Xuan Zhang, Heejung Byun, Journal of Internet Computing and Services, Vol. 26, No. 1, pp. 1-7, Feb. 2025
10.7472/jksii.2025.26.1.1, Full Text:  HTML
Keywords: WSANs, divisive hierarchical clustering, K-means++, UAVs

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 (IoT) systems by monitoring and interacting with unattended environments. Sensor nodes employed by the majority of WSANs are powered by batteries. Therefore, ensuring the efficient use and conservation of energy is vital for guaranteeing network connectivity. To address this challenge, we proposed a divisive hierarchical clustering method based on the K-means++ to organize the sensors nodes. 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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