Localization and correction of location information for nodes in UWSN-LCLI

Abstract

Location is essential to identify the floating senor nodes in underwater. Underwater Sensor Networks (UWSNs) enable various critical applications like monitoring underwater environment, flora and fauna, sea life, exploration such as earthquake, disaster mitigation, motion tracking. In many cases, an underwater sensor node is necessary. Some points make the underwater study different and slightly complex. The first point is that radio signal cannot detect the underwater environment, despite radio waves acoustic signals used, which runs due to water currents. The second point is that earthy networks are deployed in the 2D environment, but 3D node deployment is needed in the Underwater environment. The third important point is locating the sensor node position in 3D deployment because nodes move (Floating Nodes) with water waves. It is a very complex assumption to fix the location of the sensor node.Various locations finding algorithms are proposed for the earthy network. Still, the underwater acoustic communication channel's unique properties, like high and varying transmission delay, required the Algorithm to locate the node's position. Only a few localization algorithms are proposed and presented in recent years to address these location finding requirements. Existing algorithms are distributed, scalable, and iterative in nature, but at the same Time, these algorithms use many acknowledgement messages for location identification and communication. To reduce the number of messages and to overcome the transmission, a new Location Finding and Correcting Algorithm for underwater acoustic sensor networks has been proposed in this paper. Our proposed Algorithm (LCLI- Localization and Correction of Location Information for nodes in UWSNs) find the Location of nodes accurately and efficient by considering localization errors. In this paper, we have also proposed a location correcting Algorithm that may use when the localization errors are more. Our proposed Algorithm (UWSN-LCLI) is compared with existing proposed algorithms, and our Algorithm outperforms and shows significant improvement.