CALLOC: Curriculum Adversarial Learning for Secure and Robust Indoor Localization
This addresses the need for robust and secure indoor localization for applications like asset tracking and personalized services, representing a strong specific gain rather than an incremental improvement.
The paper tackles the problem of achieving accurate and secure indoor localization despite environmental variations and adversarial attacks, introducing CALLOC which improves mean error by up to 6.03x and worst-case error by 4.6x compared to state-of-the-art methods.
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.