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Due to the increasing requirements of the seamless and round-the-clock Location-based services (LBSs), a growing interest in Wi-Fi network aided location tracking is witnessed in the past decade. One of the significant problems of the conventional Wi-Fi location tracking approaches based on received signal strength (RSS) fingerprinting is the time-consuming and labor intensive work involved in location fingerprint calibration. To solve this problem, a novel unlabeled Wi-Fi simultaneous localization and mapping (SLAM) approach is developed to avoid the location fingerprinting and additional inertial or vision sensors. In this approach, an unlabeled mobility map of the coverage area is first constructed by using the crowd-sourcing from a batch of sporadically recorded Wi-Fi RSS sequences based on the spectral cluster assembling. Then, the sequence alignment algorithm is applied to conduct location tracking and mobility map updating. Finally, the effectiveness of this approach is verified by the extensive experiments carried out in a campus-wide area. [ABSTRACT FROM AUTHOR]

Highlights: [•] A location tracking system SCaNME without any cumbersome work in fingerprint recording is proposed. [•] The mobility map is constructed from Wi-Fi data which have no explicit information of coordinates. [•] The performance of SCaNME has been carefully examined based on the actual Wi-Fi data in HKUST campus. [ABSTRACT FROM AUTHOR]

Highlights: [•] The entropy is employed as a new metric to evaluate the fingerprint-based Wi-Fi location accuracy. [•] The relations of entropy and accuracy with variable RP densities and standard deviations are given. [•] The reckless increase of the number of APs cannot be an effective way to improve location accuracy. [•] The substantial saving of computation cost by the entropy is achieved over the existing metrics. [ABSTRACT FROM AUTHOR]