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A comparative study on performances of sensor deployment algorithms in WSN

机译:WSN中传感器部署算法性能的比较研究

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Deployment of mobile sensors to achieve full coverage of a region of interest and connectivity within sensors are the most significant and practically challenging issues for increasing lifetime of a wireless sensor network (WSN). Extensive researches have been carried out in recent past to address these issues and many deployment algorithms have been developed. However, for a decision maker it is very important to adopt an objective-specific suitable algorithm for deployment of sensors to address various practical aspects in real time. In this paper, we present a comparative study on performances of three well-developed deployment algorithms such as grid-based self-deployment and algorithms utilizing optimization techniques such as particle swarm and genetic algorithm. Performances of these three deployment schemes are evaluated in terms of coverage, uniformity, connectivity, and computational time in absence of any obstacles as well as in presence of randomly placed few unknown obstacles in an area of interest. The effect of initial positions of sensors on the performances of these three algorithms is also investigated. To achieve this objective, a series of simulation experiments are conducted and results are presented in this paper.
机译:移动传感器的部署以实现感兴趣区域和传感器内的连接的完全覆盖是用于增加无线传感器网络(WSN)的寿命的最重要和实际上挑战的问题。最近已经进行了广泛的研究,以解决这些问题,并且已经开发了许多部署算法。但是,对于决策者来说,采用客观特定的合适算法来部署传感器,是非常重要的,以便实时解决各种实际方面。在本文中,我们提供了三种发达的部署算法的性能比较研究,例如基于网格的自我部署和利用粒子群和遗传算法等优化技术的算法。在没有任何障碍物的情况下,在没有任何障碍物的覆盖,均匀性,连接和计算时间方面评估这三种部署方案的性能以及在感兴趣区域中随机放置少量未知障碍物。还研究了传感器初始位置对这三种算法的性能的影响。为实现这一目标,进行了一系列仿真实验,并在本文中提出了结果。

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