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Title ENERGY-EFFICIENT NODE LOCALIZATION AND TRACKING FOR REAL-TIME UWSN APPLICATIONS

Author(s) Zahra Khalid Satti

Abstract Depletion of terrestrial resources has driven human exploration towards underwater realms, where challenges such as diminished optical clarity and increased hydrostatic pressure hinder effective communication and examination considering acoustic waves. The use of electromagnetic (EM) Underwater Wireless Sensor Networks (UWSNs) has gained favor due to their low cost, higher data rate, minimum propagation delay compared to acoustic, but long-range underwater communication remains challenging. This research proposes a methodology to address these challenges, emphasizing the development of an efficient node localization and tracking system for UWSNs. The approach involves segmenting UWSNs considering real time applications into two major steps one is Autonomous Underwater Vehicles (AUVs)/ or implementing dynamic courier node for localization/tracking of sensor nodes and data transmission to an offshore base station (BS). The study also highlights the dynamic nature of ocean depth and its challenges to underwater networking. To mitigate disruptions, the research focuses on deploying sensor nodes randomly (Gaussian distribution) at various oceanic depths. This research also tackles the challenges inherent in UWSNs by proposing a novel method to improve tracking and localization efficiency in terms of 3D trajectory. The primary issues addressed include communication and data collection difficulties in underwater environments due to limited light penetration and high pressure, which affect equipment functionality. By utilizing Bayesian inference and Kalman filtering, the research attempts to create a reliable and accurate state estimation technique for UWSNs. In the suggested methodology, the Extended Kalman Filter (EKF), a well-known instrument for state estimation in linear systems with Gaussian noise, is employed. When handling several sensors or information sources, though, it could not be up to par. Through the integration of Bayesian approaches, the suggested methodology improves the performance of EKF. This results in the creation of a framework that mixes and integrates data from numerous KFs. Based on sensor measurement, the proposed methodology updates the state estimate using Bayes' theorem and expresses uncertainty as probabilities. Significant RMSE reduction as compared to the KF method's RMSE value of 0.1 to 0.5 meters possible using the suggested approach. The novel approach's performance was validated through the use of MATLAB and EKF, along with real-time data obtained from the National Centers for Environment Information. In order to increase the precision and effectiveness of object tracking and localization in UWSNs, the Helmholtz approach is applied to simulations based on ocean data to characterize dynamic underwater communication channels. v Performance evaluation measures include root mean square error (RMSE), estimate error, and convergence time. The analysis shows that the proposed strategy for tracking nodes and localizing them in UWSNs is significantly better than the current approaches. The suggested protocol, in instance, leverages more effective routing and data transfer to reduce energy consumption by thirty percent. Node efficiency gains twenty percent in shallow and mid-water environments and twenty percent in deep-water settings. The reason for these gains is a decreased Root Mean Square Error (RMSE) in localization, which decreases the need for energy-intensive error correction procedures, hence improving overall energy efficiency and extending the operational lifetime of UWSNs.

Type Thesis/Dissertation MS

Faculty Engineering and Computer Science

Department Computer Science

Language English

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