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Sea lion optimization algorithm based node deployment strategy in underwater acoustic sensor network

机译:基于海狮优化算法基于水下声学传感器网络的节点部署策略

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摘要

In the ocean, huge number of sensor nodes (SNs) are located to transfer the information between other nodes using the Underwater Acoustic Sensor Network (UASN) framework. An underwater acoustic communication technique is utilized by this UASN to exchange the information. Because of environmental conditions and adverse channel, the SNs in UASN may have link breakages. Likewise, maximum target coverage rate for SN deployment is considered as another issue. So it is very essential to create a strong communication system in underwater together with the different kind of variations in ocean environment. As a result, the system will perform better data transmission with the severely fluctuating underwater communication conditions. In this paper, a latest optimization algorithm named as Sea Lion Optimization (SLO) procedure is proposed to discover the optimal location for SN in underwater communication. This algorithm optimally places the acoustic SNs based on the maximum connectivity rate by finding the targeted optimal position. The Matlab tool is utilized for implementation purpose, and the different kinds of parameters like connectivity rate, coverage rate, and delay are taken to evaluate the performance of proposed methodology. Moreover, the existing methods like deployment scheme, Connected Dominating set based depth computation Approach (CDA) approach, and distributive approach are taken to contrast the performance of proposed methodology. When compared to the previous algorithms, our proposed methodology achieves 95% connectivity ratio for varying number of acoustic SNs.
机译:在海洋中,定位了大量的传感器节点(SNS)以使用水下声学传感器网络(UASN)框架在其他节点之间传输信息。通过该UASN利用水下声学通信技术来交换信息。由于环境条件和不利渠道,UASN中的SNS可能具有链路破损。同样,SN部署的最大目标覆盖率被认为是另一个问题。因此,在水下创建一个强大的通信系统以及海洋环境的不同类型,是至关重要的。结果,系统将利用严重波动的水下通信条件执行更好的数据传输。在本文中,提出了一种名为Sea Lion Optimization(SLO)程序的最新优化算法,以发现水下通信中SN的最佳位置。该算法通过找到目标最佳位置,最佳地将声学SNS放置在最大连接率。 MATLAB工具用于实现目的,以及相连的参数,如连接率,覆盖率和延迟等参数来评估所提出的方法的性能。此外,采用了部署方案,连接的主导集合的深度计算方法(CDA)方法和分配方法等现有方法来对比提出的方法的性能。与以前的算法相比,我们所提出的方法可以实现95%的连接率,以改变数量的声学SNS。

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