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Optimal Sink Node Placement in Large Scale Wireless Sensor Networks Based on Harris’ Hawk Optimization Algorithm

机译:基于哈里斯鹰优化算法的大规模无线传感器网络中的最佳宿节点放置

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Large-scale wireless sensor network (LSWSN) is composed of a huge number of sensor nodes that are distributed in some region of interest (ROI), to sense and measure the environmental conditions like pressure, temperature, pollution levels, humidity, wind, and so on. The objective is to collect data for real-time monitoring so that appropriate actions can be taken promptly. One of the sensor nodes used in an LSWSN is called the sink node, which is responsible for processing and analyzing the collected information. It works as a station between the network sensor nodes and the administrator. Also, it is responsible for controlling the whole network. Determining the sink node location in an LSWSN is a challenging task, as it is crucial to the network lifetime, for keeping the network activity to the most possible extent. In this paper, the Harris' hawks optimization (HHO) algorithm is employed to solve this problem and subsequently the Prim's shortest path algorithm is used to reconstruct the network by making minimum transmission paths from the sink node to the rest of the sensor nodes. The performance of HHO is compared with other well-known algorithms such as particle swarm optimization (PSO), flower pollination algorithm (FPA), grey wolf optimizer (GWO), sine cosine algorithm (SCA), multi-verse optimizer (MVO), and whale optimization algorithm (WOA). The simulation results of different network sizes, with single and multiple sink nodes, show the superiority of the employed approach in terms of energy consumption and localization error, and ultimately prolonging the lifetime of the network in an efficacious way.
机译:大规模无线传感器网络(LSWSN)由分布在某些感兴趣区域(ROI)的大量传感器节点组成,感知和测量像压力,温度,污染水平,湿度,风和湿度和风力等的环境条件很快。目标是收集实时监控数据,以便可以及时采取适当的操作。 LSWSN中使用的一个传感器节点称为宿节点,负责处理和分析所收集的信息。它用作网络传感器节点和管理员之间的电台。此外,它负责控制整个网络。确定LSWSN中的水槽节点位置是一个具有挑战性的任务,因为它对网络寿命至关重要,用于将网络活动保持在最大程度上。在本文中,采用Harris的Hawks优化(HHO)算法来解决这个问题,随后使用Prim的最短路径算法来通过将来自宿节点的最小传输路径与传感器节点的其余部分进行最小传输路径来重建网络。与其他众所周知的算法相比,诸如粒子群优化(PSO),花授粉算法(FPA),灰狼优化器(GWO),正弦余弦算法(SCA),多韵优化器(MVO)等其他众所周知的算法进行比较。和鲸鱼优化算法(WOA)。不同网络尺寸的仿真结果,单个和多个宿节点,在能量消耗和本地化误差方面显示了采用的方法的优越性,并最终以有效的方式延长了网络的寿命。

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