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.

翻译：传感器具备探测其周围环境的能力。然而，其精确位置的不确定性会显著影响其感知性能。鲁棒可行半径定义了确保鲁棒可行性的最大范围。本研究提出了一种新方法，将其与定向传感器网络相结合，利用分布式贪婪算法来增强覆盖范围。具体而言，我们给出了定向传感器网络中传感器鲁棒可行半径的精确计算公式。所提模型策略性地将传感器定向于具有高覆盖潜力的区域，同时考虑了面对不确定性时的鲁棒性。我们分析了该算法在动态环境中的适应性，证明了其提升效率与鲁棒性的能力。实验结果验证了其在最大化覆盖范围和优化传感器定向方面的有效性，凸显了其在现实场景中的实用优势。