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Abstract
WiFi fingerprinting-based indoor localization has been widely used due to its simplicity and can be implemented on the smartphones. The major drawback of WiFi fingerprinting is that the radio map construction is very labor-intensive and time-consuming. Another drawback of WiFi fingerprinting is the Received Signal Strength (RSS) variance problem, caused by environmental changes and device diversity. RSS variance severely degrades the localization accuracy. In this paper, we propose a robust crowdsourcing-based indoor localization system (RCILS). RCILS can automatically construct the radio map using crowdsourcing data collected by smartphones. RCILS abstracts the indoor map as the semantics graph in which the edges are the possible user paths and the vertexes are the location where users may take special activities. RCILS extracts the activity sequence contained in the trajectories by activity detection and pedestrian dead-reckoning. Based on the semantics graph and activity sequence, crowdsourcing trajectories can be located and a radio map is constructed based on the localization results. For the RSS variance problem, RCILS uses the trajectory fingerprint model for indoor localization. During online localization, RCILS obtains an RSS sequence and realizes localization by matching the RSS sequence with the radio map. To evaluate RCILS, we apply RCILS in an office building. Experiment results demonstrate the efficiency and robustness of RCILS.
Highlights
Indoor localization has attracted much interest in recent years due to the diverse location-based services (LBS) that require accurate positioning [1]
We propose a robust crowdsourcing-based indoor localization system
robust crowdsourcing-based indoor localization system (RCILS) can automatically construct a WiFi radio map based on the crowdsourcing data
Summary
Indoor localization has attracted much interest in recent years due to the diverse location-based services (LBS) that require accurate positioning [1]. There are several technologies available to provide indoor positioning solutions such as WiFi [2], radio-frequency identification (RFID) [3], Bluetooth [4], Ultrawide Band (UWB) [5], inertial sensors-based localization [6,7], etc. WiFi fingerprinting has been widely used due to its simplicity leveraging on the pre-existing WiFi infrastructures This approach does not require any specialized hardware or additional infrastructure support because most smartphones are WiFi-enabled. A set of known locations are selected as the reference points (RPs) and WiFi Received Signal Strengths (RSSs) from all detected access points (APs) are collected at each RP. To improve the localization performance, this collection takes a few seconds in every point to collect a sufficient
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