UASN-3D: An energy efficient localization based on LEACH-BR algorithm (EELBL-BR)

Underwater acoustic sensor networks (UASN) research is gaining popularity as it has various applications such as military communication, oil rig maintenance, animal survival, linking submarines to land, gathering data for water monitoring, and other commercial fields. The two most critical requirements for the application's proper operation are the accurate knowledge of sensor node locations and the efficient transmission of accurate underwater sensor node information to the base station with efficient energy consumption. The proposed energy efficient localization based on the LEACH-beacon and reinforced node (EELBL-BR) algorithm satisfies both the requirements in 3D-UASN. The proposed algorithm considers the deployment and computation of accurate location of sensor nodes in the underwater environment by applying I-LASP (improvement of localization algorithm for compensating stratification effect based on extended improved particle swarm optimization technique) (Yadav N, Khilar PM. Trans Emerg Telecommun Technol. 2023;34:e4772.) for 3D environment. It performs clustering of sensor nodes for enhancing network lifetime using three different types of nodes such as beacon, reinforced, and member nodes. The proposed clustering LEACH-BR (low-energy adaptive clustering hierarchy-beacon and reinforced nodes) algorithm is based upon the LEACH algorithm which provides accurate location of all the sensor nodes, improves energy consumption and reliability in the underwater environment. The result shows that the proposed algorithm EELBL-BR, considering both beacon and reinforced nodes, provides the improvement in the number of alive nodes, reduction in the number of dead nodes, reduction in energy consumption and enhances residual energy in the UASN by 68.90%, 51.91%, 51.47%, and 68.12% respectively with respect to the number of rounds as compared to that of the existing algorithm by Rizvi et al. (Wirel Pers Commun. 2022;124(4):3725-3741.) and thus outperforms the existing algorithm (Rizvi HH, Khan SA, Enam RN. Wirel Pers Commun. 2022;124(4):3725-3741.).