Electro-hydraulic servo valve is a complex component integrating machine, electricity, and fluid, which is widely used in aerospace hydraulic system. It is a key component of the hydraulic system, and as a highly reliable and integrated component, faults are often concealed, and acquiring labeled fault samples is challenging. These factors limit the development of efficient fault diagnose based method of data-driven. In this paper, a hybrid digital twin modeling technique combining physical model and data-driven is proposed for electro-hydraulic servo valve fault diagnosis under insufficient or uneven sample size. Firstly, a high-fidelity digital twin model of the servo valve is built by combining virtual simulation based on physical model and generative adversarial network. Then using the built digital twin model, simulated signals under fault conditions are generated to expand the sample size and train the data-driven convolutional neural network-based fault diagnosis model. The experimental results show that the proposed diagnostic framework can solve the problem of the lack of sample size of the hydraulic system and effectively improve the accuracy of fault diagnosis. The proposed combined physical and data-driven digital twin framework can be applied to other hydraulic systems and fields..

The two-factor authentication (2FA) has become pervasive as the mobile devices become prevalent. Existing 2FA solutions usually require some form of user involvement, which could severely affect user experience and bring extra burdens to users. In this work, we propose a secure 2FA that utilizes the individual acoustic fingerprint of the speaker/microphone on enrolled device as the second proof. The main idea behind our system is to use both magnitude and phase fingerprints derived from the frequency response of the enrolled device by emitting acoustic beep signals alternately from both enrolled and login devices and receiving their direct arrivals for 2FA. Given the input microphone samplings, our system designs an arrival time detection scheme to accurately identify the beginning point of the beep signal from the received signal. To achieve a robust authentication, we develop a new distance mitigation scheme to eliminate the impact of transmission distances from the sound propagation model for extracting stable fingerprint in both magnitude and phase domain. Our device authentication component then calculates a weighted correlation value between the device profile and fingerprints extracted from run-time measurements to conduct the device authentication for 2FA. Our experimental results show that our proposed system is accurate and robust to both random impersonation and Man-in-the-middle (MiM) attack across different scenarios and device models.

The social trust assessment can spur extensive applications such as social recommendations, shopping, financial investment strategies, etc, but remain a challenging problem having limited exploration. Such explorations mainly limit their studies to static network topology or simplified dynamic networks, toward the social trust relationship prediction. In contrast, in this paper, we explore the social trust by taking into account the time-varying online social networks whereas the social trust relationship may vary over time. The DTrust, a dynamic graph neural network-based solution, will be proposed for accurate social trust prediction. In particular, DTrust is composed of a static aggregation unit and a dynamic unit, respectively responsible for capturing both the spatial dependence features and temporal dependence features. In the former unit, we stack multiple NNConv layers derived from the edge-conditioned convolution network for capturing the spatial dependence features correlated to the network topology and the observed social relationships. In the latter unit, a gated recurrent unit (GRU) is employed for learning the evolution law of social interaction and social trust relationships. Based on the extracted spatial and temporal features, we then employ a fully connected neural network for learning, able to predict the social trust relationships for both current and future time slots. Extensive experimental results exhibit that our DTrust can outperform the benchmark counterparts on two real-world datasets.

The robot needs to know the pose in order to maintain stability and execute the walking path. Current solutions either rely on visual data, which is easily affected by the environment and lighting conditions, or integrate kinematics and inertial measurement unit (IMU) measurement data, however, there will be drift problems caused by accumulated errors. Aiming at the defects and stability problems of vision sensor in location, this paper combines vision sensor and IMU to complete the high-precision pose estimation at low cost, designs the combined positioning algorithm based on the extended Kalman Filter (EKF). Specifically, this paper proposes the number of correctly matched feature points and depth error as the judgment conditions of the combined strategy, and uses the IMU data to construct a process model, merges the pose estimation results of the vision sensor, and selectively corrects the vision sensor. The robot positioning experiment was carried out in the indoor laboratory scene, results show that the algorithm proposed in this paper can effectively suppress the positioning stability problem of the vision sensor and improve the accuracy of the pose estimation.

