Motion tracking problems in Internet of Things (IoT) and wireless networking

The dissertation focuses on inferring various motion patterns of internet-of-things (IoT) devices, by leveraging inertial sensors embedded in these objects, as well as wireless signals emitted (or reflected) from them. For instance, we use a combination of GPS signals and inertial sensors on drones to precisely track its 3D orientation over time, ultimately improving safety against failures and crashes. In another application in sports analytics, we embed sensors and radios inside baseballs and cricket balls and compute their 3D trajectory and spin patterns, even when they move at extremely high speeds. In a third application for wireless networks, we explore the possibility of physically moving wireless infrastructure like Access Points and basestations on robots and drones for enhancing the network performance. While these are diverse applications in drones, sports analytics, and wireless networks, the common theme underlying the research is in the development of the core motion-related building blocks. Specifically, we emphasize the philosophy of "fusion of multi modal sensor data with application specific model” as the design principle for building the next generation of diverse IoT applications. To this end, we draw on theoretical techniques in wireless communication, signal processing, and statistics, but translate them to completely functional systems on real-world platforms

Implementation of African Satellite Augmentation System (ASAS) for Maritime Applications

This paper introduces implementation of the new project known as African Satellite Augmentation System (ASAS) for Africa and Middle East, designed by the CNS Systems Company and its research group supported by partners. The ASAS project as Regional Satellite Augmentation Systems (RSAS) will provide service for maritime, land (road and rail), and aeronautical applications. Thus, with existing and other newly designed RSAS networks, it will be integrated in Global Satellite Augmentation System (GSAS) with new Satellite Communication, Navigation and Surveillance (CNS) for improved Ship Traffic Control (STC) and Ship Traffic Management (STM). This System also enhances safety and emergency systems, transport security and control of ocean shipping freight, logistics and the security of the crew and passengers onboard ships and fishing vessels as well. The current CNS infrastructures of the first generation of Global Navigation Satellite System (GNSS-1) applications are represented by old fundamental solutions for Position, Velocity, and Time (PVT) of the satellite navigation and determination systems, such as the US GPS and Russian (former USSR) GLONASS military requirements, respectively. The establishment of Space, Ground, and User segment, including Local Satellite Augmentation System (LSAS), are discussed as a new basic infrastructures for maritime and other mobile applications, which will be integrated with RSAS in the future GSAS network

Security of GPS/INS based On-road Location Tracking Systems

Location information is critical to a wide-variety of navigation and tracking applications. Today, GPS is the de-facto outdoor localization system but has been shown to be vulnerable to signal spoofing attacks. Inertial Navigation Systems (INS) are emerging as a popular complementary system, especially in road transportation systems as they enable improved navigation and tracking as well as offer resilience to wireless signals spoofing, and jamming attacks. In this paper, we evaluate the security guarantees of INS-aided GPS tracking and navigation for road transportation systems. We consider an adversary required to travel from a source location to a destination, and monitored by a INS-aided GPS system. The goal of the adversary is to travel to alternate locations without being detected. We developed and evaluated algorithms that achieve such goal, providing the adversary significant latitude. Our algorithms build a graph model for a given road network and enable us to derive potential destinations an attacker can reach without raising alarms even with the INS-aided GPS tracking and navigation system. The algorithms render the gyroscope and accelerometer sensors useless as they generate road trajectories indistinguishable from plausible paths (both in terms of turn angles and roads curvature). We also designed, built, and demonstrated that the magnetometer can be actively spoofed using a combination of carefully controlled coils. We implemented and evaluated the impact of the attack using both real-world and simulated driving traces in more than 10 cities located around the world. Our evaluations show that it is possible for an attacker to reach destinations that are as far as 30 km away from the true destination without being detected. We also show that it is possible for the adversary to reach almost 60-80% of possible points within the target region in some cities

Characterisation of GNSS carrier phase data on a moving zero-baseline in urban and aerial navigation

We present analyses of Global Navigation Satellite System (GNSS) carrier phase observations in multiple kinematic scenarios for different receiver types. Multi-GNSS observations are recorded on high sensitivity and geodetic-grade receivers operating on a moving zero-baseline by conducting terrestrial urban and aerial flight experiments. The captured data is post-processed; carrier phase residuals are computed using the double difference (DD) concept. The estimated noise levels of carrier phases are analysed with respect to different parameters. We find DD noise levels for L1 carrier phase observations in the range of 1.4–2 mm (GPS, Global Positioning System), 2.8–4.6 mm (GLONASS, Global Navigation Satellite System), and 1.5–1.7 mm (Galileo) for geodetic receiver pairs. The noise level for high sensitivity receivers is at least higher by a factor of 2. For satellites elevating above 30◦, the dominant noise process is white phase noise. For the flight experiment, the elevation dependency of the noise is well described by the exponential model, while for the terrestrial urban experiment, multipath and diffraction effects overlay; hence no elevation dependency is found. For both experiments, a carrier-to-noise density ratio (C/N0) dependency for carrier phase DDs of GPS and Galileo is clearly visible with geodetic-grade receivers. In addition, C/N0 dependency is also visible for carrier phase DDs of GLONASS with geodetic-grade receivers for the terrestrial urban experiment. © 2020 by the authors. Licensee MDPI, Basel, Switzerland

