Data Cleaning for Indoor Crowdsourced RSSI Sequences

Received Signal Strength Indication (RSSI) has been increasingly deployed in indoor localization and navigation. Comparing with traditional fingerprint-based methods, crowdsourced method can collect RSSIs without expert surveyors and designated fingerprint collection points low-costly and efficiently. However, the crowdsourced RSSIs may contain some false and incomplete data. In this paper, we focus on two quality types of indoor crowdsourced RSSI sequences: missing values and false values. For the received signal strength values, we propose a RSSI sequences alignment and matching method to complete the missing values. For the location labels, we construct an indoor logical graph to capture the indoor topology and spatial consistent. To repair the missing and false location labels, we design a AP distribution based mapping method to map crowdsourced RSSIs to floor plan.