In this paper, we research a synthesis scheme for secure wireless communication in the broadcasting multiusers directional modulation system, which consists of multiple legitimate users (LUs) receiving the same confidential messages and multiple eavesdroppers (Eves) intercepting the confidential messages. We propose a new type of array antennas, termed random frequency diverse arrays (RFDA), to enhance the security of confidential messages due to its angle-range dependent beam patterns. Based on RFDA, we put forward a synthesis scheme to achieve multiobjective secure wireless communication. First, with known locations of Eves, the beamforming vector is designed to minimize Eves’ receiving power of confidential message (Min-ERP) while satisfying the power requirement of LUs. Furthermore, we research a more practical scenario, where locations of Eves are unknown. Unlike the scenario of known locations of Eves, the beamforming vector is designed to maximize the sum received power of LUs (Max-LRP) while satisfying a minimum received power constraint at each LU. Second, the artificial-noise projection matrix (ANPM) is calculated to reduce artificial-noise (AN) impact on LUs and enhance the interference on Eves. Numerical results verify the superior secure performance of the proposed schemes in the broadcasting multiusers system.

In this paper, we research synthesis scheme for secure wireless communication in multibeam directional modulation (MBDM) system, which consists of multiple legitimate users (LUs) receiving their own individual confidential messages, respectively, and multiple eavesdroppers (Eves) intercepting confidential messages. We propose a new type of array antennas, termed frequency diverse arrays (FDA), to enhance security of confidential messages. Leveraging FDA technology and artificial noise (AN) technology, we aim to address the PHY security problem for MBDM by jointly optimizing the frequency offsets, the precoding matrix and the AN projection matrix. In the first stage, with known locations of Eves, precoding matrix is designed to minimize Eve’s receiving power of confidential message (Min-ERP), while satisfying power requirement of LUs. And then artificial-noise projection matrix (ANPM) is calculated to enhance AN impact on Eves without influencing LUs. Furthermore, we research a more practical scenario, where locations of Eves are unknown. Unlike the scenario of the known locations of Eves, precoding matrix is designed to maximize AN transmit power (Max-ATP), while satisfying each LU’s requirement received power of confidential message. In the second stage, we analyze and further optimize secrecy capacity. The problem is solved by optimizing frequency offsets through modified artificial bee colony (M-ABC) algorithm. Numerical results show that the proposed scheme can achieve a secure transmission in MBDM system.

Motion sensor equipped smart phones can be operated as the Community Seismic Network for earthquake detection, as the build-in accelerometers share similar principles of those in high precision seismic stations. However, current smart phones often yield poor monitoring quality due to the limited sensing capability of the off-the-shelf sensors and unpredictable dynamics of daily activities of phone users. In this paper, we present a suite of algorithms towards detecting anomalous seismic events from noise contaminated signals, including lightweight signal pre-processing, a two-phase P-phase pickup and timing scheme at the local smartphones, and employing decision fusion to maximize anomaly detection performance at fusion center while meet the requirements on system false alarm rate. We experimentally evaluate the proposed approach on networked smart phones and shake table. The results, including data sampled by smart phones and the numeric simulation, indicate the effectiveness of our scheme in distinguishing anomalous seismic events from motions due to normal daily manipulation.

Wireless sensor networks are typically composed of low-cost sensors that are deeply integrated in physical environments. As a result, the sensing performance of a wireless sensor network is inevitably undermined by biases in imperfect sensor hardware and the noises in data measurements. Although a variety of calibration methods have been proposed to address these issues, they often adopt the device-level approach that becomes intractable for moderate-to large-scale networks. In this article, we propose a two-tier system-level calibration approach for a class of sensor networks that employ data fusion to improve the sensing performance. In the first tier of our calibration approach, each sensor learns its local sensing model from noisy measurements using an online algorithm and only transmits a few model parameters. In the second tier, sensors' local sensing models are then calibrated to a common system sensing model. Our approach fairly distributes computation overhead among sensors and significantly reduces the communication overhead of calibration compared with the device-level approach. Based on this approach, we develop an optimal model calibration scheme that maximizes the target detection probability of a sensor network under bounded false alarm rate. Our approach is evaluated by both experiments on a testbed of TelosB motes and extensive simulations based on synthetic datasets as well as data traces collected in a real vehicle detection experiment. The results demonstrate that our system-level calibration approach can significantly boost the detection performance of sensor networks in scenarios with low signal-to-noise ratios.

