Miracle-dz/UniFAR-FTUs huggingface dataset:
Detection of Lying Electrical Vehicles in Charging Coordination Using Deep Learning

Because charging coordination is a solution for avoiding grid instability by prioritizing charging requests, electric vehicles may lie and send false data to illegally receive higher charging priorities. In this article, we first study the impact of such attacks on both the lying and honest electric vehicles. Our evaluations indicate that lying electric vehicles have a higher chance of charging, whereas honest electric vehicles may not be able to charge or may charge late. Then, an anomaly-based detector based on a deep neural network is devised to identify lying electric vehicles. The idea is that since each electric vehicle driver has a particular driving pattern, the data reported by the corresponding electric vehicle should follow this pattern, and any deviation due to reporting false data can be detected. To train the detector, we first create an honest dataset for the charging coordination application using real driving traces and information provided by an electric vehicle manufacturer, and we then propose a number of attacks as a basis for creating malicious data. We train and evaluate a gated recurrent unit model using this dataset. Our evaluations indicate that our detector can detect lying electric vehicles with high accuracy and a low false alarm rate even when tested on attacks that are not represented in the training dataset.
Strengthening Post-Quantum Security for Automotive Systems

The long lifecycle of automotive products demands that not only current but also future threats are considered during the design of automotive security. Therefore, the foreseeable breakthrough of quantum computers represents a risk for the automotive industry and the integration of Post-Quantum Cryptography (PQC) gets necessary. Lattice-based PQC is an attractive alternative for securing automotive systems. It usually employs Error-Correcting Codes (ECC) to increase the security level and to decrease the failure rate. However, ECCs are vulnerable to timing attacks. To this end, we present in this work three contributions. First, we present an implementation of PQC tailor-made for a microcontroller used in automotive systems. Second, we integrate a more powerful ECC into ThreeBears, which is an efficient Post-Quantum scheme, in order to improve its security level and to decrease the failure rate. Finally, we implement a protected ECC implementation able to resist timing attacks. Results show that the integration of PQC in automotive environments is feasible and that optimization techniques can lead to a 55.98% performance improvement. Moreover, our ECC exploration achieves a failure rate decrease from 2−135 to 2−153. Alternatively, an increase of the security level from 2141 to 2144 can be achieved. Furthermore, the timing-protected ECC presents in total only a minor performance overhead.
TAEM: Fast Transfer-Aware Effective Loop Mapping for Heterogeneous Resources on CGRA

Coarse-grained reconfigurable architecture (CGRA) is an energy-efficient and processing-flexible parallel computing architecture. Efficiency of CGRA highly depends on how to map data dependencies using different CGRA resources. Previous works investigated different strategies for transferring data dependencies, using registers, processing elements (PEs) and memory. However, these works do not consider all those resources in CGRA and take a long time during compilation period. This paper proposes a Transfer-Aware Effective loop Mapping (TAEM) method for CGRA, which can efficiently utilize all those heterogeneous resources on CGRA and significantly accelerate the compilation time. Experimental results show that TAEM is able to reduce the compilation time by 11.1x over the state-of-the-art technique RAMP, while keeping the same or better performance of loop mapping results.
Participant Comfort Adaptation in Dependable Mobile Crowdsensing Services

Mobile Crowdsensing (MCS) is a ubiquitous sensing concept under the Internet of Things (IoT) ecosystem where builtin sensors in smart mobile devices are utilized as users join in sensing campaigns launched by the crowdsensing platform. The pervasive and non-dedicated nature of the sensing instruments in MCS raises the trustworthiness issue. On the other hand, due to granting access to the hardware on their devices, user comfort –which is directly related to the information revealed or the type of sensor activation by user– is also another barrier against wide adoption of MCS in the IoT Era. In this article, we present an adaptive mechanism to manage user comfort in an adaptive manner while ensuring the trustworthiness of the crowdsensed data through auction based reputation maintenance at the MCS platform. The proposed mechanism allows the users to adaptively switch their sensory allocation that are made available to the MCS platform based on historical tracking of the changes in their utility. Through simulations, we show that adaptive management of sensory selection in the auction-based MCS campaign can result in up to >3% increase in user comfort and up to >2% improvement in platform utility when compared to the fixed configuration of sensory arrays based on constant comfort levels used in user recruitment.
Optimization of Resource Allocation in Multi-Cell OFDM Systems: A Distributed Reinforcement Learning Approach

