The distributed deep learning architecture between front-deployed sensors and edge-deployed gateways attracts increasing interest. However, the inference performance of distributed deep models is also impacted by the delivery loss of intermediate representation in the wireless link, especially in the harsh industrial fading environments. Traditional communication systems usually focus on transmission errors at bit level, which treat all bits in the packets equally and fail to suit the varying importance in distributed deep models, which urges the essential evolution of the communication method to form a joint co-design paradigm for distributed deep models. This article then proposes to optimize the Mean Time To First Failure (MTTFF) of wireless link instead of traditional bit error rate, which enables a guaranteed transmission window. This paper first derives the analytical model of MTTFF under MIMO systems, then utilizes the kernel mixture distribution to obtain a closed-form solution of MTTFF, which forms a optimization algorithm minimizing the transmitted power while achieving the aiming MTTFF. Extensive reallife experiments show more than 70% satisfaction rate of MTTFF, which leads to more than 10 times higher inference accuracy than the original deep model.

In industrial environments, the wireless link of IoT systems often experiences complex channel fading effects, making accurate online estimation of link quality crucial for improving system performance. Using Gaussian Mixture Model (GMM) to fit I/Q symbols allows estimation of Rician channel parameters, but traditional GMMs typically rely on prior knowledge of the number of Gaussian components to ensure clustering accuracy, posing challenges for adaptive channel modulation schemes in industrial settings. This paper proposes an adaptive Gaussian mixture model based on Kullback-Leibler divergence (KLD), which autonomously determines the optimal number of clusters through iterative evaluation, achieving optimal clustering performance. Firstly, this study proposes the utilization of forward KLD as an optimization target, leveraging its known optimal prior of zero to avoid local optima. Secondly, the redundancy in the number of clusters is assessed using reverse KLD constructed with the single Gaussian distribution. These improvements ensure that the GMM converges correctly to the global optimum regardless of the initial cluster count settings.

Targeting to improve the time synchronization accuracy of multi-hop Time-Sensitive Wireless Sensor Networks (TS-WSNs) for mission-critical industrial automation applications, a Minimum Variance Time Synchronization (MVTS) algorithm utilizing the concept of Packet-Coupled Oscillators (PkCOs) is proposed. This MVTS algorithm utlizes an output feedback approach to mitigate the impact of communication noise on the accumulation of synchronization errors. In addition, a Time-Division Multiple Access (TDMA) packet-exchange superframe is introduced to achieve efficient and low-overhead time synchronization. The optimal gain matrix of the MVTS algorithm is obtained by the Linear Matrix Inequality (LMI) optimization with theoretic analysis. The proposed MVTS algorithm is evaluated by both simulation and experiments on an IEEE 802.15.4 hardware testbed. The experimental results show that the proposed algorithm can effectively reduce the growth rate of clock offset along multi-hop nodes and improve the time synchronization accuracy of the TS-WSNs. Note to Practitioners—This paper explores a method to achieve precise time synchronization in TS-WSNs. The primary challenge being addressed is the accumulation of synchronization errors that occur in multi-hop TS-WSNs, which can compromise the accuracy of time synchronization. To mitigate this challenge while taking communication overhead into account, we propose a solution that combines a TDMA-based packet-exchange superframe structure with the MVTS algorithm. This approach introduces an output feedback consensus control scheme to minimize synchronization error variance. The optimal gain matrix for this consensus control scheme is derived through LMI optimization. The algorithm is implemented on an IEEE 802.15.4-compatable wireless node SAM R21 by Microchip and the experimental results of a 10-hop netowrk shows that the maximum synchronization error is$8.32\mu s$, reduced by 56% and 32%, repsectivly, compared to the baseline method FTSP and the recent PISync.

Early faults, with their small amplitudes and slow variations, are easily masked by noise or process trends, making detection difficult. Also, observer-based methods struggle with timely and accurate early fault detection. To address these challenges, a novel observer-driven temporal graph convolutional network (OD-TGCN) is proposed for early fault detection of control systems subject to semi-stationary periodic input disturbances and measurement noise. We employ graph representation to describe the mechanistic relationships and capture temporal features between variables in the control system. First, graph nodes are constructed based on the control inputs, output signals of the control systems and the fault detection residual of the disturbance decoupling generalized proportional-integral observer (DD-GPIO). Then, a method for constructing the adjacency matrix of the graph based on the system matrix is provided. Finally, the output of the temporal graph convolutional network (TGCN) is passed to a Multi-Layer Perceptron (MLP) for graph-based fault detection. The proposed method is applied to a two-wheeled self-balancing robot. Comparative results show that OD-TGCN significantly outperforms DD-GPIO and typical TGCN in early fault detection accuracy. Additionally, OD-TGCN exhibits notable robustness across datasets with different disturbances/noise.

