A Localization Database Establishment Method Based on Crowdsourcing Inertial Sensor Data and Quality Assessment Criteria

Abstract

Aimed at the challenge of generating indoor localization databases with daily life crowdsourcing-based inertial sensor data, this paper proposes an anchor point-based forward–backward smoothing method to obtain reliable localization solutions. More importantly, a quantitative framework is proposed to evaluate the quality of smartphone-based inertial sensor data automatically without user intervention. Through this framework, the reliability of each inertial sensor data can be evaluated and sorted. Tests with multiple people and multiple smartphones in a public office building and a shopping mall illustrate that the proposed method can provide a WiFi fingerprinting database that has similar accuracy to that generated by a supervised map-aided database-generation method. Therefore, the proposed method and framework can guide the promotion of crowdsourcing-based Internet of Things applications in the context of big data.