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

TL;DR

AOASS introduces an adaptive, obstacle-aware mobile anchor framework for WSN localization that enhances accuracy and energy efficiency through reinforcement learning, obstacle detection, and advanced hop estimation.

Contribution

This paper presents a novel single mobile anchor framework combining obstacle-aware navigation, LSTM-based hop estimation, and reinforcement learning for improved WSN localization.

Findings

Higher localization accuracy across various obstacle densities

Improved energy efficiency compared to existing methods

Optimized anchor trajectories with collision avoidance

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 noncollinear 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 scalability and potential for real world WSN applications, offering an intelligent and adaptable solution for data driven IoT systems.