Calibrations for Wi-Fi Indoor Positioning Systems (IPS) are expensive and time-consuming. Recalibrations are necessary periodically to compensate the drifts of radio map. Many crowdsourcing-based approaches have been proposed to calibrate the radio map strategically with user-contributed data. However, these approaches still suffer from several drawbacks. Calibration-on-demand is desirable to reduce the maintenance cost and to enhance the reliability of the Wi-Fi IPS. Wi-Fi IPS degrades only when the radio map changes significantly and lastingly. Such changes are caused by radio map anomalous events. Examples of these events include changes in indoor environment, existence of spurious data from the sensing system and unexpected failure in utility infrastructures. This paper presents a theoretical and experimental study of a crowdsourcing online Wi-Fi radio map anomalous event detection system. Detection of these events will enhance the operation intelligence of Wi-Fi IPS and enable high-level context-aware services. This system is consisted of an outlier detector for abnormal signal identification and an event discriminator for determination of event occurrence. Real world smartphone data and simulated data are used to test the performance of the system. Initial results show that the system is able to detect over 92% of simulated events and 87.5% of real world events with a low false alarm rate.