In this paper, we aim to achieve robust and efficient train rescheduling of intercity express railway lines considering periodic train timetables and passenger uncertainties. Considering intercity express railways’ features of longer section lengths, multiple trains running in close succession within long sections and the varying passenger demands, we improve the multi-train state space model of intercity express railway operation, in which the changes and uncertainties of passenger flows are modeled as system parameter uncertainties and the primary train delays caused by temporary speed restriction extreme weather, and signal failure, etc. are modeled as external interference. Then, a real-time train rescheduling controller is developed that combines iterative learning and model predictive control to enhance its resistance against both the model uncertainties (i.e., varying passenger flows) and the external interference (i.e., the primary delays). The objective function of the rescheduling controller is to recover from delays while preventing the control force amplitude from becoming excessively large. Each period of the periodic timetable is modeled as a batch, and a batch-based state space error predictive model is developed to simultaneously recover the nominal timetable and minimize the control force amplitude. It is proven that the error norm will eventually converge to a bounded value as the number of iterations increases. The performance of the proposed method is evaluated through simulations based on the Beijing–Tianjin intercity express rail line.

In railway transportation systems, train section running times are usually uncertain due to the influence of various complex factors, making rescheduled timetables difficult to use continually, and rescheduling has to be repeated. When acceptable, rescheduled timetables should have a certain degree of robustness against such uncertainty. We use the empirical statistical distribution of the deviation between actual and scheduled train arrival times to capture this uncertainty. To equip timetables with acceptable robustness, a chance-constrained programming model is established for the robust rescheduling problem. We design a two-stage hybrid heuristic algorithm to solve the proposed model. In the first stage, a hybrid intelligent algorithm combining ant colony optimization and Monte Carlo simulations is used to solve the model initially, and bisection method is embedded into it to improve solving efficiency. Then, the chance-constrained programming model is transformed into its deterministic equivalent form to generate a better solution by utilizing the solution result of the hybrid intelligent algorithm. Finally, we test our method on the realistic dataset of Elizabeth Line to verify its effectiveness and robustness. Compared to the deterministic rescheduling method, our algorithm, with a confidence level of 0.6-0.7, reduces the number of rescheduling times by 22.2%-100% while only increasing delays by 0.5%-3.7%. The experimental results demonstrate that we propose an effective and adjustable method to enhance the robustness of timetables, allowing operators to formulate an ideal timetable with acceptable robustness while minimizing adverse effects on punctuality.

Accurate online link quality metrics represented by the Rician parameter are critical to enhancing the reliability of industrial wireless networks subject to temporal fading channels. The Rician parameters can be estimated by fitting the received I/Q symbols with GMM (Gaussian Mixture Model). However, the classical Expectation-Maximization estimations of GMM rely on the preset hyper-parameter of kernel numbers to guarantee the convergence, making it hard to work under adaptive modulation schemes. To address this challenge, we first reveal that the derivative of likelihood is less capable of representing the global optimal, which leads to the well-known local optimal problem and the failure to recognize the false convergence caused by incorrectly configured kernel numbers. A new empirical metric derived from KLD (Kullback-Leibler divergence) has been proposed to identify the local optimal convergence, as well as a new metric tuple to discriminate redundant kernels. A novel estimation algorithm has then been designed to shift the number of kernels from the preset hyper-parameter to the adjustable parameter. This improvement guarantees the global optimal convergence of the GMM with any initial number of kernels. Extensive experiments demonstrate that the proposed method achieves over ten times better accuracy, while requires less than half the iterations.

The distributed deep learning architecture can support the front-deployment of deep learning systems in resource constrained IoT devices and is attracting increasing interest. However, most ready-to-use deep models are designed for centralized deployment without considering the transmission loss of the intermediate representation inside the distributed architecture. This oversight significantly affects the inference performance of distributed deployed deep models. To alleviate this problem, a state-of-the-art work chooses to retrain the original model to form an intermediate representation with ordered importance and yields better inference accuracy under constrained transmission bandwidth. This paper first reveals that this solution is essentially a pruning-like solution, where unimportant information is adaptively pruned to fit within the limited bandwidth. With this understanding, a novel scheme named Naturally Aggregated Intermediate Representation (NAIR) has been proposed, which aims to naturally amplify the difference of importance embedded in the intermediate representation from a mature deep model and reassemble the intermediate representation into a hierarchy of importance from high-to-low to accommodate the transmission loss. As a result, this method shows further improved performance in various scenarios, avoids compromising the overall inference performance of the system, and saves astronomical retraining and storage costs. The effectiveness of NAIR has been validated through extensive experiments, achieving a 112% improvement in performance compared to the state-of-the-art work.

