Abstract
Sensor-rich smartphone enables a novel approach to training the fingerprint database for mobile indoor localization via crowd sensing. In this survey, we discuss the crowd sensing based mobile indoor localization in terms of foundational knowledge, signals of fingerprints, trajectory of obtaining fingerprints, indoor maps, evolution of a fingerprint database, positioning algorithms, state-of-the-art solutions, and challenges. The survey concludes that the crowd sensing is a low cost solution of generating and updating an organic fingerprint database. Although the crowd sensing concept is widely accepted by the academic community in these years, there are a lot of unsolved problems which hinder the concept of transferring into a practical system. We address the challenges and predict future trends in the end.
Highlights
People usually spend over 90% of their daily lives indoors where the mobile device, for example, smartphone, is like a shadow inseparably sticking to users, which greatly increases the interests of mobile indoor localization for academia and industry alike
The contents are structured as follows: Section 2 introduces the foundation of crowd sensing indoor localization in high level; Section 3 examines the possible signals for fingerprints of indoor localization; Section 4 surveys the methods for obtaining the walking trajectory of a participant; Section 5 discusses the types of map used for indoor localization; Section 6 looks at how the fingerprints organically change; the widely applied positioning algorithms are discussed in Section 7; Section 8 compares the state-ofthe-art solutions published recent years; Section 9 points out the challenges of crowd sensing based indoor localization; and Section 10 concludes by identifying open research topics and future research directions
A vast literature has addressed how to integrate multisensor estimates into one single output, like covariance intersection [44], covariance union [45], and so forth. The limitation of such problems is that they typically fuse the estimates without modeling the trustworthiness of the users, or they only identify the unreliable estimates by some simple outlier detection methods, like kNN [46], spatial weighted outlier detection (SOD) [47], local outlier factor (LOF) [48], and so forth
Summary
Sensor-rich smartphone enables a novel approach to training the fingerprint database for mobile indoor localization via crowd sensing. In this survey, we discuss the crowd sensing based mobile indoor localization in terms of foundational knowledge, signals of fingerprints, trajectory of obtaining fingerprints, indoor maps, evolution of a fingerprint database, positioning algorithms, stateof-the-art solutions, and challenges. The survey concludes that the crowd sensing is a low cost solution of generating and updating an organic fingerprint database. The crowd sensing concept is widely accepted by the academic community in these years, there are a lot of unsolved problems which hinder the concept of transferring into a practical system. We address the challenges and predict future trends in the end
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