—Drones equipped with microelectromechanical sys- tem (MEMS) inertial measurement unit (IMU) sensors are exposed to acoustic injection attacks. These attacks resonate sensors, compromising their output and causing drones to crash. Several mitigation strategies have been proposed; however, they are limited in terms of practicality as they cannot make the drone fly t o its planned destination in the event of an attack. To remedy this, we aim at recovering the compromised sensor values for the practical mitigation of acoustic injection attacks. To achieve this, we first c onstructed a r ealistic testbed and delved into the implications of resonant MEMS sensors on drones. We discovered that sampling jitter, which refers to the inconsistent timing delay in retrieving sensor values, has a significant i m pact o n d r one c r ashes d u ring t h e a t tack. Note that while any real-time system needs to satisfy its real-time

/pdf/un-rocking-drones-foundations-of-acoustic-injection-attacks-10svfmgk.pdf

Un-Rocking Drones: Foundations of Acoustic Injection Attacks and Recovery Thereof

Smart inertial sensors have emerged in the last years enabling developers to implement sensor fusion or tasks like gesture detection directly in the sensor hardware and thus reducing the processing latency and energy consumption. The development of software for smart inertial sensors however faces difficulties from the limited hardware capabilities of the $\mu \mathrm{C}$ hardware and the lack of options to conduct reproducible tests directly on the hardware. We propose a Sensor-in-the-Loop architecture that allows a developer to record data from a smart sensor and inject the recorded data back into the sensor at a later time to evaluate the performance of the software in a reproducible way. With the proposed architecture it is possible to examine the performance and computational load on real hardware with recorded data thus allowing developers to optimize and improve the software in a more targeted way. In our implementation, the memory overhead for the code instrumentalization is just 0.63%. The used RTT interface is at least four times faster than the regular SPI sensor interface. In experiments, we show that our proposed architecture is able to record and inject data of up to three 3-DOF sensors with 1.6kHz sampling frequency in real time.

Advanced Debugging Architecture for Smart Inertial Sensors using Sensor-in-the-Loop

Pedestrian Dead Reckoning is a method estimating a persons path from a known starting point based on length and direction of all performed steps. Measuring these parameters, e.g. using inertial sensors, introduces small errors that accumulate quickly into large distance errors. Knowledge of a building's model may reduce these errors as it can be used to keep the estimated position from moving through walls and onto likely paths. Common indoor localization approaches like particle filters track, verify and re-sample several hundred positioning estimates with each user step, resulting in a comparably high computational load. In this paper, we use backtracking to improve an existing localization system tracking a single localization estimate using a foot-mounted inertial sensor and a smartphone. We show how backtracking a single localization estimate can improve the accuracy of indoor positioning systems and discuss restrictions and disadvantages of this approach. Our quantitative results show a reduction of the positioning error by up to 75% and an average endpoint accuracy of 1.91% of the travelled distance with an average computation time of $356.7\mu s$ on a 2014 Motorola G2 smartphone.

Improving Pedestrian Dead Reckoning Using Likely Paths and Backtracking for Mobile Devices

In this work, four sensor fusion algorithms for inertial measurement unit data to determine the orientation of a device are assessed regarding their usability in a hardware restricted environment such as body-worn sensor nodes. The assessment is done for both the functional and the extra-functional properties in the context of human operated devices. The four algorithms are implemented in three data formats: 32-bit floating-point, 32-bit fixed-point and 16-bit fixed-point and compared regarding code size, computational effort, and fusion quality. Code size and computational effort are evaluated on an ARM Cortex M0+. For the assessment of the functional properties, the sensor fusion output is compared to a camera generated reference and analyzed in an extensive statistical analysis to determine how data format, algorithm, and human interaction influence the quality of the sensor fusion. Our experiments show that using fixed-point arithmetic can significantly decrease the computational complexity while still maintaining a high fusion quality and all four algorithms are applicable for applications with human interaction.

https://www.mdpi.com/1424-8220/21/8/2747/pdf

On the Functional and Extra-Functional Properties of IMU Fusion Algorithms for Body-Worn Smart Sensors.