In High-Speed Railway (HSR) networks with hub stations connecting multiple HSR lines, Train Timetable Rescheduling (TTR) under disruptions (such as speed restrictions caused by high wind) has been a challenging problem, which requires collaborative consideration of the traffic and impacts on all lines. Compared to the first principle model of complex railway networks, data-driven modeling provides a better solution to describe how the performance of one HSR line is affected by a train rescheduling decision made for another lines, but it faces the challenges of incompleteness, imbalance and lack of comprehensiveness of history data as disruptions in railways (e.g. delays, accidents) are relatively rare compared to normal operations. This paper proposes a multi-line rescheduling framework consisting of an interactive railway operation simulation and experiment (iROSE) system, a surrogate model and a heuristic algorithm to enable network-wise optimal rescheduling of multiple lines. To compensate for the limits of incomplete history data, a relatively low-cost but accurate enough surrogate model is developed from simulation data of the realistic but computation-intensive iROSE simulator. To reduce the demand for data and the time on running the costly simulator, a multi-surrogate search method is developed. A data expansion-based knowledge transfer method and joint distribution adaptation and tradaboost are also adopted to further improve the accuracy of the surrogate model. Our extensive experiments show that the proposed method can obtain higher precision fine search models with few simulations and solve the problem of TTR under wind-caused speed restrictions in complex railway networks with multiple lines.Note to Practitioners—This paper was motivated by the Train Timetable Rescheduling problem of complex high-speed railway networks of multiple lines connected via hub stations, in which the delays caused by high-wind speed restrictions on one line may easily affect trains on other lines in the network. Thus the impacts of a local-line TTR decision on other parts of the HSR network should be evaluated appropriately in the sense of precision and real-time, to assist the local dispatcher in making a network-wise decision. However, the incomplete and imbalanced historical data may not accurately capture how the system behaves during disruptions. In order to address these challenges, this paper proposes a data-driven rescheduling optimization framework to allow network-wise optimal decision-making. The proposed framework consists of an on-demand iROSE system, a surrogate model representing the operation performance of the whole railway network, and a heuristic method responsible for the traffic rescheduling of partial HSR lines. The realistic iROSE simulator is able to compensate the imbalanced actual history operation data by giving a precise evaluation of the network’s performance. Then a multi-surrogate search method and a knowledge transfer method are developed to avoid the time-consuming caused by expensive simulation. The developed surrogate model is able to capture the insights of delay propagation in a multi-line HSR network and enable the dispatchers to have a quick and comprehensive evaluation of how a TTR rescheduling decision made for one line affects other lines in the network. As a result, a network-wise better decision on train rescheduling can be made.

With the rapid development of advanced sensing, communication, and the industrial Internet of Things, it has become much easier to obtain, transmit, and, store a massive amount of real-world data. However, imperfect data is inevitable in real-world systems, such as the existence of outliers, contaminated, incomplete, inaccurate, and even missing information in the data. This phenomenon is called data imperfection, which usually makes traditional datadriven modeling and automation methods either unfeasible or ending at undesired inaccuracies. This has been a wellknown challenge to data-driven methods when applied to real-world systems, such as process industry, manufacturing, energy networks, and transportation systems.

This article investigates the detection and identification of physical faults in devices and false-data-injection attacks in communication networks for multi-agent systems with event-triggered transmission mechanisms and subject to external periodic disturbances. First, a new detection and identification scheme, including a local disturbance decoupling (LDD) observer and a distributed disturbance decoupling (DDD) observer, is proposed. Then, based on zero-assignment and the rank-deficiency of the transfer function matrix at zeros, a co-design method for the LDD observer and DDD observer is proposed, which enables the decoupling of periodic disturbances from the residuals for detection and identification. This new scheme no longer requires the transmission of control signals from the node being monitored or the exchange of information between its neighbors, significantly reducing the communication overhead and enhancing the system's security. Finally, a simulation based on a multi-two-wheeled trolley system is used to verify the effectiveness of the proposed method.

