  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. Crowdsourcing-based localization has been
proven to be effective for mass-market location-based IoT applications. This
paper proposes an enhanced crowdsourcing-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 (FAI) 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 multi-sensor integrated
localization using crowdsourced data.