In this paper, the problem of joint subcarrier and power allocation is studied for multi-cell orthogonal frequency-division multiplexing (OFDM) systems. This joint subcarrier and power resource allocation problem is formulated as an optimization problem whose goal is to maximize the system spectral efficiency. To solve the proposed problem, the original optimization problem is first decomposed into two subproblems: subcarrier allocation and power allocation. By solving these two subproblems, an initial subcarrier and power allocation scheme is accordingly obtained. An multi-agent reinforcement learning (MARL) algorithm is proposed to further increase the spectral efficiency. In particular, using the proposed MARL algorithm, each BS can adapt its allocation scheme according to the wireless environmental states. Numerical results show that the proposed MARL method can achieve up to 53.6% gain in terms of spectral efficiency compared to the conventional scheme. The proposed MARL scheme also converges more rapidly than the conventional single-agent Q-learning approach.
Seesaw: End-to-end Dynamic Sensing for IoT using Machine Learning

IoT edge devices’ small form factor places tight constraints on their battery life. These devices are often equipped with multiple sensors, a few of them responsible for most of the energy usage. Naïvely lowering the sensing rate of these power-hungry sensors reduces energy consumption, but also degrades application’s output quality. In this work, we observe that it is possible to leverage low-power sensors in a system to predict the impact of throttling a power-intensive sensor on application’s output accuracy. We thus propose Seesaw, an end-to-end ML-based solution that automatically identifies correlations between power-intensive and lightweight sensors without human expertise. Further, Seesaw deploys a low-overhead decision tree predictor to determine the optimal sensing rates for power-intensive sensors, thus avoiding significant quality degradation. We show that Seesaw improves battery life for (1) video recording on mountable video cameras and (2) route tracking on fitness trackers by 32% and 66%, respectively, without significant accuracy loss.
On a Security-oriented Design Framework for Medical IoT Devices: The Hardware Security Perspective

As medical devices more and more use Internet of Things based technologies, serious concerns are raised about their security and the privacy of patient’s personal health data. To address these concerns, while maintaining reasonable overheads, designers of medical devices need to take security into account from the beginning until the completion of their designs. In this work we identify the relevant security domains and focus to the Hardware Security perspective. Additionally, we present a secure design and evaluation framework which can assist designers towards more secure medical devices. The framework integrates a complete insulin pump architecture containing all the basic components used in such applications. To illustrate the advantages of the proposed framework we perform a Side Channel Analysis attack against the embedded encryption algorithm of the device to obtain the secret encryption key. Then, we make use of the framework to identify all the components of the system which are either directly or indirectly affected by the attack. This analysis leads us to determine more complex combined attacks which may complement the SCA attack into compromising the overall security of the system.
Enabling Human-Robot-Interaction for Remote Robotic Operation via Augmented Reality

Human-Robot Interaction (HRI) will be a crucial component of smart factories of the future (FoF). This demo presents a reliable and cost-effective HRI system based on Augmented Reality (AR). In this demonstration, we offer concepts and methods currently being developed to enable high-level human-robot collaboration and interaction. The user interaction and instructions are captured via AR and communicated to a Robot Operating System (ROS) powered robotic arm.
Programmable Systems for Intelligence in Automobiles (PRYSTINE): Technical Progress after Year 2