In intercity high-speed railways (HSR) with high speeds and dense traffic, fast decision-making in timetable rescheduling is critical to minimize delays and maintain regular services during disruptions. Different from traditional mathematical programming methods, which are often too computation-intensive for real-time implementation, this paper develops a state-space dynamic model of train traffic with the extension to accommodate multiple trains in the sections between stations. Variations of the state space model are established for scenarios of mild delays and severe delays, respectively. Two automatic rescheduling state feedback controllers are designed to achieve two objectives, to restore the nominal timetable in case of mild delays and to retain regular departure intervals in case of severe delays, respectively. Stability analysis theoretically proves the stability and convergence of the proposed feedback controller and real-time rescheduling algorithm. The proposed rescheduling method indeed is a real-time state feedback controller, and the simulation results of the Beijing-Tianjin intercity HSR show that the proposed method features negligible computation times in the order of microseconds, in contrast to the 56s and 65s required by conventional Mixed-Integer Programming (MIP) for nominal timetable recovery and regular departure interval problems, respectively. The proposed state-space feedback control rescheduling method is quasi-optimal compared to MIP with the added advantage of greater computational efficiency and fast decision-making.

Train Timetable Rescheduling (TTR) is a crucial task in the daily operation of high-speed railways to maintain punctuality and efficiency in the presence of unexpected disturbances. However, it is challenging to promptly create a rescheduled timetable in real time. In this study, we propose a reinforcement-learning-based method for real-time rescheduling of high-speed trains. The key innovation of the proposed method is to learn a well-generalized dispatching policy from a large amount of samples, which can be applied to the TTR task directly. At first, the problem is transformed into a multi-stage decision process, and the decision agent is designed to predict dispatching rules. To enhance the training efficiency, we generate a small yet good-quality action set to reduce invalid explorations. Besides, we propose an action sampling strategy for action selection, which implements forward planning with consideration of evaluation uncertainty, thus improving search efficiency. Extensive experimental results demonstrate the effectiveness and competitiveness of the proposed method. It has been proven that the local policies trained by the proposed method can be applied to numerous problem instances directly, rendering it unnecessary to use human-designed rules.

Train timetable rescheduling (TTR) aims to promptly restore the original operation of trains after unexpected disturbances or disruptions. Currently, this work is still done manually by train dispatchers, which is challenging to maintain performance under various problem instances. To mitigate this issue, this study proposes a reinforcement learning-based approach to TTR, which makes the following contributions compared to existing work. First, we design a simple directed graph to represent the TTR problem, enabling the automatic extraction of informative states through graph neural networks. Second, we reformulate the construction process of TTR’s solution, not only decoupling the decision model from the problem size but also ensuring the generated scheme’s feasibility. Third, we design a learning curriculum for our model to handle the scenarios with different levels of delay. Finally, a simple local search method is proposed to assist the learned decision model, which can significantly improve solution quality with little additional computation cost, further enhancing the practical value of our method. Extensive experimental results demonstrate the effectiveness of our method. The learned decision model can achieve better performance for various problems with varying degrees of train delay and different scales when compared to handcrafted rules and state-of-the-art solvers.

Precise timing over timestamped packet-exchange communication is an enabling technology in the mission-critical industrial Internet of Things (IIoT), particularly when satellite-based timing is unavailable. The main challenge is to ensure timing accuracy when the clock synchronization system is subject to disturbances caused by the drifting frequency, time-varying delay, jitter, and timestamping uncertainty. In this work, a robust packet-coupled oscillators (R-PkCOs) protocol is proposed to reduce the effects of perturbations manifested in the drifting clock, timestamping uncertainty, and delays. First, in the spanning-tree clock topology, time synchronization between an arbitrary pair of clocks is modeled as a state-space model, where clock states are coupled with each other by one-way timestamped packet exchange (referred to as packet coupling), and the impacts of both drifting frequency and delays are modeled as disturbances. A static output controller is adopted to adjust the drifting clock. The$H_{\infty }$robust control design solution is proposed to guarantee that the ratio between the modulus of synchronization precision and the magnitude of the disturbances are always less than a given value. Therefore, the proposed time synchronization protocol is robust against the disturbances, which means that the impacts of drifting frequency and delays on the synchronization accuracy are limited. The one-hour experimental results demonstrate that the proposed R-PkCO’s protocol can realize time synchronization with the precision of 6$\mu \text{s}$in a 21-node IEEE 802.15.4 network. This work has widespread impacts in the process automation of automotive, mining, oil, and gas industries.

