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.

Vulnerability of satellite-based navigation signals to intentional and unintentional interference calls for a high-level overview of Global Navigation Satellite System (GNSS) threats occurring globally to understand the magnitude and evolution of the problem. Therefore, a mechanism needs to be developed whereby disparate monitoring systems will be capable of contributing to a common entity of basic information about the threat scenarios they experience. This paper begins with a literature survey of 37 state-of-the-art GNSS threat monitoring systems, which have been analysed based on their respective operational features - constellations monitored and whether they possess the capability to perform interference-type classification, spoofing detection, and interference localisation. Also described is a comparative analysis of four GNSS threat reporting formats in use today. Based on these studies, the paper describes the Horizon2020 Standardisation of GNSS Threat Reporting and Receiver Testing through International Knowledge Exchange, Experimentation and Exploitation (STRIKE3) proposed integrated threat monitoring demonstration system and related standardised threat reporting message, to enable a high-level overview of the prevailing international GNSS threat scenarios and its evolution over time.

Collaborative navigation is the most promising technique for infrastructure-free indoor navigation for a group of pedestrians, such as rescue personnel. Infrastructure-free navigation means using a system that is able to localize itself independent of any equipment pre-installed to the building using various sensors monitoring the motion of the user. The most feasible navigation sensors are inertial sensors and a camera providing motion information when a computer vision method called visual odometry is used. Collaborative indoor navigation sets challenges to the use of computer vision; navigation environment is often poor of tracked features, other pedestrians in front of the camera interfere with motion detection, and the size and cost constraints prevent the use of best quality cameras resulting in measurement errors. We have developed an improved computer vision based collaborative navigation method addressing these challenges using a depth (RGB-D) camera, a deep learning based detector to avoid using features found from other pedestrians and for controlling the inconsistency of object depth detection, which would degrade the accuracy of the visual odometry solution if not controlled. Our analysis show that our method improves the visual odometry solution using a low-cost RGB-D camera. Finally, we show the result for computing the solution using visual odometry and inertial sensor fusion for the individual and UWB ranging for collaborative navigation.

Integration of different positioning systems such as wireless local area networks (WLAN) and INS has proven to be the most promising approach for navigation in global navigation satellite system challenging environments. However, the integrated solution suffers from the errors induced by the sensors in the INS and the low availability and infrastructure dependency of the WLAN. Visual aiding is a complementary method for augmenting these systems, because it suffers from errors and availability problems of a different nature. We introduce the concept of a “visual gyroscope” and a “visual odometer,” based on recovering user information by tracking the feature motion between consecutive images. All calculations are of sufficiently low complexity to be adoptable for navigation with current smartphones. The camera orientation is observed using the vanishing point locations, and this information is transformed into user heading change information and also used for visual odometer calculations. The visual odometer retrieves the camera translation information based on a special camera configuration and the motion of the matched scale-invariant feature transform features. The performance of both of these tools is evaluated. Experiments in selected environments have proven that visual aiding with a visual gyroscope and visual odometer improves the two-dimensional navigation solution significantly.

Global Navigation Satellite System (GNSS)-based positioning is experiencing rapid changes. The existing GPS and the GLONASS systems are being modernized to better serve the current challenging applications under harsh signal conditions. These modernizations include increasing the number of transmission frequencies and changes to the signal components. In addition, the Chinese BeiDou Navigation Satellite system (BDS) and the European Galileo are currently under development for global operation. Therefore, in view of these new upcoming systems the research and development of GNSS receivers has been experiencing a new upsurge. In this article, the authors discuss the main functionalities of a GNSS receiver in view of BDS. While describing the main functionalities of a software-defined BeiDou receiver, the authors also highlight the similarities and differences between the signal characteristics of the BeiDou B1 open service signal and the legacy GPS L1 C/A signal, as in general they both exhibit similar characteristics. In addition, the authors implement a novel acquisition technique for long coherent integration in the presence of NH code modulation in BeiDou D1 signal. Furthermore, a simple phase-preserved coherent integration based acquisition scheme is implemented for BeiDou GEO satellite acquisition. Apart from the above BeiDou-specific implementations, a novel Carrier-to-Noise-density ratio estimation technique is also implemented in the software receiver, which does not necessarily require bit synchronization prior to estimation. Finally, the authors present a BeiDou-only position fix with the implemented software-defined BeiDou receiver considering all three satellite constellations from BDS. In addition, a true multi-GNSS position fix with GPS and BDS systems is also presented while comparing their performances for a static stand-alone code phase-based positioning.

A wide variety of sensors and devices are used in indoor positioning scenarios to improve localization accuracy and overcome harsh radio propagation conditions. The availability of these individual sensors suggests the idea of sensor fusion to achieve a more accurate solution. This work aims to address, with the goal of improving localization accuracy, the fusion of two conventional candidates for indoor positioning scenarios: Ultra Wide Band (UWB) and Wireless Fidelity (WiFi). The proposed method consists of a Machine Learning (ML)-based enhancement of WiFi measurements, environment observation, and sensor fusion. In particular, the proposed algorithm takes advantage of Received Signal Strength (RSS) values to fuse range measurements utilizing a Gaussian Process (GP). The range values are calculated using the WiFi Round Trip Time (RTT) and UWB Two Way Ranging (TWR) methods. To evaluate the performance of the proposed method, trilateration is used for positioning. Furthermore, empirical range measurements are obtained to investigate and validate the proposed approach. The results prove that UWB and WiFi, working together, can compensate for each other’s limitations and, consequently, provide a more accurate position solution.