A Low Cost Mass-Market Deployable Security Approach Against GPS Spoofing Attacks

The Global Positioning System (GPS) is used ubiquitously for navigation and timing synchronization purposes. Many telecommunication, finance and aviation systems rely heavily on GPS information for routine operations. GPS functions by relying on satellites orbiting the earth in very accurately predictable orbits, which are used as references to identify the positions of objects (receivers). Receivers calculate their positions by receiving GPS signals and calculating their relative distances to each of the satellites. With enough relative distances, the receiver can resolve its position using the method known as trilateration [1]. In this thesis, we underline the vulnerability of this orbiting infrastructure to spoofing attacks, by easily procurable and affordable software defined radios. GPS Signal spoofing is a type of malicious attack, where an attacker generates fake GPS signal with valid GPS properties but false navigational and/or timing information to fool non-suspecting receivers. These signals appear authentic and receivers end up processing the false signal and extracting wrong information. There are two types of GPS services, civilian and military. The military service is encrypted and not vulnerable to such attacks because the pseudorandom codes are not disclosed to the public. However, this service is accessible to authorized military personnel alone. All other commercial and public GPS receivers which form the mass of the population are vulnerable to spoofing attacks. The civilian GPS broadcast band is not encrypted, and this makes it easy for an attacker to recreate the signal that appears valid to GPS receivers. In this thesis we implement a low cost, easy for mass-market application Doppler measurement based spoofing detection approach, utilizing non-specialized off the shelf commercial receivers

Development of Non Expensive Technologies for Precise Maneuvering of Completely Autonomous Unmanned Aerial Vehicles

In this paper, solutions for precise maneuvering of an autonomous small (e.g., 350-class) Unmanned Aerial Vehicles (UAVs) are designed and implemented from smart modifications of non expensive mass market technologies. The considered class of vehicles suffers from light load, and, therefore, only a limited amount of sensors and computing devices can be installed on-board. Then, to make the prototype capable of moving autonomously along a fixed trajectory, a “cyber-pilot”, able on demand to replace the human operator, has been implemented on an embedded control board. This cyber-pilot overrides the commands thanks to a custom hardware signal mixer. The drone is able to localize itself in the environment without ground assistance by using a camera possibly mounted on a 3 Degrees Of Freedom (DOF) gimbal suspension. A computer vision system elaborates the video stream pointing out land markers with known absolute position and orientation. This information is fused with accelerations from a 6-DOF Inertial Measurement Unit (IMU) to generate a “virtual sensor” which provides refined estimates of the pose, the absolute position, the speed and the angular velocities of the drone. Due to the importance of this sensor, several fusion strategies have been investigated. The resulting data are, finally, fed to a control algorithm featuring a number of uncoupled digital PID controllers which work to bring to zero the displacement from the desired trajectory

A Review on IoT Deep Learning UAV Systems for Autonomous Obstacle Detection and Collision Avoidance

[Abstract] Advances in Unmanned Aerial Vehicles (UAVs), also known as drones, offer unprecedented opportunities to boost a wide array of large-scale Internet of Things (IoT) applications. Nevertheless, UAV platforms still face important limitations mainly related to autonomy and weight that impact their remote sensing capabilities when capturing and processing the data required for developing autonomous and robust real-time obstacle detection and avoidance systems. In this regard, Deep Learning (DL) techniques have arisen as a promising alternative for improving real-time obstacle detection and collision avoidance for highly autonomous UAVs. This article reviews the most recent developments on DL Unmanned Aerial Systems (UASs) and provides a detailed explanation on the main DL techniques. Moreover, the latest DL-UAV communication architectures are studied and their most common hardware is analyzed. Furthermore, this article enumerates the most relevant open challenges for current DL-UAV solutions, thus allowing future researchers to define a roadmap for devising the new generation affordable autonomous DL-UAV IoT solutions.Xunta de Galicia; ED431C 2016-045Xunta de Galicia; ED431C 2016-047Xunta de Galicia; , ED431G/01Centro Singular de Investigación de Galicia; PC18/01Agencia Estatal de Investigación de España; TEC2016-75067-C4-1-

Localization and Trajectory Control Algorithms Applied on Drones

In this dissertation, the trajectory control of the quadcopter is explored and developed with the objective of finding the best way travel in terms of speed and energy consumption. The sensor fusion of several GPS modules is implemented as an algorithm that provides better localization measurements and reduces noise. An attempt to identify the NAZA® attitude controller in order to obtain its mathematical model is also subjects of this thesis. Trajectory algorithms are designed and tested with and without faults in the motors, on the X8 configuration in Simulink®. The main contributions are the improved GPS signal reception and algorithms for an autonomous trajectory following quadcopter. Experiments in the real-world quadcopter were done in order to validate the performance of such contributions. The simulations and experiments presented good performance of the quadcopter’s behavior when integrating the filtered GPS signal. Simulations show the continuous improvement for trajectory generation and following of the drone between the three controllers tested (from worst to best): PID, state space feedback and differential flatness