The front deployed deep learning is a promising technology of the next generation industrial applications, which can extract essential information from high dimension sensors. However, part of these heavy computation tasks at resource constrained front devices have to be offloaded to the edge or cloud devices, which forms the cooperative deep learning system through the exchange of intermediate data. The inference efficiency of cooperative deep learning system will then be highly correlated with the communication latency caused by the non-stationary industrial multipath-rich fading channel. This paper proposes a novel method to control the distribution of communications latency, which is able to support efficient cooperative deep learning architecture in the harsh industrial environment. The proposed method is essentially an inverse process of Gaussian Mixture Model (GMM), which adjusts latency samples to approach the given arbitrary shape function. To achieve this objective, a new variation of Expectation-Maximization (EM) algorithm in analytical domain is derived to decompose arbitrary distribution shape with multiple Gaussian kernels and an optimized stochastic resource allocation algorithm is proposed to approximate each Gaussian kernels. The performance of proposed method is verified by both classical Rician channel model and field measured industrial fading channel responses.

To enhance the reliability of digital automation systems in the Industry 4.0 era, this paper investigates a robust fault-tolerant control scheme in the discrete-time domain subject to periodic disturbances, consisting of a fault estimator, dynamic disturbance compensation loop, and fault-tolerant controller. The fault estimator simultaneously estimates both the system states and actuator/sensor faults. The existence and stability conditions of the proposed estimator are given, and a robust design method is proposed to make the state estimates robust to disturbances. To further reduce the estimation errors caused by periodic disturbances, a novel disturbance compensation loop is introduced and is optimized by a joint zero-assignment and pole-optimization method to delicately compensate for the adverse impacts of periodic input disturbances. The proposed robust fault-tolerant controller uses fault estimation to ensure fast recovery in the event of bounded actuator/sensor faults. The proposed scheme is evaluated through simulations of a two-wheeled mobile robot subject to periodic disturbances and simultaneous abrupt inclination angular sensor and ramp actuator faults, where its performance is shown to exceed that of existing methods.

Time-sensitive wireless applications have strict requirements on real-time data transmission and control operation. Even though time synchronization has been extensively studied for providing a common timing among distributed wireless nodes, there still exists a lack of research for low-accuracy and large-drifting clocks, such as internal Resistor-Capacitor (RC) oscillator clocks with around$4\times 10^{5}$parts per million (ppm) frequency drift, which are widely used in wearable sensor systems. This paper proposes a Proportional Packet-Coupled Oscillators (P-PkCOs) protocol for synchronizing poor-performing internal RC oscillator clocks with high disturbances in the single-cluster wireless network. The behaviour of such a drifting clock is described by a non-identical and time-varying model. To achieve time synchronization on low-accuracy internal RC oscillator clocks, a packet-coupled synchronization scheme is proposed for adjusting drifting clocks via the proportional control-based correction scheme. The RC oscillator frequency in an embedded system cannot be corrected, and this work utilizes the clock threshold adjustment as a substitute for frequency correction. The stability region of controller parameters is given to guarantee that the clock threshold approaches a value, which is jointly determined by the nominal threshold and the corresponding clock frequency. We also propose a linear matrix inequality condition to prove that the P-PkCOs performance is robust against the large clock disturbances. We demonstrate the implementation of P-PkCOs. The experimental results show that P-PkCOs can achieve and maintain robust time synchronization on the internal RC oscillator clocks.

The wireless-enabled Industrial Internet of Things (IIoT) system is promising due to its flexibility and cable-free deployment. The varying fading channel will lead to the random transmission delays and jitters, which are the major challenges hindering the adoption of wireless communication in mission-critical industrial systems. The performance or even the stability of closed-loop feedback control system will degrade severely with such delays and jitters. As a result, the maximum delay margin should be met to guarantee the performance of the IIoT system. Aiming to better satisfy this requirement, a novel concept to shape the delay distribution under the industrial multipath fading channel is proposed in this article. Consequently, a two-layer closed feedback control algorithm, referred as TACAN in this article, is designed through the decoupling of original optimization function, by which the variance of the delay distribution is minimized to improve the reliability and stability of the IIoT systems. The performance of proposed delay shape control method is verified by both the classical Rician channel model and the field measured industrial fading channel responses.