Autonomous driving has the potential to disruptively change the automotive industry as we know it today. For this, fail-operational behavior is essential in the sense, plan, and act stages of the automation chain in order to handle safety-critical situations by its own, which currently is not reached with state-of-the-art approaches.The European ECSEL research project PRYSTINE realizes Fail-operational Urban Surround perceptION (FUSION) based on robust Radar and LiDAR sensor fusion and control functions in order to enable safe automated driving in urban and rural environments. This paper showcases some of the key results (e.g., novel Radar sensors, innovative embedded control and E/E architectures, pioneering sensor fusion approaches, AI controlled vehicle demonstrators) achieved until year 2.
Efficient Multi-Grained Wear Leveling for Inodes of Persistent Memory File Systems

Existing persistent memory file systems usually store inodes in fixed locations, which ignores the external and internal imbalanced wears of inodes on the persistent memory (PM). Therefore, the PM for storing inodes can be easily damaged. Existing solutions achieve low accuracy of wear-leveling with high-overhead data migrations. In this paper, we propose a Lightweight and Multi-grained Wear-leveling Mechanism, called LMWM, to solve these problems. We implement the proposed LMWM in Linux kernel based on NOVA, a typical persistent memory file system. Compared with MARCH, the state-of-theart wear-leveling mechanism for inode table, experimental results show that LMWM can improve 2.5× lifetime of PM and 1.12× performance, respectively.
TSN-Builder: Enabling Rapid Customization of Resource-Efficient Switches for Time-Sensitive Networking

Time-Sensitive Networking (TSN) emerges as a promising technique empowering deterministic forwarding on standard Ethernet without sacrificing compatibility. There are some commercial off-the-shelf (COTS) switches that support TSN recently. However, the resource partitioning on these switches is normally inefficient for the on-chip memory resource in many specific application scenarios. We observe that the critical requirements (e.g., topology, flow features) of these scenarios are pre-determined. Thus, developing a TSN switch in a Top-down approach is feasible and urgently needed.In this paper, we propose TSN-Builder, a template-based developing model for customizing resource-efficient TSN switches rapidly with targeted application-dependent requirements. TSN-Builder decomposes the integrated TSN switching function into multiple function templates. With a fine-grained resource abstraction, TSN-Builder provides platform-independent customization interfaces for developers to customize the resource parameters. We prototype TSN switches on FPGA to evaluate the resource consumption and performance under different application scenarios. Experimental results show that TSN-Builder reduces the on-chip memory by up to 80.53% under the same Quality-of-Service, compared to the resource configuration in the COTS switch.
Secrecy Performance Analysis of Adaptive SSK Transmission

In this paper, we propose an adaptive antenna index (AI) mapping scheme with the aid of partial channel state information (CSI) (i.e., channel gain order information) of legitimate channel. Based on the received partial CSI that is unknown to an eavesdropper (Eve), transmitter (Tx) adapts AI mapping patterns. In addition, Tx also adapts transmit power allocation to each transmit antenna according to the partial CSI. Since Eve is unaware of the partial CSI of the legitimate channel, it cannot successfully decode the AI patterns; and transmission over the legitimate channel is secured from wiretapping of Eve. Further, we offer an accurate analytical framework for ergodic secrecy rate (ESR) and bit error rate (BER) of 2-ary space shift keying modulated multiple-input single-output system in the presence of a passive eavesdropper. Moreover, the analytical results of ESR and BER demonstrate the robustness of the proposed scheme. All numerical results are thoroughly verified by using the Monte Carlo simulations.
Bi-Objective Optimization of Data-Parallel Applications on Heterogeneous HPC Platforms for Performance and Energy Through Workload Distribution

