Toward Robust Crowdsourcing-Based Localization: A Fingerprinting Accuracy Indicator Enhanced Wireless/Magnetic/Inertial Integration Approach

The next-generation Internet of Things (IoT) systems have an increasingly demand on intelligent localization which can scale with big data without human perception. Thus, traditional localization solutions without accuracy metric will greatly limit vast applications. Crowd sourcing-based localization has been proven to be effective for mass-market location-based IoT applications. This paper proposes an enhanced crowd sourcing-based localization method by integrating inertial, wireless, and magnetic sensors. Both wireless and magnetic fingerprinting accuracy are predicted in real time through the introduction of fingerprinting accuracy indicators (FAIs) from three levels (i.e., signal, geometry, and database). The advantages and limitations of these FAI factors and their performances on predicting location errors and outliers are investigated. Furthermore, the FAI-enhanced extended Kalman filter (EKF) is proposed, which improved the dead-reckoning (DR)/WiFi, DR/Magnetic, and DR/WiFi/Magnetic integrated localization accuracy by 30.2%, 19.4%, and 29.0%, and reduced the maximum location errors by 41.2%, 28.4%, and 44.2%, respectively. These outcomes confirm the effectiveness of the FAI-enhanced EKF on improving both accuracy and reliability of multisensor integrated localization using crowd sourced data.