Blast waves with a large amount of energy, from the use of explosive weapons, is a major cause of traumatic brain injury in armed and security forces. The monitoring of blast waves is required for defence and civil applications. The utilization of wireless sensing technology to monitor blast waves has shown great advantages, such as easy deployment and flexibility. However, due to drifting embedded clock frequency, the establishment of a common timescale among distributed blast monitoring sensors has been a challenge, which may lead to a network failing to estimate the precise acoustic source location. This work adopts a packet-coupled oscillators (PkCOs) protocol to synchronize drifting clocks in a wireless blast wave monitoring network. In order to address packet collisions caused by the concurrent transmission, an anti-phase synchronization solution is utilized to maintain clock synchronization, and the corresponding superframe structure is developed to allow the hybrid transmission of theSyncpacket and the blast wave monitoring data. As a network scales up and the hop distance grows, the packet exchange lag increases during a superframe. This, along with the drifting clock frequency, leads to the degradation of synchronization performance while the clock frequency is usually assumed to be zero and nondrifting. Thus, a compensation strategy is proposed to eliminate the joint impacts and to improve synchronization precision. The theoretical performance analysis of the PkCOs algorithm in the network is presented along with verification by simulation means. Finally, the performance of the PkCOs synchronization protocol is evaluated on an IEEE 802.15.4 hardware testbed. The experimental results show that the PkCOs algorithm provides an alternative clock synchronization solution for blast wave monitoring networks.

Wireless networks have been widely utilized in industries, where wireless links are challenged by the severe nonstationary Rician fading channel, which requires online link quality estimation to support high-quality wireless services. However, most traditional Rician estimation approaches are designed for channel measurements and work only with nonmodulated symbols. Then, the online Rician estimation usually requiresa prioriaiding pilots or known modulation order to cancel the modulation interference. This article proposes a nondata-aided method with redundant Gaussian mixture model (GMM). The convergence paradigm of GMM with redundant subcomponents has been analyzed, guided by which the redundant subcomponents can be iteratively discriminated to approach the global optimization. By further adopting the constellation constraint, the probability to identify the redundant subcomponent is significantly increased. As a result, accurate estimation of the Rician parameters can be achieved without additional overhead. Experiments illustrate not only the feasibility but also the near-optimal accuracy.

Owing to its unique, concealment and easy customization by combining different wrist and hand gestures, high-density surface electromyogram (HD-sEMG) is recognized as a potential solution to the next generation biometric authentication, which usually adopts a wireless body sensor network (BSN) to acquire the multi-channel HD-sEMG biosignals from distributed electrode arrays. For more accurate and reliable classification, biometric authentication requires the distributed biosignals to be sampled simultaneously and be well aligned, which means that the sampling jitters among the arrays need to be tiny. To synchronize data sampling clocks of a cluster of BSN nodes for biometric authentication, this article modifies the packet-coupled oscillators protocol by using a dynamic controller (D-PkCOs). This protocol only involves one-way single packet exchange, which reduces the communication overhead significantly. For the purpose of maintaining precise sampling of these BSN nodes subject to drifting clock frequency and varying delays, the dynamic controller is designed via the$H_\infty$robust method, and it is proved that all the BSN nodes’ sampling jitters are bounded. The experimental results demonstrate that the D-PkCOs protocol can keep the sampling jitters less than a microsecond in a 10-node IEEE 802.15.4 network. The application of D-PkCOs to the BSN shows that the HD-sEMG signal with a high signal-to-noise ratio is obtained, which leads to better gesture classification performance.

Railway system performances depend on effective dynamic scheduling and train operation control. The fast expansion and increasing complexity of high-speed railway (HSR) networks raise new challenges in maintaining the punctuality and efficiency in daily operations, in particular, in the event of disruption. This paper aims to review the state-of-art in dynamic traffic scheduling, trains operation control, and their integration for safer, more punctuate, efficient, and resilient HSRs, whose origins may trace back to their counterparts in traditional railways. First, the existing two-tier hierarchy of scheduling and control in HSR’s daily operation is introduced. At the higher layer of scheduling, a general model of dynamic train scheduling is discussed, followed by reviewing the scheduling methodologies. At the lower layer of train operation control, recent progress in tracking control of high-speed trains is discussed, with focus on the latest advances in single train control and cooperative control for multiple trains. Then, as the trend of technological progress for future HSRs, the recent development of integrating dynamic scheduling and operation control is introduced, which is made possible by efficient information exchanges among the scheduling subsystem and the train control subsystem. A three-layer integration framework and associated co-optimization methodologies are presented by introducing a co-optimization layer that bridges the separated scheduling and control layers. Finally, this review is concluded with discussions on open questions and possible directions for future research.

