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Optimal Coverage of Wireless Sensor Network using Termite Colony Optimization Algorithm

机译:使用白蚁菌落优化算法的无线传感器网络的最佳覆盖

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Due to great advancement in the field of science and technology, Wireless Sensor Network (WSN) has been become one of the most attractive and intriguing research area in last few years. Large-scale sensor networks has been deployed by interconnecting several hundred to a few thousand sensors nodes opens up diverse technical challenges and tremendous application possibilities. This paper presents a Termite Colony Optimization (TCO) algorithm, an adaptable and balanced way, which optimizes a minimal number of sensors while covering a maximum area. TCO is a population based metaheuristic approach developed from the intelligent behavior of termite that is known to us as Swarm Intelligence. It has the ability to solve the presented problem in efficient way that has shown more effectiveness and robustness compared to other population based algorithms. In this paper, we have justified our simulation results with the results of other papers. After that we have experimented with varying different parameters like grid size, number of sensors and termites. It has also proved the capability of our TCO over the constraints and difficulties as well.
机译:由于科学技术领域的巨大进步,无线传感器网络(WSN)已成为过去几年中最具吸引力和有趣的研究领域之一。通过互连数百到几千个传感器节点部署了大规模的传感器网络,开辟了不同的技术挑战和巨大的应用可能性。本文介绍了白蚁殖民地优化(TCO)算法,适应性和平衡的方式,其在覆盖最大面积的同时优化最小数量的传感器。 TCO是一种基于人口的成分型方法,从我们作为群智能所知的白蚁的智能行为发展。与其他基于群体的算法相比,它有能力以有效的方式解决呈现的问题,这些方法已经为与其他群体的算法相比。在本文中,我们对我们的模拟结果进行了证明的其他论文的结果。之后,我们已经尝试了不同的不同参数,如网格尺寸,传感器数量和白蚁。它还证明了我们的TCO对限制和困难的能力。

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