AOASS：自适应障碍感知正方形螺旋框架用于单移动锚点基于WSN定位
AOASS: Adaptive Obstacle-Aware Square Spiral Framework for Single-mobile Anchor-Based WSN Localization
AI总结：
AOASS通过自适应障碍检测和优化运动模式，提升WSN中单移动锚点的定位精度与能耗效率，适用于现实物联网应用。
摘要：
在无线传感器网络（WSN）中，准确且节能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。本研究引入了AOASS（自适应障碍感知正方形螺旋），一种新的单移动锚点框架，结合了优化的正方形螺旋运动模式与自适应障碍检测。移动锚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三个非共线的信标信号以进行可靠的定位估计。通过OLSTM DV Hop模型进一步提高定位精度，该模型将长短期记忆（LSTM）网络与传统DV Hop算法结合，以更好地估计跳数距离并减少多跳误差。锚点轨迹由TD3 LSTM强化学习代理管理，支持基于卡尔曼预测层和模糊逻辑ORCA安全模块的平滑无碰撞导航。在不同障碍密度下的仿真实验表明，AOASS在定位精度、能耗效率和轨迹优化方面均优于现有方法。这些结果展示了该框架的可扩展性和在现实世界WSN应用中的潜力，为数据驱动的物联网系统提供了智能且适应性强的解决方案。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