Many Cyber-Physical Systems (CPS) are composed of low-cost devices that are deeply integrated with physical environments. As a result, the performance of a CPS system is inevitably undermined by various physical uncertainties , which include stochastic noises, hardware biases, unpredictable environment changes, and dynamics of the physical process of interest. Traditional solutions to these issues (e.g., device calibration and collaborative signal processing) work in an open-loop fashion and hence often fail to adapt to the uncertainties after system deployment. In this article, we propose an adaptive system-level calibration approach for a class of CPS systems whose primary objective is to detect events or targets of interest. Through collaborative data fusion, our calibration approach features a feedback control loop that exploits system heterogeneity to mitigate the impact of aforementioned uncertainties on the system performance. In contrast to existing heuristic-based solutions, our control-theoretical calibration algorithm can ensure provable system stability and convergence. We also develop a routing algorithm for fusion-based multihop CPS systems that is robust to communication unreliability and delay. Our approach is evaluated by both experiments on a testbed of Tmotes as well as extensive simulations based on data traces gathered from a real vehicle detection experiment. The results demonstrate that our calibration algorithm enables a CPS system to maintain the optimal sensing performance in the presence of various system and environmental dynamics.

Mission-critical target detection imposes stringent performance requirements for wireless sensor networks, such as high detection probabilities and low false alarm rates. Data fusion has been shown as an effective technique for improving system detection performance by enabling efficient collaboration among sensors with limited sensing capability. Due to the high cost of network deployment, it is desirable to place sensors at optimal locations to achieve maximum detection performance. However, for sensor networks employing data fusion, optimal sensor placement is a nonlinear and nonconvex optimization problem with prohibitively high computational complexity. In this paper, we present fast sensor placement algorithms based on a probabilistic data fusion model. Simulation results show that our algorithms can meet the desired detection performance with a small number of sensors while achieving up to seven-fold speedup over the optimal algorithm.

Wireless sensor networks (WSNs) are typically composed of low-cost sensors that are deeply integrated with physical environments. As a result, the sensing performance of a WSN is inevitably undermined by various physical uncertainties, which include stochastic sensor noises, unpredictable environment changes and dynamics of the monitored phenomenon. Traditional solutions (e.g., sensor calibration and collaborative signal processing) work in an open-loop fashion and hence fail to adapt to these uncertainties after system deployment. In this paper, we propose an adaptive system-level calibration approach for a class of sensor networks that employ data fusion to improve system sensing performance. Our approach features a feedback control loop that exploits sensor heterogeneity to deal with the aforementioned uncertainties in calibrating system performance. In contrast to existing heuristic based solutions, our control-theoretical calibration algorithm can ensure provable system stability and convergence. We also systematically analyze the impacts of communication reliability and delay, and propose an optimal routing algorithm that minimizes the impact of packet loss on system stability. Our approach is evaluated by both experiments on a testbed of Tmotes as well as extensive simulations based on data traces gathered from a real vehicle detection experiment. The results demonstrate that our calibration algorithm enables a network to maintain the optimal detection performance in the presence of various system and environmental dynamics.

Wireless sensor networks are typically composed of low-cost sensors that are deeply integrated in physical environments. As a result, the sensing performance of a wireless sensor network is inevitably undermined by biases in imperfect sensor hardware and the noises in data measurements. Although a variety of calibration methods have been proposed to address these issues, they often adopt the device-level approach that becomes intractable for moderate- to large-scale networks. In this paper, we propose a two-tier system-level calibration approach for a class of sensor networks that employ data fusion to improve the sensing performance. In the first tier of our calibration approach, each sensor learns its local sensing model from noisy measurements using an online algorithm and only transmits a few model parameters. In the second tier, sensors' local sensing models are then calibrated to a common system sensing model. Our approach fairly distributes computation overhead among sensors and significantly reduces the communication overhead of calibration. Based on this approach, we develop an optimal model calibration scheme that maximizes the target detection probability of a sensor network under bounded false alarm rate. Our approach is evaluated by both experiments on a testbed of TelosB motes and extensive simulations based on data traces collected in a real vehicle detection experiment. The results demonstrate that our system-level calibration approach can significantly boost the detection performance of sensor networks in the scenarios with low signal-to-noise ratios.

