN: | 1687-1499 |