This article proposes an improved incremental random vector functional-link network (RVFL) with a compact structure and presents its application to quality prediction of blast furnace (BF) ironmaking processes. Different from the original RVFL, the improved incremental RVFL has no input-output direct links and no output bias (NLNB) and approaches the desired network output by sequentially updating the output weights of fixed hidden neurons. Moreover, it is proved that this improved incremental RVFL with NLNB (I-I-RVFL-NLNB) is able to approximate a given continuous function with arbitrary small errors with fewer hidden neurons. As a result, the developed network has more compact requiring less execution time, while retaining the conventional incremental RVFL's advantage of monotonically decreasing errors and avoiding the overfitting issue of the basic RVFL-NLNB. Since the zero approximation error is not needed in practical applications, the terminal condition of the existing incremental RVFL is improved by using the difference of root mean squared error (RMSE) between two consecutive iterations as one of the indices to characterize the imperceptible descending trend of RMSE. A series of comparisons are made by both benchmark simulations and a real quality modeling problem for a large BF ironmaking plant in South China, which show that the proposed algorithm has better performance in terms of modeling accuracy and efficiency.

As a key enabler in the green transport system, the popularity of Electric Vehicles (EV) has attracted attention from academia and industrial communities. However, the driving range of EVs is inevitably affected by the insufficient battery volume, as such EV drivers may experience trip discomfort due to a long battery charging time (under traditional plug-in charging service). One feasible alternative to accelerate the service time to feed electricity is the battery switch technology, by cycling switchable (fully-recharged) batteries at Battery Switch Stations (BSSs) to replace the depleted batteries from incoming EVs. Along with recent advance of vehicle cooperation through emerging Information Communication Technology (ICT), in this paper we propose a Mobile Edge Computing (MEC) driven architecture to gear the intelligent battery switch service management for EVs. Here, the decision making on where to switch battery is operated by EVs in a distributed manner. Besides, the Vehicle-to-Vehicle (V2V) communication in line with public transportation bus system is applied to operate flexible information exchange between EVs and BSSs. Dedicated MEC functions are positioned for bus system to efficiently disseminate BSSs status and aggregate EVs’ reservations, concerning the massive signalling exchange cost. The Global Controller (GC) is positioned as cloud server to gather BSSs (service providers) status and EVs’ reservations (clients), and predict the service availability of BSS (e.g., whether/when a battery can be switched). We conduct performance evaluation to show the advantage of MEC system in terms of reduction of communication cost, and BSS service management scheme regarding reduction of service waiting time (e.g., how long to wait for battery switch) and increase of service satisfaction rate (e.g., how many batteries to switch for EVs).

In railway operation, train delays may occur due to various reasons (e.g. severe weather, infrastructure failure, human factors, etc.) and may change and spread rapidly to subsequent trains. Prediction of train delay changes and propagation is important to provide decision-making support for railway dispatchers to reschedule. In this paper, a multi-stage intelligent method is proposed for predicting the dynamic changes and propagation of train delays using random vector functional-link networks (RVFLNs) with improved transfer learning and ensemble learning. First, to improve the prediction performance of the single RVFLNs model, a novel improved Stacking ensemble learning RVFLNs (SRN) regression algorithm is proposed for prediction modeling of the delay changes. Then, to ensure the classification accuracy of unlabeled and class-imbalanced train data, an improved transfer learning RVFLNs (ITRN) classifier is proposed to decide whether the initial delay will lead to associated delays, with the aid of the improved SMOTE algorithm for processing imbalanced data. If associated delays are identified by the ITRN classifier, the proposed SRN algorithm will be further to predict the subsequent associated delays. By iterating the classification and prediction procedures, the propagation range and chain of train delays can be obtained. The effectiveness and practicability of the proposed method are verified by using two experiments against actual train graph data from different railway lines.

In blast wave monitoring, a traditional travel time tomography method is encountered with local minimum travel time and low coverage density of rays. In this article, a novel B-spline fitting method with the knot-optimization artificial immune system (AIS) is proposed for 2-D overpressure reconstruction. It possesses the advantages of handling point sets of large sizes and adjusts the knot vector flexibly. Based on the overpressure value in the explosion from the travel time tomography method, the proposed method combining the advantages of B-splines and knot point optimization AIS is able to achieve the optimal sensor distribution and raise the reconstruction precision. The detailed experimental results about the comparison of linear fitting interpolation, cubic fitting interpolation, natural neighbor fitting interpolation, v4 fitting interpolation, Delaunay triangulation fitting, and B-spline method are also given. Furthermore, for the knot optimization issue in B-spline, the proposed adaptive fitting method with knot-optimization AIS has a smaller root-mean-square (RMS) error with eight knot nodes in comparison with the classic B-spline fitting method. This article is conducted to provide new insights to reconstructing 2-D Internet-of-Things-based (IoT-based) overpressure in blast wave monitoring more precisely under limited sensor deployment and further give a new approach to overpressure reconstruction scenarios.