Performance and energy are the two most important objectives for optimization on modern parallel platforms. In this article, we show that moving from single-objective optimization for performance or energy to their bi-objective optimization on heterogeneous processors results in a tremendous increase in the number of optimal solutions (workload distributions) even for the simple case of linear performance and energy profiles. We then study full performance and energy profiles of two real-life data-parallel applications and find that they exhibit shapes that are non-linear and complex enough to prevent good approximation of them as analytical functions for input to exact algorithms or optimization software for determining the Pareto front. We, therefore, propose a solution method solving the bi-objective optimization problem on heterogeneous processors. The method's novel component is an efficient and exact global optimization algorithm that takes as an input performance and energy profiles as arbitrary discrete functions of workload size, which accurately and realistically take into account resource contention and NUMA inherent in modern parallel platforms, and returns the Pareto-optimal solutions (generally speaking, load imbalanced). To construct the input discrete energy functions, the method employs a methodology that accurately models the energy consumption by a hybrid data-parallel application executing on a heterogeneous HPC platform containing different computing devices using system-level power measurements provided by power meters. We experimentally analyse the proposed solution method using three data-parallel applications, matrix multiplication, 2D fast Fourier transform (2D-FFT), and gene sequencing, on two connected heterogeneous servers consisting of multicore CPUs, GPUs, and Intel Xeon Phi. We show that it determines a superior Pareto front containing the best load balanced solutions and all the load imbalanced solutions that are ignored by load balancing methods.
IoTSpy: Uncovering Human Privacy Leakage in IoT Networks via Mining Wireless Context

Internet of Things (IoT) has been emerging as one of the most significant technologies that may change the world. The development and deployment of IoT systems significantly improve the efficiency of work, advance the development of productive force, and affect the human society’s modes of production, working, and life deeply. The number of deployed IoT devices and applications has had explosive growth in the past years. While people enjoy the benefits brought by IoT, the addressing of its security and privacy issues does not keep pace with the development of IoT technologies. We find that encrypted wireless IoT traffic can still leak information about user privacy. We can infer what the user is doing at home by deploying a wireless sniffer outside the user’s house.In this paper, we propose a method to eavesdrop different kinds of user privacy via analyzing the wireless context. First, we extract the packet sequence features to fingerprint and detect the IoT events. Then we analyze the detected IoT events and infer the user’s activities and even the user’s moods. Furthermore, by analyzing the wireless context, which is discovered by mining the IoT event dependencies, we can infer the user’s living habits, routines, and even installed IoT applications. The leakage of such information not only exposes the user’s privacy but also extends the attack surface that an attacker can utilize to control the smart home. We implement our eavesdropping approach and conduct extensive experiments on the SmartThings platform. The evaluation demonstrates the feasibility and effectiveness of our eavesdropping approach.
A Vision based Voice Controlled Indoor Assistant Robot for Visually Impaired People

Due to ever-increasing number of visually impaired people, the demands of assistant robots have been growing significantly. For the visually impaired, "independent living" which is the key to rebuild the dignity and self-confidence that can be achieved by assistant robot. This paper describes the design and development of a personal assistant robot, which is controlled by voice commands to find and determine the relative location of the object in the indoor environment with a corelation factor (CRF) algorithm developed in this study. The proposed assistant robot is a semi-humanoid robot consists of several cameras on the different location of its body. The cameras are used for autonomous movement, object detection, distance measurement, and motion planning. In addition, the robot keeps the user informed about the outcomes of its actions increases the usefulness of the robot. To justify the proposed system and measure its performance, successful experiments were conducted in indoor environments. The experimental results demonstrate that the proposed assistant robot performs all of its actions with high accuracy which will make visually impaired people feel the indoor environment safe, convenient and comfortable.
Development of Uric Acid Sensor Using Copper Oxide and Silver Nanoparticles Immobilized SMSMS Fiber Structure-Based Probe

