A Novel Smartphone Application for Indoor Positioning of Users based on Machine Learning

Smartphones are linked with individuals and are valuable and yet easily available sources for characterising users’ behaviour and activities. User’s location is among the characteristics of each individual that can be utilised in the provision of location-based services (LBs) in numerous scenarios such as remote health-care and interactive museums. Mobile phone tracking and positioning techniques approximate the position of a mobile phone and thereby its user, by disclosing the actual coordinate of a mobile phone. Considering the advances in positioning techniques, indoor positioning is still a challenging issue, because the coverage of satellite signals is limited in indoor environments. One of the promising solutions for indoor positioning is fingerprinting in which the signals of some known transmitters are measured in several reference points (RPs). This measured data, which is called dataset is stored and used to train a mathematical model that relates the received signal from the transmitters (model input) and the location of that user (the output of the model). Considering all the improvements in indoor positioning, there is still a gap between practical solutions and the optimal solution that provides near theoretical accuracy for positioning. This accuracy directly impacts the level of usability and reliability in corresponding LBSs. In this paper, we develop a smartphone app with the ability to be trained and detect users’ location, accurately. We use Gaussian Process Regression (GPR) as a probabilistic method to find the parameters of a non-linear and non-convex indoor positioning model. We collect a dataset of received signals’ strength (RSS) in several RPs by using a software which is prepared and installed on an Android smartphone.We also find the accurate 2σ confidence interval in the presented GPR method and evaluate the performance of the proposed method by measured data in a realistic scenario. The measurements confirm that our proposed method outperforms some conventional methods including KNN, SVR and PCA-SVR in terms of accuracy

Collaborative Private Classifiers Construction

Cyber-physical systems (CPS) are smart computer systems that control or monitor machines through computer-based algorithms, which are vulnerable to both cyber and physical threats. Similar to the growing number of applications, CPS also employ classification algorithms as a tool for data analysis and continuous monitoring of the system. While the utility of data is significantly important in building an accurate and efficient classifier, a free access to original (raw) format of data is a crucial challenge due to privacy constraints. Therefore, it is tremendously important to train classifiers in a private setting in which the privacy of individuals is protected, while data remains still practically useful for building the model. In this chapter, we investigate the application of three privacy preserving models, namely anonymization, Differential Privacy (DP), and cryptography, to privatize data and evaluate the performance of two popular classifiers, Naïve Bayes and Support Vector Machine (SVM) over the protected data. Their performances are compared in terms of accuracy, training construction costs on the same data and in the same private environment. Finally, comprehensive findings on constructing the privacy preserved classifiers are outlined. The attack models against the training data and against the private classifier models are also discussed.</p

Indoor Geo-Indistinguishability:Adopting Differential Privacy for Indoor Location Data Protection

Due to the extensive applicability of Location-Based Services (LBSs) and the Global Navigation Satellite System (GNSS) failure in indoor environments, indoor positioning systems have been widely implemented. Location fingerprinting, in particular, collects the Received Signal Strength (RSS) from users&amp;#x0027; devices, allowing Location Service Providers (LSPs) to precisely identify their locations. Therefore, LSPs and potential attackers have implicit access to this sensitive data, violating users&amp;#x0027; privacy. This issue has been addressed in outdoor environments by introducing Geo-indistinguishability (GeoInd), an alternative representation of Differential Privacy (DP). In indoor environments, however, the user lacks their coordinates, posing a new difficulty. This paper presents a novel framework for implementing GeoInd for indoor environments. The proposed framework introduces two distance calculation and RSS generation methods based solely on RSS values. Moreover, involving other participants or trusted third parties is not necessary to protect privacy, regardless of the attackers&amp;#x0027; prior knowledge. The proposed framework is evaluated in a simulated environment and two experimental settings. The results validate the proposed framework's efficiency, effectiveness, and applicability in indoor environments under the GeoInd setting.</p

WiFi Fingerprinting Signal Strength Error Modeling for Short Distances

Abstract—With increasing user demands on Location-based Services (LBS) and Social Networking Services (SNS), indoor positioning has become more crucial. Because of the general failure of GPS indoors, non-GNSS navigation technologies are essential for such areas. Wireless Local Area Networks (WLAN) have widely been employed for indoor localisation based on the Received Signal Strength (RSS)-based location fingerprinting technique. The fingerprinting technique stores the locationdependent characteristics of a signal collected at known locations ahead of the system’s use for localisation in a database. When positioning, the user’s device records its own vector(s) of signal strength and matches it against the pre-recorded database of vectors by applying pattern matching algorithms. Location is then calculated based on the best matches between the new and stored vectors. We examined the relationship between th

Design and performance of the fingerprinting technique for indoor location

Due to the general failure of Global Positioning System (GPS) for indoor positioning, non-satellite-based technologies are important forindoor localization. Using wireless networks based on the Received Signal Strength (RSS) location fingerprinting technique is the mostpopular positioning method used for indoor environments.This research proposes a new positioning technique based on fingerprinting that utilises one of the most available signals ofopportunity (SoOP), which is frequency modulated (FM) broadcast radio signals. Then the fusion of FM and Wi-Fi is investigated. Theresult outperformed the previous methods in terms of accuracy and a more robust and reliable positioning system is presented.Moreover, an analytical framework for estimating the accuracy performance of fingerprinting indoor positioning systems is suggested.Using this model, the most common signal distances such as Euclidean, Manhattan and Chebychev are fully analyses and comparedtogether both mathematically and by simulation so that we can identify which provides least positioning error. Crame-Rao lower bound(CRB) is widely used for assessing localization performance limits but the recent measurement revealed that CRB does not alwaysrepresent an actual lower bound for indoor positioning. We utilise and modify two more advanced lower bounds and propose anoptimization trend in system configuration such that the attained root mean square error in the position estimator gets closer to theminimal attainable variance in the fingerprinting position estimator. Finally, a new method for error estimation in indoor localizationsystems is designed and novel precision measurements factors for fingerprinting method is developed. Thus the quality of service ofthe positioning system is improved and the integrity of the system is guaranteed.In this research, the problem of evaluation and enhancement the accuracy of fingerprinting positioning systems utilizing terrestrial FMsignals is addressed and analytical frameworks and appropriate solid tools for designing more precise indoor positioning systems aredeveloped. In summary, the FM-based positioning analysis, analytical position error estimation tools, statistical analysis on theaccuracy of the indoor positioning systems, and the design criteria tools in this thesis are novel and provide interesting insights intothe positioning system performance. These tools are used to optimise the system performance under given performance objectives andconstraints