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Hybrid optimization routing management for autonomous underwater vehicle in the internet of underwater things
Earth Science Informatics ( IF 2.8 ) Pub Date : 2020-10-20 , DOI: 10.1007/s12145-020-00538-6
Y. Harold Robinson , S. Vimal , E. Golden Julie , Manju Khari , Christopher Expósito-Izquierdo , Javier Martínez

Internet of Underwater Things (IoUTs) is completely embraced by acoustic sensor nodes that are commonly battery consumption. The sensor’s battery life is restricted and it is problematic while it needs to recharge. Moreover, these kinds of underwater sensor nodes may form the cluster to store the huge amount of energy. The cluster head formation is the main problem that the cluster head needs to use added energy for aggregation and data collection to broadcast the data packets to the destination. The autonomous underwater vehicles (AUVs) have to be used to connect the sensor nodes with the internet or particular devices, the path discovery within the AUV is the primary issue for producing the efficient routing in IoUTs. In this paper, the proposed routing strategy is used to provide energy proficiency while performing the cluster-based routing. AUV is responsible for selecting the cluster head and maintaining cluster-based scheduling. The AUV path is constructed to increase the residual energy for the sensor nodes in the network. It coordinates the AUV’s effective way of arranging calculations into the steering convention. This incorporated specialized strategy depends on two stages: A-ANTD (AUV-Assisted Network Transmit Devising) and TARD (Transmit Assisted Route Devising). A-ANTD uses the collaboration of performance of multiple ventures to decrease energy utilization for the system and stays away from the problem area and sector issue with a vital GN (Gateway Nodules) plans. The experimental results show that the proposed methodology has improved performance compared with the other methods.



中文翻译:

水下物联网中自主水下航行器的混合优化路由管理

水下物联网(IoUT)完全被通常消耗电池的声音传感器节点所包含。传感器的电池寿命受到限制,在需要充电时会出现问题。而且,这些种类的水下传感器节点可以形成簇以存储大量能量。群集头的形成是群集头需要使用额外的能量进行聚合和数据收集以将数据包广播到目标的主要问题。必须使用自动水下航行器(AUV)将传感器节点与Internet或特定设备连接,AUV内的路径发现是在IoUT中产生有效路由的主要问题。在本文中,提出的路由策略用于在执行基于群集的路由时提高能源效率。AUV负责选择集群头并维护基于集群的调度。AUV路径构造为增加网络中传感器节点的剩余能量。它协调了AUV将计算安排到转向约定中的有效方式。这种合并的专业策略取决于两个阶段:A-ANTD(AUV辅助网络传输设计)和TARD(传输辅助路线设计)。A-ANTD利用多家企业的绩效协作来降低系统的能源利用率,并通过重要的GN(网关结节)计划来避免问题区域和部门问题的发生。实验结果表明,与其他方法相比,该方法具有更好的性能。AUV路径构造为增加网络中传感器节点的剩余能量。它协调了AUV将计算安排到转向约定中的有效方式。这种合并的专业策略取决于两个阶段:A-ANTD(AUV辅助网络传输设计)和TARD(传输辅助路线设计)。A-ANTD利用多家企业的绩效协作来降低系统的能源利用率,并通过重要的GN(网关结节)计划来避免问题区域和部门问题的发生。实验结果表明,与其他方法相比,该方法具有更好的性能。AUV路径构造为增加网络中传感器节点的剩余能量。它协调了AUV将计算安排到转向约定中的有效方式。这种合并的专业策略取决于两个阶段:A-ANTD(AUV辅助网络传输设计)和TARD(传输辅助路线设计)。A-ANTD利用多家企业的绩效协作来降低系统的能源利用率,并通过重要的GN(网关结节)计划来避免问题区域和部门问题的发生。实验结果表明,与其他方法相比,该方法具有更好的性能。这种合并的专业策略取决于两个阶段:A-ANTD(AUV辅助网络传输设计)和TARD(传输辅助路线设计)。A-ANTD利用多家企业的绩效协作来降低系统的能源利用率,并通过重要的GN(网关结节)计划来避免问题区域和部门问题的发生。实验结果表明,与其他方法相比,该方法具有更好的性能。这种合并的专业策略取决于两个阶段:A-ANTD(AUV辅助网络传输设计)和TARD(传输辅助路线设计)。A-ANTD利用多家企业的绩效协作来降低系统的能源利用率,并通过重要的GN(网关结节)计划来避免问题区域和部门问题的发生。实验结果表明,与其他方法相比,该方法具有更好的性能。

更新日期:2020-10-20
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