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<p>Internet of Things (IoT)-based Unmanned Aerial Vehicles (UAV) network plays a crucial role in various domains such as agriculture, environmental monitoring and smart cities. This network senses the environmental conditions and transmits the data via unmanned aerial vehicles resulting in safer and secure communication. Generally, unmanned aerial vehicles transmit data relatively quickly due to their incredible mobility. High mobility in the UAV network stimulates challenges such as link failure, signal propagation issues and degradation of network performance. To overcome these challenges, a new methodology called UAV Clustering with Stable Backup node (UCSB) is proposed to control and manage mobility in IoT-based UAV networks. The proposed scheme forms the cluster based on the Kőnig’s theorem in the network. Each cluster node has a stable backup node with high energy. If the cluster head nodes drain energy, then the energy is received from the stable backup node for data transmission. After the completion of the cluster formation, a centralized architecture approach is implemented. The cluster head holds control over all the cluster members. If the cluster members are required to join/leave the cluster, then the cluster head grants the request. Cluster heads are aware of the locations, bandwidth levels, and routing metrics of all cluster members within range. Hence, this proposed methodology reduces energy consumption by utilizing stable backup nodes and controls mobility through UAV clustering strategy. The proposed scheme addresses existing research issues by proactively managing energy consumption through stable backup nodes, ensuring seamless mobility control and enhanced network reliability. The simulation results of Quality of Service (QoS) parameters such as throughput, delay, delivery ratio, drop rate, link rate, normalized routing overhead and control overhead were calculated for the proposed system. The proposed methodology provides better network performance when compared to other existing mobility management techniques.</p>

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Efficient UAV clustering with stable backup nodes for mobility management in UAV based IoT network

  • 期刊论文

  • 发表日期：

    2025年11月26日

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  • A. Rajashekar,
  • Dharamendra Chouhan

摘要

Internet of Things (IoT)-based Unmanned Aerial Vehicles (UAV) network plays a crucial role in various domains such as agriculture, environmental monitoring and smart cities. This network senses the environmental conditions and transmits the data via unmanned aerial vehicles resulting in safer and secure communication. Generally, unmanned aerial vehicles transmit data relatively quickly due to their incredible mobility. High mobility in the UAV network stimulates challenges such as link failure, signal propagation issues and degradation of network performance. To overcome these challenges, a new methodology called UAV Clustering with Stable Backup node (UCSB) is proposed to control and manage mobility in IoT-based UAV networks. The proposed scheme forms the cluster based on the Kőnig’s theorem in the network. Each cluster node has a stable backup node with high energy. If the cluster head nodes drain energy, then the energy is received from the stable backup node for data transmission. After the completion of the cluster formation, a centralized architecture approach is implemented. The cluster head holds control over all the cluster members. If the cluster members are required to join/leave the cluster, then the cluster head grants the request. Cluster heads are aware of the locations, bandwidth levels, and routing metrics of all cluster members within range. Hence, this proposed methodology reduces energy consumption by utilizing stable backup nodes and controls mobility through UAV clustering strategy. The proposed scheme addresses existing research issues by proactively managing energy consumption through stable backup nodes, ensuring seamless mobility control and enhanced network reliability. The simulation results of Quality of Service (QoS) parameters such as throughput, delay, delivery ratio, drop rate, link rate, normalized routing overhead and control overhead were calculated for the proposed system. The proposed methodology provides better network performance when compared to other existing mobility management techniques.