Wireless sensor network (WSN) is applicable in all IoT applications, thus it has many advancements. However, it has many drawbacks like localization, link failure, and so on. In addition, the reduction of received signal strength (RSS) often causes path loss, while transferring the data when the path is lost then it drops the packets. To address this problem, the current research aimed to develop a novel grey wolf ant lion recurrent (GWALR) localization model in WSN to find the location of each unknown node. Moreover, the fitness function of GWALR is utilized to track the location of each node. The key focus of this proposed model is to find the location of unknown nodes and to improve the RSS by reducing the localization error. In addition, the model that attained high RSS measure has better data broadcasting rate. Finally, the performance of the proposed approach is compared with existing works and attained better accuracy and reduced error rate. Thus the outcome of the proposed model proved the efficiency of the proposed work by gaining maximum throughput ratio as 7000bps, data broadcasting rate as 99%, accuracy 99.8% and reduced error rate as 1.4%.

无线传感器网络（WSN）适用于所有IoT应用程序，因此具有许多进步。但是，它具有许多缺点，例如本地化，链接故障等。另外，接收信号强度（RSS）的降低通常会导致路径丢失，而当路径丢失时传输数据会丢失数据包。为了解决这个问题，当前的研究旨在在WSN中开发一种新颖的灰狼蚂蚁复发（GWALR）定位模型，以找到每个未知节点的位置。此外，利用GWALR的适应度函数来跟踪每个节点的位置。该模型的重点是寻找未知节点的位置，并通过减少定位误差来改进RSS。另外，获得较高RSS度量的模型具有更好的数据广播速率。最后，将该方法的性能与现有工作进行了比较，获得了更好的准确性和更低的错误率。因此，所提出模型的结果通过获得最大吞吐率（7000bps），数据广播率（99％），准确性（99.8％）和降低的错误率（1.4％）证明了所提出工作的效率。