Molé: a scalable, user-generated WiFi positioning engine

Abstract

Abstract-We describe the design, implementation, and evaluation of Molé, a mobile organic localization engine. Unlike previous work on crowd-sourced WiFi positioning, Molé uses a hierarchical name space. By not relying on a map and by being more strict than uninterpreted names for places, Molé aims for a more flexible and scalable point in the design space of localization systems. Molé employs several new techniques, including a new statistical positioning algorithm to differentiate between neighboring places, a motion detector to reduce update lag, and a scalable "cloud"-based fingerprint distribution system. Molé's localization algorithm, called Maximum Overlap (MAO), accounts for temporal variations in a place's fingerprint in a principled manner. It also allows for aggregation of fingerprints from many users and is compact enough for on-device storage. We show through end-to-end experiments in two deployments that MAO is significantly more accurate than state-of-the-art Bayesian-based localizers. We also show that non-experts can use Molé to quickly survey a building, enabling room-grained location-based services for themselves and others.

A Survey of Enabling Technologies for Network Localization, Tracking, and Navigation

“…Moreover, RSS readings can be affected by RF interference from other electronic devices, e.g., microwave ovens or cordless phones operating on the same frequency, which calls for robust fingerprint matching algorithms, as discussed in [67]. Given the effort associated with creating and maintaining radiomaps, solutions based on crowdsourcing (discussed later in this section), SLAM (discussed in Section X), as well as parametric and non-parametric models have been proposed [71], [106]. In particular, radiomaps based on models can be obtained from a much sparser set of RSS fingerprints and have the potential of reducing the computation effort in estimating the position.…”

“…The Molé system allows for aggregation of fingerprints from many users and is compact enough for on-device storage, while it employs a scalable cloud-based fingerprint distribution system [106]. FreeLoc addresses radiomap construction across heterogeneous devices by employing relative, rather than absolute RSS values, and uses techniques for maintaining a single fingerprint for each location in the radiomap, irrespective of any number of uploaded data sets for a given location, thus keeping the radiomap to a reasonable size [107].…”

WLAN Fingerprint Indoor Positioning Strategy Based on Implicit Crowdsourcing and Semi-Supervised Learning

“…Luo et al use users' active feedback to improve the performance of crowdsourcing positioning system, similar to the work done by Hossain et al [9,10]. Mole is a mobile organic localization engine that requires participants to actively bind locations and manages locations using a hierarchical structure [11]. The OIL system uses a Voronoi diagram-based method to guide participants to move toward the uncovered area to make the crowdsourcing sampling process faster.…”

Store layout optimization using indoor positioning system

“…Specifically, a number of studies combined Wi-Fi RSS fingerprinting and floor plans established through site surveys to build IPSs. Recent studies such as LiFS, 32 Zee, 33 Mole´, 34 and GROPING 35 introduced IPSs that overcome the shortcomings of time-consuming and labor-intensive site surveys through crowdsourcing using sensors embedded in smartphones such as accelerometers, gyroscopes, and compasses. In addition, some studies have analyzed customer movement paths or performed customer behavior analysis based on collected sensor data.…”