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In the last decade, we observed a constantly growing number of Location-Based Services (LBSs) used in indoor environments, such as for targeted advertising in shopping malls or finding nearby friends. Although privacy-preserving LBSs were addressed in the literature, there was a lack of attention to the problem of enhancing privacy of indoor localization, i.e., the process of obtaining the users' locations indoors and, thus, a prerequisite for any indoor LBS. In this work we present PILOT, the first practically efficient solution for Privacy-Preserving Indoor Localization (PPIL) that was obtained by a synergy of the research areas indoor localization and applied cryptography. We design, implement, and evaluate protocols for Wi-Fi fingerprint-based PPIL that rely on 4 different distance metrics. To save energy and network bandwidth for the mobile end devices in PPIL, we securely outsource the computations to two non-colluding semi-honest parties. Our solution mixes different secure two-party computation protocols and we design size-and depth-optimized circuits for PPIL. We construct efficient circuit building blocks that are of independent interest: Single Instruction Multiple Data (SIMD) capable oblivious access to an array with low circuit depth and selection of the k-Nearest Neighbors with small circuit size. Additionally, we reduce Received Signal Strength (RSS) values from 8 bits to 4 bits without any significant accuracy reduction. Our most efficient PPIL protocol is 553x faster than that of Li et al. (INFOCOM'14) and 500× faster than that of Ziegeldorf et al. (WiSec'14). Our implementation on commodity hardware has practical run-times of less than 1 second even for the most accurate distance metrics that we consider, and it can process more than half a million PPIL queries per day.

Clock offset predictions along with satellite orbit predictions are used in self-assisted GNSS to reduce the Time-to-First-Fix of a satellite positioning device. This paper compares three methods for predicting GNSS satellite clock offsets: polynomial regression, Kalman filtering and support vector machines (SVM). The regression polynomial and support vector machine model are trained from past offsets. The Kalman filter uses past offsets to estimate the clock offset coefficients. In tests with GPS and GLONASS data, it is found that all three methods significantly improve the clock predictions relative to extrapolation with the basic clock model of the last obtained broadcast ephemeris (BE). In particular, the 68% quantile of 7 day clock offset errors of GPS satellites was reduced by 66% with polynomial regression, 69% with Kalman filtering and 56% with SVM on average. acceptedVersion

Internet of Things (IoT) connects sensing devices to the Internet for the purpose of exchanging information. Location information is one of the most crucial pieces of information required to achieve intelligent and context-aware IoT systems. Recently, positioning and localization functions have been realized in a large amount of IoT systems. However, security and privacy threats related to positioning in IoT have not been sufficiently addressed so far. In this paper, we survey solutions for improving the robustness, security, and privacy of location-based services in IoT systems. First, we provide an in-depth evaluation of the threats and solutions related to both global navigation satellite system (GNSS) and non-GNSS-based solutions. Second, we describe certain cryptographic solutions for security and privacy of positioning and location-based services in IoT. Finally, we discuss the state-of-the-art of policy regulations regarding security of positioning solutions and legal instruments to location data privacy in detail. This survey paper addresses a broad range of security and privacy aspects in IoT-based positioning and localization from both technical and legal points of view and aims to give insight and recommendations for future IoT systems providing more robust, secure, and privacy-preserving location-based services. publishedVersion

Due to the universal presence of motion, vibration, and shock, inertial motion sensors can be applied in various contexts. Development of the microelectromechanical (MEMS) technology opens up many new consumer and industrial applications for accelerometers and gyroscopes. The multiformity of applications creates different requirements to inertial sensors in terms of accuracy, size, power consumption and cost. This makes it challenging to choose sensors that are suited best for the particular application. In addition, development of signal processing algorithms for inertial sensor data require understanding on the physical principles of both motion generated and sensor operation principles. This chapter aims to aid the system designer to understand and manage these challenges. The principles of operation of accelerometers and gyroscopes are explained with examples of different applications using inertial sensors data as input. Especially, detailed examples of signal processing algorithms for pedestrian navigation and motion classification are given. acceptedVersion

This paper presents a method for improving the accuracy of extended GNSS satellite orbit predictions with convolutional neural networks (CNN). Satellite orbit predictions are used in self-assisted GNSS to reduce the Time to First Fix of a satellite positioning device. We describe the models we use to predict the satellite orbit and present the improvement method that uses CNN. The CNN estimates future prediction errors of our model and these estimates are used to correct our orbit predictions. We also describe how the neural network can be implemented into our prediction algorithm. In tests with GPS and BeiDou data, the method significantly improves orbit prediction accuracy. For example, the 68% error quantile of 7 day orbit prediction errors of GPS satellites was reduced by 45% on average. acceptedVersion

