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The next-generation internet of things (IoT) systems have an increasinglydemand on intelligent localization which can scale with big data without humanperception. Thus, traditional localization solutions without accuracy metricwill greatly limit vast applications. Crowdsourcing-based localization has beenproven to be effective for mass-market location-based IoT applications. Thispaper proposes an enhanced crowdsourcing-based localization method byintegrating inertial, wireless, and magnetic sensors. Both wireless andmagnetic fingerprinting accuracy are predicted in real time through theintroduction of fingerprinting accuracy indicators (FAI) from three levels(i.e., signal, geometry, and database). The advantages and limitations of theseFAI factors and their performances on predicting location errors and outliersare investigated. Furthermore, the FAI-enhanced extended Kalman filter (EKF) isproposed, which improved the dead-reckoning (DR)/WiFi, DR/Magnetic, andDR/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 EKFon improving both accuracy and reliability of multi-sensor integratedlocalization using crowdsourced data.

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