提出新方法提升不确定地形中定向传感器的覆盖鲁棒性。
A Radius of Robust Feasibility Approach to Directional Sensors in Uncertain Terrain
- 基于鲁棒可行性半径，设计分布式贪心算法优化传感器方向。
- 在动态环境中验证，显著提升覆盖效率与抗不确定性能力。
- 适合部署在环境变化大、定位不精确的智能感知场景。
传感器具备探测周围环境的能力，但其精确定位的不确定性会严重影响感知性能。本文引入鲁棒可行性半径概念，定义了在不确定地形中确保稳健可行性的最大范围。针对定向传感器网络，提出一种新颖方法，结合该半径理论，利用分布式贪心算法提升覆盖效果。文中推导出定向传感器网络中鲁棒可行性半径的精确公式，并在高覆盖潜力区域智能调整传感器朝向，兼顾鲁棒性。通过分析算法在动态环境中的适应性，证明其能有效提升效率与稳定性。实验结果表明，该方法可最大化覆盖范围并优化传感器方向，展现出在真实场景中的实用优势。
原文摘要 · Abstract (English)
A sensor has the ability to probe its surroundings. However, uncertainties in its exact location can significantly compromise its sensing performance. The radius of robust feasibility defines the maximum range within which robust feasibility is ensured. This work introduces a novel approach integrating it with the directional sensor networks to enhance coverage using a distributed greedy algorithm. In particular, we provide an exact formula for the radius of robust feasibility of sensors in a directional sensor network. The proposed model strategically orients the sensors in regions with high coverage potential, accounting for robustness in the face of uncertainty. We analyze the algorithm's adaptability in dynamic environments, demonstrating its ability to enhance efficiency and robustness. Experimental results validate its efficacy in maximizing coverage and optimizing sensor orientations, highlighting its practical advantages for real-world scenarios.
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