Investigations of Dempster-Shafer theory in the context of WLAN-based indoor localization

Accurate user's locations and real-time location estimations in indoor environments, are important parameters to achieve reliable Location Based Services (LBSs). Non-Bayesian frameworks are gaining more and more interest in order to improve the location accuracy indoors when WLAN positioning is used. The main objective of this thesis is to study the feasibility of Dempster Shafer non-Bayesian combining in the context of received signal strength (RSS)-based indoor WLAN localization. The motivation of our work has been to look for new approaches in order to try to deal better with the incomplete or erroneous data measurements used in the training phase of any WLAN positioning algorithm. State-of-art studies show that the accuracy of mobile position estimation by WLAN localization algorithms with the Bayesian framework is not satisfactory. Thus, it makes sense to try to investigate non-Bayesian approaches and to see their usefulness in the context of WLAN localization. First, a comprehensive analysis of various DST combining rules with RSS-based positioning methods has been performed. Then, the idea has been implemented via MATLAB simulator and the outputs were compared to the Bayesian approaches. The comparison is in terms of root mean square errors, correct floor detection probabilities and error radius and we used real-field data measurements as test data. Typically, the current published research work based on non-Bayesian frameworks in the context of wireless localization is limited to fingerprinting methods. Both the fingerprinting and the path-loss model using the DST frameworks are carried out in this thesis. The thesis results contain two parts. The first one examines the fingerprinting with various DST combination while the other one deals with the path-loss and DST combination. The positioning accuracy estimated by Bayesian framework is compared to the DST and a high correlation between these two has been observed. As expected, the Bayesian framework results are slightly less accurate (on average) than the DST, because the DST fuse RSS from multiple access points with different beliefs or underlying uncertainty and allows the uncertainty to be a model parameter

Seamless Outdoors-Indoors Localization Solutions on Smartphones: Implementation and Challenges

© ACM, 2016. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in http://doi.org/10.1145/2871166[EN] The demand for more sophisticated Location-Based Services (LBS) in terms of applications variety and accuracy is tripling every year since the emergence of the smartphone a few years ago. Equally, smartphone manufacturers are mounting several wireless communication and localization technologies, inertial sensors as well as powerful processing capability, to cater to such LBS applications. A hybrid of wireless technologies is needed to provide seamless localization solutions and to improve accuracy, to reduce time to fix, and to reduce power consumption. The review of localization techniques/technologies of this emerging field is therefore important. This article reviews the recent research-oriented and commercial localization solutions on smartphones. The focus of this article is on the implementation challenges associated with utilizing these positioning solutions on Android-based smartphones. Furthermore, the taxonomy of smartphone-location techniques is highlighted with a special focus on the detail of each technique and its hybridization. The article compares the indoor localization techniques based on accuracy, utilized wireless technology, overhead, and localization technique used. The pursuit of achieving ubiquitous localization outdoors and indoors for critical LBS applications such as security and safety shall dominate future research efforts.This research was sponsored by Koya University, Kurdistan Region-Iraq. The authors also would like to thank Dr. Ali Al-Sherbaz (from the University of Northampton-UK) and Dr. Naseer Al-Jawad (from the University of Buckingham-UK) for providing and improving the quality of this article in terms of academic and technical writing.Maghdid, HS.; Lami, IA.; Ghafoor, KZ.; Lloret, J. (2016). 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Location-Enabled IoT (LE-IoT): A Survey of Positioning Techniques, Error Sources, and Mitigation

The Internet of Things (IoT) has started to empower the future of many industrial and mass-market applications. Localization techniques are becoming key to add location context to IoT data without human perception and intervention. Meanwhile, the newly-emerged Low-Power Wide-Area Network (LPWAN) technologies have advantages such as long-range, low power consumption, low cost, massive connections, and the capability for communication in both indoor and outdoor areas. These features make LPWAN signals strong candidates for mass-market localization applications. However, there are various error sources that have limited localization performance by using such IoT signals. This paper reviews the IoT localization system through the following sequence: IoT localization system review -- localization data sources -- localization algorithms -- localization error sources and mitigation -- localization performance evaluation. Compared to the related surveys, this paper has a more comprehensive and state-of-the-art review on IoT localization methods, an original review on IoT localization error sources and mitigation, an original review on IoT localization performance evaluation, and a more comprehensive review of IoT localization applications, opportunities, and challenges. Thus, this survey provides comprehensive guidance for peers who are interested in enabling localization ability in the existing IoT systems, using IoT systems for localization, or integrating IoT signals with the existing localization sensors