The increasingly connected world magnifies the threats to users' location privacy. Encryption protocols offer solutions to privacy concerns, but they are computationally very demanding. A reduction of the bit-length of the Received Signal Strength (RSS) measurements is required for a realistic, privacy-preserving positioning system based on fingerprinting. This paper studies the practical design of quantizers for RSS fingerprinting data and analyses the effect of the quantization on the positioning performance, with several real data sets and positioning algorithms. Our results show that a 4 bit quantization of RSS yields the same positioning accuracy as with unquantized RSS and that 1 bit approaches (i.e. proximity based positioning) are also feasible for certain applications. acceptedVersion

Accurate position information is nowadays very important in many applications. For instance, maintaining the situation awareness in command center in emergency operations is very crucial. Due to signal strength attenuation and multipath, Global Navigation Satellite Systems are not suitable for indoor navigation purposes. Radio network-based positioning techniques, such as wireless local area network, require local infrastructure that is often vulnerable in emergency situations. We propose here a distributed system for personal positioning based on inertial sensors. The system consists of an inertial measurement unit (IMU) connected to a radio carried by a person and the server connected to another radio. Step length and heading estimation is computed in the IMU and sent to the server. On the server side, the position is estimated using particle filter-based map matching. The benefit of the distributed architecture is that the computational capacity can be kept very low on the user side, which leads to long operation time as power consumption also remains very low.

This paper presents results on the accuracy of the extended (aka autonomous) orbit and clock predictions for the first QZSS satellite in orbit. The purpose of extended prediction is the reduction of the time-to-first-fix of a stand-alone satellite navigation receiver and the improvement of the availability of positioning during weak signal conditions. We describe the models we used to predict satellite orbit and clock and present the prediction accuracies we obtained with our models. With a constant-parameter solar radiation pressure (SRP) model, we obtained orbit prediction accuracies of 8, 25, and 80m for prediction lengths of 3, 7, and 14 days, respectively. With an SRP model with seasonally varying parameters the accuracies for prediction lengths of 3, 7, and 14 days are 7, 14, and 34 m, respectively. When predicting the QZSS clock for 7 days, the 68% and 95% quantiles of the accumulated prediction errors were 9 and 24m when using parameters with age of 7 days, 13 and 33m with age of 14 days, and 22 and 62m with age of 28 days. acceptedVersion

Nowadays mobile applications demand higher context awareness. The applications aim to understand the user's context (e.g., home or at work) and provide services tailored to the users. The algorithms responsible for inferring the user's context are the so-called context inference algorithms, the place detection being a particular case. Our hypothesis is that people use mobile phones differently when they are located in different places (e.g. longer calls at home than at work). Therefore, the usage of the mobile phones could be an indicator of the users' current context. The objective of the work is to develop a system that can estimate the user's place label (home, work, etc.), based on phone usage. As training and validation set, we use a database containing phone usage information of 200 users over several months including phone call and SMS logs, multimedia usage, accelerometer, GPS, network information and system information. The data was split into visits, i.e., periods of uninterrupted time that the user has been in a certain place (Home, Work, Leisure, etc.). The data include information about the phone usage during the visits, and the semantic label of the place visited (Home, Work, etc.). We consider two approaches to represent this data: the first approach (so-called visits approach) saves each visit separately; the second approach (so-called places approach) combines all visits of one user to a certain place and creates place-specific information. For place detection, we used five popular classification methods, Naïve Bayes, Decision Tree, Bagged Tree, Neural Network and K-Nearest Neighbors, in both representation approaches. We evaluated their classification rates and found that: 1) Bagged Tree outperforms the other methods; 2) the places data-representation gives better results than the visits data-representation. acceptedVersion

Currently, people are used to getting accurate GNSS based positioning services. However, in indoor environments, the GNSS cannot provide the accuracy and availability comparable to open outdoor environments. Therefore, alternatives to GNSS are needed for indoor positioning. In this thesis, methods for pedestrian indoor positioning are proposed. With these novel methods, the mobile unit performs all the required positioning measurements and no dedicated positioning infrastructure is required.This thesis proposes novel radio map configuration methods for WLAN fingerprinting based on received signal strength measurements. These methods with different model parameters were studied in field tests to identify the best models with reasonable positioning accuracy and moderate memory requirements. A histogram based WLAN fingerprinting model is proposed to aid IMU based pedestrian dead reckoning that is obtained using a gyro and a 3-axis accelerometer, both based on MEMS technology. The sensor data is used to detect the steps taken by a person on foot and to estimate the step length and the heading change during each step.For the aiding of the PDR with WLAN positioning, this thesis proposes two different configurations of complementary extended Kalman filters. The field tests show that these configurations produce equivalent position estimates. Two particle filters are proposed to implement the map aided PDR: one filter uses only the PDR and map information, while the other uses also the WLAN positioning. Based on the field tests, map aiding improves the positioning accuracy more than WLAN positioning.Novel map checking algorithms based on the sequential re-selection of obstacle lines are proposed to decrease the computation time required by the indoor map matching. To present the map information, both unstructured and structured obstacle maps are used. The feasibility of the proposed particle filter algorithms to real time navigation were demonstrated in field tests.

