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|Title:||Fingerprints for Indoor Localization|
|Department:||Computing and Software|
|Keywords:||indoor localization;fingerprint;mobile crowdsensing;active learning|
|Abstract:||Location-based services have experienced substantial growth in the last decade. However, despite extensive research efforts, sub-meter location accuracy with low-cost infrastructure continues to be elusive. Comparing with infrastructure-based solutions, infrastructure-free indoor localization has the major advantage of avoiding extra cost for infrastructure deployment. There are two typical types of infrastructure-free indoor localization solutions, i.e., Pedestrian Dead Reckoning (PDR)-based and fingerprint-based. PDR-based solutions rely on inertial measurement units to estimate the user's relative location. Despite the effort, many issues still remain in PDR systems. For example, any deployed smartphone-based PDR system needs to cope with the changing orientation of smartphone that the phone might be putting in a pocket, or being taken out to use, etc. In addition, the outputs of Micro Electro-Mechanical Systems (MEMS) sensors on smart devices vary over time which results in rapidly accumulated localization errors without external references. Fingerprint-based solutions utilize different types of location dependent parameters to estimate user's absolute location. Although fingerprint-based solutions are usually more practical than PDR-based, they suffer from laborious site survey process. In this dissertation, we aim to mitigate these challenges. First of all, illumination intensity is introduced as a new type of fingerprints to provide location references for PDR-based indoor localization. We propose IDyLL -- an indoor localization system using inertial measurement units (IMU) and photodiode sensors on smartphones. Using a novel illumination peak detection algorithm, IDyLL augments IMU-based pedestrian dead reckoning with location fixes. Moreover, we devise a burned-out detection algorithm for simultaneous luminary-assisted IPS and burned-out luminary detection. Experimental study using data collected from smartphones shows that IDyLL is able to achieve high localization accuracy at low costs. As for fingerprint collection, several frameworks are proposed to ease the laborious site survey process, without compromising fingerprint quality. We propose TuRF, a path-based fingerprint collection mechanism for site survey. MobiBee, a treasure hunt game, is further designed to take advantage of gamification and incentive models for fast fingerprint collection. Motivated by applying mobile crowdsensing for fingerprint collection, we propose ALSense, a distributed active learning framework, for budgeted mobile crowdsensing applications. Novel stream-based active learning strategies are developed to orchestrate queries of annotation data and the upload of unlabeled data from mobile devices. Extensive experiments demonstrate that ALSense can indeed achieve higher classification accuracy given fixed data acquisition budgets. Facing malicious behaviors, three types of location-related attacks and their corresponding detection algorithms are investigated. Experiments on both crowdsensed and emulated dataset show that the proposed algorithms can detect all three types of attacks with high accuracy.|
|Appears in Collections:||Open Access Dissertations and Theses|
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