User perception-based quantitative studies of Location Based Services of today and tomorrow

Modern Location Based Services (LBS) are not any more limited to navigation or routing services, but they have flourished in every sphere of life whether it is regular activity tracker or family finder. The continuous advancement of location technologies, such as GNSS and cellular in outdoors and WLAN in indoors, opens new challenges for the LBS providers. Due to the emergence of location-enabled smartphone technologies, the use of location based services and applications has increased remarkably in the last few years. This forces the LBS providers to think beyond the boundaries. Therefore, the analysis of the user needs, behavior, perception and preference becomes one of the key factors and eventually prerequisites for success in this sector. The thesis comprises a survey focusing on LBS from different perspectives, such as localization knowledge, privacy concerns and LBS usage, and an analysis based on the responses from 118 volunteer participants. The analysis begins with the classification of the users with respect to their “technical knowledge in localization”, “privacy concerns” and “LBS usage” based on the survey results, and it continues with the analysis of the correlation and similarity between the user classes. The user classes are compared based on the Mann-Whitney-Wilcoxon, Fligner-Policello and unpaired t-test in terms of preferences similarity. The user perceptions with respect to the cost and feature preferences are also analyzed per user class. The aim of the thesis is to illustrate how the LBS preferences differ among various user classes and how the user classes may correlate. The main findings of the analysis are that the user’s background class has a significant impact on the preferences. Moreover, the high-level knowledge users have similar preferences as the high-level usage users, even though the correlation among the user classes is very low. Another interesting finding of this analysis is that the high-level knowledge users are relatively less willing to pay for LBS applications in comparison to the other user classes. From the privacy-concern based classification, it is observed that most of the users have certain privacy concerns, which represents one of the barriers in the LBS development. Finally, it can be inferred that the statistical analysis and the comparative results justify the empirical user classification derived in this thesis

User perception-based quantitative studies of Location Based Services of today and tomorrow

Modern Location Based Services (LBS) are not any more limited to navigation or routing services, but they have flourished in every sphere of life whether it is regular activity tracker or family finder. The continuous advancement of location technologies, such as GNSS and cellular in outdoors and WLAN in indoors, opens new challenges for the LBS providers. Due to the emergence of location-enabled smartphone technologies, the use of location based services and applications has increased remarkably in the last few years. This forces the LBS providers to think beyond the boundaries. Therefore, the analysis of the user needs, behavior, perception and preference becomes one of the key factors and eventually prerequisites for success in this sector. The thesis comprises a survey focusing on LBS from different perspectives, such as localization knowledge, privacy concerns and LBS usage, and an analysis based on the responses from 118 volunteer participants. The analysis begins with the classification of the users with respect to their “technical knowledge in localization”, “privacy concerns” and “LBS usage” based on the survey results, and it continues with the analysis of the correlation and similarity between the user classes. The user classes are compared based on the Mann-Whitney-Wilcoxon, Fligner-Policello and unpaired t-test in terms of preferences similarity. The user perceptions with respect to the cost and feature preferences are also analyzed per user class. The aim of the thesis is to illustrate how the LBS preferences differ among various user classes and how the user classes may correlate. The main findings of the analysis are that the user’s background class has a significant impact on the preferences. Moreover, the high-level knowledge users have similar preferences as the high-level usage users, even though the correlation among the user classes is very low. Another interesting finding of this analysis is that the high-level knowledge users are relatively less willing to pay for LBS applications in comparison to the other user classes. From the privacy-concern based classification, it is observed that most of the users have certain privacy concerns, which represents one of the barriers in the LBS development. Finally, it can be inferred that the statistical analysis and the comparative results justify the empirical user classification derived in this thesis