Although the short range radio frequency technologies such as WLAN (Wireless Local Area Network) and Bluetooth were originally designed for the purpose of wireless communication, they have been widely adopted as common signals of opportunity for positioning in smart phones for both indoors and outdoors. The cell identifier and radio signal strength are the most common observables used for positioning. The applicable position methods include Cell-ID, fingerprinting, and trilateration. Fingerprinting is the most common approach, which can provide a positioning accuracy of even 2-5 meters indoors using either the pattern recognition algorithm or the probabilistic algorithm; however, the obtainable accuracy depends on the positioning environment. The objective of this chapter is to present the WLAN and Bluetooth positioning methodologies and explain the related positioning algorithms. The chapter covers an introduction of the topic, descriptions of the observables, the positioning algorithms, and the implementation issues of the positioning solutions. The chapter is concluded with a short section of future research directions followed by a brief conclusion.

In this paper we present a practical approach to relative positioning of heterogeneous mass market devices. We propose a user-centric solution exploiting measurements from on-the-fly fixed reference points detected during scanning procedures. The technique implemented on mobile devices is able to locate neighboring nodes, demonstrating to be a feasible and practical solution for sensing spatial social contexts in ad-hoc environments.

In this paper we propose a Collaborative Mapping (CM) method based on the exploitation of the WLAN Received Signal Strength (RSS) measured from short-range ad-hoc links between neighboring devices. The estimated spatial proximity allows on-the-fly calibrations of heterogeneous clients for Location Fingerprinting (LF) applications. This method can avoid long time-consuming and battery-draining calibrations when implementing Fingerprinting Location applications running on heterogeneous mass market devices.

In this paper, questions related to design of WLAN radio map for fingerprinting based positioning were investigated. The experiment results show that with histogram based algorithms, the positioning accuracy improves as the number of histogram bins increases until the number of bins reaches eight. With the number of bins lower than this, the uneven bin distribution with separate bin for missing samples gives better accuracy than even bin widths. If the calibration data contains samples from several measurement directions, it is beneficial to combine them into one fingerprint, as this decreases the size of the radio map and gives at least the same accuracy as having separate fingerprints for different measurement directions. In the experiments, the combination of the signals from correlating sources before the computation of the radio map and position estimate decreases the positioning accuracy only by 1–2 m, but decreases significantly the size of the radio map. The best method for signal combinations depends on the bin configuration and position estimation algorithm.

Abtract - The objective of this research is to improve reliability and positioning accuracy of a mobile, standalone GNSS receiver in personal positioning. We propose a novel algorithm that fuses carrier information with code phase measurements and uses the additional security feature of receiver autonomous integrity monitoring (RAIM) and fault detection and exclusion (FDE) in order to detect and exclude erroneous measurements. The weighted least squares (WLS) method completed with RAIM/FDE is used to compute the GPS position and velocity estimates from pseudorange and delta range measurements. These estimates are combined in a complementary Kalman filter (CKF), which gives the velocity smoothed position estimate. The performance of the algorithm has been verified with pedestrian navigation tests; measurements for the tested algorithms were obtained using a SiRF Star II GPS receiver. Accuracy of the obtained position solutions was compared with a DGPS position solution, recorded using Thales MobileMappers. As the measure of accuracy, we used the horizontal distance of the standalone position solution from the DGPS track, i.e. the cross track error (CTE). The maximum and mean CTE values of 51.8 m and 6.3 m, respectively, were obtained with least squares (LS) position solution. With WLS and RAIM/FDE processed position, the maximum and mean errors were 25.2 m and 3.5 m. Applying CKF on WLS and RAIM/FDE processed position and velocity the values were 8.4 m and 3.1 m. Applying CKF on WLS and RAIM/FDE processed position and WLS velocity the values were 7.7 m and 2.8 m. Thus, adding the CKF to the WLS + RAIM/FDE processing reduces both the maximum and mean errors; the CKF has the largest effect on the maximum errors. Reports of this type of CKF for fusing GPS carrier and code phase information, with the additional security feature by RAIM/FDE, do not exist in the open literature.

This paper presents the design and implementation of the Bluetooth local positioning application. Positioning is based on received power levels, which are converted to distance estimates according to a simple propagation model. The extended Kalman filter computes a 3D position estimate on the basis of distance estimates. With the used Bluetooth hardware, the mean absolute error of positioning was measured to be 3.76 m. The accuracy can be improved if Bluetooth devices are able to measure received power levels more precisely.

This paper presents the design and implementation of a local positioning prototype. The prototype implements received signal power level based positioning on IEEE 802.11b wireless LAN (WLAN) platform. In addition to a WLAN adapter, the prototype includes a host PC, which runs a positioning application. The application computes and displays position estimates on the basis of measurements performed by the WLAN adapter. A signal propagation model and an extended Kalman filter are used for enhancing the position estimates. With the used WLAN hardware, the mean absolute error of positioning was measured to be 2.6 m.