Crowdsourcing error impact on indoor positioning
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Nowadays, with the rapid development of communication technology, plenty of new applications of 5G and IoT have appeared which requires high accuracy positioning skills. Wi-Fi based fingerprinting method is one of the most promising approaches for indoor positioning. Crowdsourcing is an appropriate fingerprint data collecting method on one hand. However, it is vulnerable to different kinds of crowdsourcing errors which add errors to the fingerprint database and can decrease the accuracy of positioning on another hand. The main target of this thesis is to statistically analyze the behavior of the crowdsourcing data collected by different devices, and the effects of different kinds of intentionally or unintentionally added errors through MATLAB. From the analysis results, it can be concluded that two different kinds of manually added errors perform complete differently. Data modified with all constant RSS values, out of author’s expectation, achieves a decent accuracy similar to the original data. While data modified with only position error shows a behavior that the positioning accuracy drops with the increase of modified data proportion. Most of the distributions are closest to the Burr type XII distribution, which is particularly useful for modeling histograms.