There are numerous uses of human body motion analysis, including rehabilitation of patients, training of athletes and others. Use of Inertial Measurement Units (IMUs) is one process for analyzing human movements. Accelerometer, Gyroscope, and Magnetometer make up IMUs, however each sensor has its own limitations. In this context, sensor fusion techniques, such as Madgwick Filter (MAD), Mahony Filter (MAH), Complementary Filter (CF), Kalman Filter (KF), Extended Kalman Filter (EKF), Unscented Kalman Filter (UKF) and others are used to address such shortcomings. In this study, WitMotion sensor has been employed for range of motion (ROM) analysis of a proposed 2D rigid body and performance of WitMotion provided sensor fusion algorithm has been verified in comparison with an Electro-Goniometer (EG). The main objective of this work is to identify a reliable and effective sensor fusion technique for measuring upper limb ROM so that experiments can be repeated in various conditions. The results of this study demonstrate that the WitMotion supplied sensor fusion technique achieves sufficient accuracy, while the Madgwick filter (MAD) performs slightly better than all other considered algorithms based on Root Mean Square Error (RMSE). 

Range of Motion Measurement Using Single Inertial Measurement Unit Sensor: A Validation and Comparative Study of Sensor Fusion Techniques

In IoT domain energy aware firmware development is critical for applications that run on mobile or battery constrained devices. Virtual system prototypes (VSP) empower developers to assess the application power consumption behavior before actual hardware prototypes become available. The SystemC modeling language has become the widely adopted industry standard for the implementation of such VSPs. In this paper, we present a novel approach for extending SystemC based VSPs with pluggable, pre-compiled power models that can be configured during runtime to generate accurate power profiles for the simulated firmware. The necessary modifications to the VSP are kept minimal. We demonstrate the application of our approach by instrumenting a pre-existing SystemC model for a state-of-the-art MEMS-based inertial sensor with a power model and show that the generated power profile estimation matches closely the energy consumption measured from its hardware prototype. As an additional advantage of our proposed precompiled approach, manufactures can ship their power models to costumers without disclosing implementation IP.

SystemC Power Profiling for IoT Device Firmware using Runtime Configurable Models

Due to upcoming higher integration levels of microprocessors, the market of inertial sensors has changed in the last few years. Smart inertial sensors are becoming more and more important. This type of sensor offers the benefit of implementing sensor-processing tasks directly on the sensor hardware. The software development on such sensors is quite challenging. In this article, we propose an approach for using prerecorded sensor data during the development process to test and evaluate the functionality and timing of the sensor firmware in a repeatable and reproducible way on the actual hardware. Our proposed Sensor-in-the-Loop architecture enables the developer to inject sensor data during the debugging process directly into the sensor hardware in real time. As the timing becomes more critical in future smart sensor applications, we investigate the timing behavior of our approach with respect to timing and jitter. The implemented approach can inject data of three 3-DOF sensors at 1.6 kHz. Furthermore, the jitter shown in our proposed sampling method is at least three times lower than using real sensor data. To prove the statistical significance of our experiments, we use a Gage R&R analysis, extended by the assessment of confidence intervals of our data.

https://www.mdpi.com/1424-8220/21/14/4675/pdf

Investigation of Timing Behavior and Jitter in a Smart Inertial Sensor Debugging Architecture.

Pedestrian Dead Reckoning is a method estimating a persons path from a known starting point based on length and direction of all performed steps. Measuring these parameters, e.g. using inertial sensors, introduces small errors that accumulate quickly into large distance errors. Knowledge of a buildings geography may reduce these errors as it can be used to keep the estimated position from moving through walls and onto likely paths. In this paper, we use building maps to improve localization based on a single foot-mounted inertial sensor. We show how correction algorithms using likely and unlikely paths can rectify errors intrinsic to pedestrian dead reckoning tasks and discuss restrictions and disadvantages of these algorithms. Our quantitative results show an endpoint accuracy improvement of up to 60% when using likely paths and 23% when using unlikely paths. However, both approaches can also decrease accuracy in certain scenarios. We identify those scenarios and offer further ideas for improving Pedestrian Dead Reckoning methods.

Improving Pedestrian Dead Reckoning using Likely and Unlikely Paths

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