Abstract
Self-localization is the capability of a wireless sensor network (WSN) to estimate the location coordinates of a given target node (TN), using the location knowledge of few anchor node (AN)s. The ANs are the sensor nodes installed with GPS modules, and their locations are known in prior. Precision improvement of the TN's location is an important issue for effective data transmission in WSNs. Localization draws attention in location aware applications like nuclear attacks, object tracking, healthcare, supply chain management, biological attacks, and traffic monitoring, etc. In this article, a weight based AN selection strategy in localization algorithm for WSNs is proposed, which uses only four ANs where each one is installed with GPS module, and deployed within the sensing area. The weights are assigned to ANs depend on the distance between ANs and TNs to mitigate the effect of noise due to spatial and temporal changes in the sensing area. The proposed method uses improved cuckoo search algorithm, which is a nature inspired optimization technique to address the anisotropic nature of WSNs, and compute global optimal location coordinates of TNs. It also enhances the localization accuracy compared to the nascent localization algorithms in WSNs. Rigorous simulations are conducted to prove the efficiency of the proposed method using the performance metrics as number of ANs, mean localization accuracy, and communication range. Based on the simulation results, the localization accuracy of the proposed CERBLA algorithm is increased to 99.24% and the range measurement error is minimized to 1.18 m. The localization accuracy using the CERBLA algorithm is enhanced by 25.32%, 25.19%, 21.47%, 128.21%, 84.48%, and 181.35% compared to DCK-GWO, WOA-QT, DECPSODV-Hop, ECS-NL, QABA, and IDE-NSL-AWSN algorithms respectively. The performance of the CERBLA is outperforming the existing algorithms particularly, when the number of ANs are less than ten, and this feature is attractive to build cost-effective localization algorithms for indoor applications of WSNs.