Volume 18, Number 1

AOASS: Adaptive Obstacle-Aware Square Spiral Framework for Single-Mobile-Anchor-Based WSN Localization

  Authors

Abdelhady Naguib ^1,2, ¹Jouf University, Saudi Arabia, ²Al-Azhar University, Egypt

  Abstract

Accurate and energy-efficient localization remains a key challenge in Wireless Sensor Networks (WSNs), particularly when obstacles affect signal propagation. This study introduces AOASS (Adaptive Obstacle-Aware Square Spiral), a new single-mobile-anchor framework that combines an optimized square-spiral movement pattern with adaptive obstacle detection. The mobile anchor can sense and bypass obstacles while maintaining high localization accuracy and full network coverage, ensuring that each node receives at least three non-collinear beacon signals for reliable position estimation. Localization accuracy is further improved using the OLSTM-DV-Hop model, which integrates a Long Short-Term Memory (LSTM) network with the traditional DV-Hop algorithm to estimate hop distances better and reduce multi-hop errors. The anchor trajectory is managed by a TD3-LSTM reinforcement learning agent, supported by a Kalman-based prediction layer and a fuzzy-logic ORCA safety module for smooth and collision-free navigation. Simulation experiments across different obstacle densities show that AOASS consistently achieves higher localization accuracy, better energy efficiency, and more optimized trajectories than existing approaches. These results demonstrate the framework’s scalability and potential for real-world WSN applications, offering an intelligent and adaptable solution for data-driven IoT systems.

  Keywords

WSN Localization, Single Mobile Anchor, Path Planning, Adaptive Obstacle-Aware, OLSTM-DV-Hop, TD3-LSTM, Range Free.