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.

翻译：在无线传感器网络中，精确且能量高效的定位仍是一个关键挑战，尤其是在障碍物影响信号传播的情况下。本研究提出了AOASS（自适应障碍感知方形螺旋），这是一种新型单移动锚节点框架，它将优化的方形螺旋移动模式与自适应障碍检测相结合。移动锚节点能够感知并绕过障碍物，同时保持高定位精度和完整的网络覆盖，确保每个节点至少接收到三个非共线信标信号以实现可靠的位置估计。定位精度通过OLSTM DV Hop模型得到进一步提升，该模型将长短期记忆网络与传统DV Hop算法相结合，以更准确地估计跳距并减少多跳误差。锚节点轨迹由TD3 LSTM强化学习智能体管理，并辅以基于卡尔曼滤波的预测层和模糊逻辑ORCA安全模块，以实现平滑且无碰撞的导航。在不同障碍物密度下的仿真实验表明，与现有方法相比，AOASS始终能够实现更高的定位精度、更好的能量效率和更优化的轨迹。这些结果证明了该框架的可扩展性及其在实际无线传感器网络应用中的潜力，为数据驱动的物联网系统提供了一种智能且适应性强的解决方案。