Fabrication and experimental validation of a newly developed copper oxide nanoparticles (CuO-NPs) and silver nanoparticles (AgNPs) immobilized SM-MM-SM-MM-SM (SMSMS) in-line Mach–Zehnder interferometer (MZI) fiber structure-based sensor using localized surface plasmon resonance (LSPR) technique are reported in this article. The AgNPs and CuO-NPs are used to improve the biocompatibility and sensitivity of the sensor. The nanoparticles (NPs) and immobilized probe were characterized by UV-Vis spectrophotometer, HR-TEM, SEM, and EDX, revealing superior performance over the existing uric acid (UA) sensors. The proposed optimized structure-based optical fiber sensors (OFSs) exhibit excellent specificity, sensitivity, and limit of detection (LoD) for the detection of UA. Moreover, the developed sensor exhibits linear profile over a very wide range: 1) $10~\mu \text{M}$ –1 mM for detection over UA presents in serum, while the normal range of UA in serum is 100– $400~\mu \text{M}$ and 2) 0.4–10 mM for detection over UA presents in urine, while the normal range of UA in urine is 1.5–4.4 mM. In addition, the developed sensor has a distinguished advantage, such as multicompatibility over the detection of UA presents in both serum and urine along with its high selectivity, reusability, reproducibility, and lowest LoD. The proposed AgNPs and CuO-NPs immobilized and the uricase functionalized SMSMS structure-based LSPR sensor has better compatibility and are potent candidates for medical applications.
A Hybrid-Integrated Artificial Mechanoreceptor in 180nm CMOS

A low-power wireless chip for an implantable tactile sensor system is presented. The reported ASIC utilizes the low-loss magnetic human body communication channel for both wireless powering and data transfer. The chip is hybrid-integrated with an in-house fabricated MEMS capacitive force sensor to form an implantable artificial mechanoreceptor (IAM). An on-chip correlated double sampling capacitance-to-time converter consumes 3. 9µW from a 1.2V on-chip regulated supply and achieves a resolution of 22.8fF over an input capacitance range of 100pF, while occupying an area of only 0.04mm2. A wireless power management feedback is used to ensure robust operation for different hand gestures and under process-voltage-temperature (PVT) variations. The IAM can operate in on-off keying (OOK) or frequency-shift keying (FSK) modulation formats, where the transmitter consumes only 15. 6µW in OOK mode and is more immune to hand gesture variations in FSK mode. The 1.62mm2 chip is fabricated in a standard 180nm CMOS process and consumes 104. 3µW.
An Approach to Cost-Efficient Fault Tolerance in Inherently Redundant Fail-Operational Systems

Embedded systems in safety-critical environments are often subject to strict reliability requirements. This holds particularly true for modern fail-operational systems. In order to deliver a guaranteed minimum functionality at all times, these systems are often based on expensive fault tolerance mechanisms. In this work, we consider fail-operational systems with inherent redundancy. This property describes the presence of multiple hardware components, each of which is underutilized to a certain degree and thus able to serve as a fallback for one of the other components. We propose an off-chip fault tolerance mechanism for a pair of inherently redundant execution units that requires no further replication of these expensive resources. The key component of this concept is a lightweight proxy unit that handles faults of one execution unit by dynamically migrating the safety-critical portion of its functionality to its redundant counterpart. We present a prototypical implementation of this concept and evaluate the fault handling time of the resulting system experimentally. The results show that for an exemplary, processor-based control system with 256 bits of internal state, a cycle time of four milliseconds, and 64 bits of payload data that are read from or written to attached devices per cycle, the presented implementation is able to detect the failure of a unit, activate a fallback functionality on the complementary unit, and restore the internal state variables within five milliseconds.
Propagation of Intense Electron Beams Through a Rectangular Tube

In this study, cylindrical intense electron beams were injected into a rectangular tube. The beam cross section profiles were observed along the propagation path. The single-beam profile was deformed periodically. The center of the beam cross section was shifted when the beam was not injected just on the tube center. When two beams were injected, the beams rotated each other, and the distance between them decreased. Using a simple model with momentum equations of the line density elements for the beam, it was evident that $E\times B$ drift, which originated from the asymmetric electric field and the magnetic field on the beam, caused the previously mentioned phenomena.