Wireless sensor networks (WSNs) deployed for mission-critical applications face the fundamental challenge of meeting stringent spatiotemporal performance requirements using nodes with limited sensing capacity. Although advance network planning and dense node deployment may initially achieve the required performance, they often fail to adapt to the unpredictability and variability of physical reality. This paper explores efficient use of mobile sensors to address limitations of static WSNs for target detection. We propose a data-fusion-based detection model that enables static and mobile sensors to effectively collaborate in target detection. An optimal sensor movement scheduling algorithm is developed to minimize the total moving distance of sensors while achieving a set of spatiotemporal performance requirements including high detection probability, low system false alarm rate, and bounded detection delay. The effectiveness of our approach is validated by extensive simulations based on real data traces collected by 23 sensor nodes.

Sensor deployment is a key issue in target detection. While a lot of works have been done to achieve "physical coverage'', few work addresses meeting high detection accuracy presented as a specific high detection probability and a low false alarm rate. In this paper, we employ a data-fusion based collaborative detection scheme for achieving guaranteed accuracy in sensor deployment. A "density first'' clustering algorithm is adopt to organize preselected surveillance locations into deployment units, place sensors to cover these locations from the intensive region to sparse one. The simulation results show that our deployment algorithm can significantly reduce total number of deployed sensors with guaranteed accuracy compare with the previous works.

Minimizing energy consumption is a key issue in designing real-time applications on wireless embedded systems. While a lot of work has been done to manage energy consumption on single processor real-time system, few work addresses network-wide energy consumption management for real-time tasks. Moreover, existing work on network-wide energy consumption assumes that the underlying network is always connected, which is not consistent with the practice in which wireless nodes often turn off their network interfaces in asleep schedule to reduce energy consumption. In this paper, we propose solutions to minimize network-wide energy consumption for real-time tasks with precedence constraints executing on wireless embedded systems. Our solutions take the radio sleep scheduling of wireless nodes into account when adjusting the execution modes of processors. We also propose a runtime dynamic energy management scheme to further reduce energy consumption while guaranteeing the timing constraint. The experiments show that our approach significantly reduces total energy consumption compared with the previous work.

Mission-critical target detection imposes stringent performance requirements for wireless sensor networks, such as high detection probabilities and low false alarm rates. Data fusion has been shown as an effective technique for improving system detection performance by enabling efficient collaboration among sensors with limited sensing capability. Due to the high cost of network deployment, it is desirable to place sensors at optimal locations to achieve maximum detection performance. However, for sensor networks employing data fusion, optimal sensor placement is a non-linear optimizationproblem with prohibitive computational complexity. In this paper, we present fast sensor placement algorithms based on a probabilistic data fusion model.Simulation results show that our algorithms can meet the desired detection performance with a small number of sensors while achieving up to 7-fold speedup over the optimal algorithm.

This paper is concerned with the global convergence of the solutions of a nonautonomous system with variable delays, arising from the description of the states of neurons in delayed Cohen-Grossberg type in a time-varying situation. By exploring intrinsic features between nonautonomous system and its asymptotic equation, several novel sufficient conditions are established to ensure that all solutions of the networks converge to a periodic function or a constant vector for delayed Cohen-Grossberg-type neural network (NN) models in time-varying situation. The results can be applied directly to group of NNs models including Hopfield NNs, bidirectional association memory NNs, and cellular NNs. Our results are not only presented in terms of system parameters and can be easily verified but also are less restrictive than previously known criteria. Numerical simulations have also been presented to demonstrate the theoretical analysis.