Abstract
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%.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
