Indoor localization has become increasingly vital for many applications from tracking assets to delivering personalized services. Yet, achieving pinpoint accuracy remains a challenge due to variations across indoor environments and devices used to assist with localization. Another emerging challenge is adversarial attacks on indoor localization systems that not only threaten service integrity but also reduce localization accuracy. To combat these challenges, we introduce CALLOC, a novel framework designed to resist adversarial attacks and variations across indoor environments and devices that reduce system accuracy and reliability. CALLOC employs a novel adaptive curriculum learning approach with a domain specific lightweight scaled-dot product attention neural network, tailored for adversarial and variation resilience in practical use cases with resource constrained mobile devices. Experimental evaluations demonstrate that CALLOC can achieve improvements of up to 6.03x in mean error and 4.6x in worst-case error against state-of-the-art indoor localization frameworks, across diverse building floorplans, mobile devices, and adversarial attacks scenarios.

翻译：室内定位在从资产追踪到个性化服务等众多应用中日益重要。然而，受室内环境差异及辅助定位设备多样性的影响，实现精确的定位仍面临挑战。另一个新兴挑战是针对室内定位系统的对抗攻击，这不仅威胁服务完整性，还会降低定位精度。为应对这些挑战，我们提出CALLOC框架——一种新型方案，旨在抵御对抗攻击及因室内环境和设备变化导致的系统精度与可靠性下降。CALLOC采用创新的自适应课程学习方法，结合领域适配的轻量级缩放点积注意力神经网络，专门针对资源受限移动设备的实际应用场景，增强了对抗攻击与环境变化的韧性。实验评估表明，在多样化的建筑平面图、移动设备及对抗攻击场景下，CALLOC相较于现有先进的室内定位框架，平均误差和最差情况误差分别可提升至6.03倍和4.6倍。