Improved Asynchronous Localization Algorithm Based On Mobility Prediction For Underwater Acoustic Networks

Dr. Subhash Gokul Patil; Dr. Pinku Rajan; Dr. Sarang Maruti Patil; Dr. Gaurav Jain; Dr. Sucheta Moharir; Pallavi HJ; Kaminee Subhash Patil

doi:10.29284/pkvp8021

Authors

Dr. Subhash Gokul Patil R.C.Patel Institute of Technology, Shirpur, MH, Orcid ID:0009-0007-6049-2130
Dr. Pinku Rajan ABV Indian Institute of Information Technology and Management, Gwalior, Orcid ID:0000-0002-1422-5943
Dr. Sarang Maruti Patil Amity University, Mumbai, MH, Orcid ID:0000-0002-2849-2209
Dr. Gaurav Jain Poornima Unversity, Jaipur, Orcid ID:0000-0001-5272-6717
Dr. Sucheta Moharir St. Vincent Pallotti College of Engineering & Technology, Nagpur, Orcid ID: 0000-0003-3549-7064
Pallavi HJ Global Academy of Technology, Bengaluru
Kaminee Subhash Patil R.C.Patel Institute of Technology, Shirpur, MH, Orcid ID:0009-0006-7881-9670

DOI:

https://doi.org/10.29284/pkvp8021

Keywords:

Received signal strength (RSS), UASNs(underwater acoustic sensor networks), Lambert-W function, Durbin algorithm, Asynchronization

Abstract

accurate localization of sensor nodes is essential for the effective operation of Underwater Acoustic Sensor Networks (UASNs). Conventional wireless sensor network techniques cannot be directly applied underwater due to severe attenuation of radio waves and the difficulty of achieving precise time synchronization. To address these challenges, this study employs Received Signal Strength (RSS) as the primary distance-estimation metric, as it does not require clock synchronization and provides more reliable measurements in acoustic environments. We propose an enhanced localization algorithm capable of operating under clock asynchronization and node mobility. The algorithm estimates inter-node distances using RSS with the support of the Lambert-W function, enabling robust computation even in nonlinear conditions. Node mobility is incorporated by calculating velocity through the Durbin algorithm, after which initial coordinates and velocities are used to predict the final node positions at any given time. The improved approach meets the critical requirements of UASNs, including higher localization accuracy, efficient processing for dynamic nodes, and reduced computational cost, making it a practical and effective solution for real-time underwater applications.

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