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Optimization for connectivity and coverage issue in target‐based wireless sensor networks using an effective multiobjective hybrid tunicate and salp swarm optimizer
International Journal of Communication Systems ( IF 2.1 ) Pub Date : 2020-11-22 , DOI: 10.1002/dac.4679
Jehan Chelliah 1 , Navaz Kader 2
Affiliation  

The two main important issues in designing target‐based wireless sensor networks (WSNs) are coverage and connectivity maximization. In order to tackle the coverage and connectivity problems, we have proposed a hybrid optimization‐based model. Thereby, the target‐based WSNs can include the sensor nodes which are placed based on determining minimum number of selected potential positions. To do that, an optimization approach based on a hybrid tunicate swarm optimizer (TSO) and salp swarm optimizer (SSO) is proposed for coverage and connectivity problems in WSNs. Feature exploitation ability of SSO is improved by TSO operators, because these operators can act as local search operators. Basically, these hybrid algorithm operators are used to derive the fitness function followed with the solution representation step. Initially, the population is generated, and the hybrid tunicate swarm optimizer and salp swarm optimizer (HTSS) algorithm has updated the solutions in the next subsequent step. Ultimately, the nondominated solutions are determined in the final step. The two different scenarios of WSN are used for simulation of this scheme. Simulations have shown its outstanding performance in solving the coverage and connectivity problems in wireless networks.

中文翻译:

使用有效的多目标混合包被和蜂群优化器优化基于目标的无线传感器网络中的连接性和覆盖范围问题

设计基于目标的无线传感器网络(WSN)时,两个主要的重要问题是覆盖范围和连接性最大化。为了解决覆盖和连接问题,我们提出了一种基于混合优化的模型。因此,基于目标的WSN可以包括基于确定所选潜在位置的最小数量而放置的传感器节点。为此,针对WSN中的覆盖和连接问题,提出了一种基于混合被膜群优化器(TSO)和萨尔普群优化器(SSO)的优化方法。TSO运营商提高了SSO的功能开发能力,因为这些运营商可以充当本地搜索运营商。基本上,这些混合算法运算符用于导出适应度函数以及随后的解决方案表示步骤。原来,生成种群,并且混合的被膜群优化器和蜂群优化器(HTSS)算法已在下一后续步骤中更新了解决方案。最终,在最后一步中确定非主导解决方案。WSN的两种不同方案用于此方案的仿真。仿真显示了其在解决无线网络的覆盖和连接问题方面的出色性能。
更新日期:2021-01-04
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