In everyday life, the Wireless Sensor Network has attained high demand increasingly since it provides more network structure to create various kinds of innovative real-time applications. One of the essential applications of WSN is target coverage. Forest, agriculture, underwater, terrorism, and other applications have used the target coverage model following its nature. Existing target coverage models are not efficient and continuous, and the application performance is poor. The above-said problem has taken into account, and various earlier research works proposed a different target coverage model, not up to the application requirement. This paper focused on providing an efficient target coverage model for various real-time applications. Thus, a complete, continuous, target coverage model is created for environmental monitoring applications using a novel Termite Flies Optimization (TFO) algorithm. Based on the termite fly's movement, distance, targets are covered by optimal sensor nodes. From the experiment, it is found that the proposed TFO algorithm outperforms the existing approaches.

在日常生活中，由于无线传感器网络提供了更多的网络结构来创建各种创新的实时应用程序，因此它的需求越来越高。WSN 的基本应用之一是目标覆盖。森林、农业、水下、恐怖主义和其他应用程序已经使用了其性质的目标覆盖模型。现有的目标覆盖模型效率不高、不连续，应用性能较差。考虑到上述问题，早期的各种研究工作提出了不同的目标覆盖模型，不能满足应用需求。本文着重于为各种实时应用程序提供有效的目标覆盖模型。因此，一个完整的、连续的、目标覆盖模型是使用新颖的白蚁优化 (TFO) 算法为环境监测应用程序创建的。基于白蚁飞的运动、距离、目标被最佳传感器节点覆盖。从实验中发现，所提出的 TFO 算法优于现有方法。