Obtaining accurate point prediction of industrial processes' key variables is challenging due to the outliers and noise that are common in industrial data. Hence the prediction intervals (PIs) have been widely adopted to quantify the uncertainty related to the point prediction. In order to improve the prediction accuracy and quantify the level of uncertainty associated with the point prediction, this article estimates the PIs by using ensemble stochastic configuration networks (SCNs) and bootstrap method. The estimated PIs can guarantee both the modeling stability and computational efficiency. To encourage the cooperation among the base SCNs and improve the robustness of the ensemble SCNs when the training data are contaminated with noise and outliers, a simultaneous robust training method of the ensemble SCNs is developed based on the Bayesian ridge regression and M-estimate. Moreover, the hyperparameters of the assumed distributions over noise and output weights of the ensemble SCNs are estimated by the expectation-maximization (EM) algorithm, which can result in the optimal PIs and better prediction accuracy. Finally, the performance of the proposed approach is evaluated on three benchmark data sets and a real-world data set collected from a refinery. The experimental results demonstrate that the proposed approach exhibits better performance in terms of the quality of PIs, prediction accuracy, and robustness.

Aiming at the problem that the downlink channel estimation performance is limited due to the fast time-varying and non-stationary characteristics in the high-speed mobile scenarios, we propose a channel estimation network based on deep learning, called ChanEstNet. ChanEstNet uses the convolutional neural network (CNN) to extract channel response feature vectors and recurrent neural network (RNN) for channel estimation. We use a large amount of high-speed channel data to conduct offline training for the learning network, fully exploit the channel information in the training sample, make it learn the characteristics of fast time-varying and non-stationary channels, and better track the features of channels changing in high-speed environments. The simulation results show that in the high-speed mobile scenarios, compared with the traditional methods, the proposed channel estimation method has low computational complexity and significant performance improvement.

In this paper, a concise two-state clock model is proposed to describe the behaviour of drifting internal oscillator clocks with non-identical and time-varying frequencies, which provides a simple analytical solution of different coupling schemes' effects on synchronisation performance. The improved Pulse-Coupled Oscillators (PCO) scheme presented in this paper guarantees the synchronisation on non-identical and time-varying clocks. Meanwhile, it differs from classical PCO by scheduling the transmission of Syncs (i.e., the Pulses of clock) at different time slots. This collision-free pulse coupling scheme makes the concept of PCO applicable to wireless sensor networks, as it is practically impossible in realistic wireless networks for multiple oscillators to transmit Pulses at the same time. Furthermore, a proportional controller is adopted to correct the clock, and convergence and stability of the proposed algorithm are theoretically analysed, proved and evaluated by hardware experiments. The experimental results show that the improved PCO scheme can achieve synchronisation with the precision of around 400 μs on internal oscillator clocks of about 350, 000 ppm.

Motion tracking technologies have been widely used in mobile interaction applications, such as Virtual Reality (VR), healthy monitoring, and virtual touch control. Compared with dedicated hardware devices, mobile phones use reliable speakers and microphones, and can serve as ubiquitous devices for cheap acoustic-based motion tracking solutions. However, for complex indoor environments, it is very difficult for acoustic-based methods to achieve accurate motion tracking due to multipath fading and limited sampling rate at mobile devices. In this paper, a new parameter named Multipath Effect Ratio (MER) is defined to indicate the multipath fading effect on received signals at different frequencies. Based on MER, a novel multipath effect mitigating technique is developed to calculate the phase change of acoustic signals and track the corresponding moving distance by using multiple speakers. A Phase-based Acoustic Motion Tracking (PAMT) method is then proposed and implemented on standard Android smartphones. Experiment results show, without any specialized hardware, PAMT can achieve an impressive millimeter-level accuracy for localization and motion tracking applications in multipath fading environments. Specifically, the measurement errors are less than 2mm and 4mm in one-dimensional and two-dimensional scenarios, respectively.

Traditional wired monitoring systems exhibit huge limitations in blast wave monitoring. To meet the requirements of long range, low cost, weight reduction, increased ease of installation maintenance, and big-data transmission in blast wave monitoring, a new distributed linear-spatial-array (D-LSA) sensing system based on low-power wide-area network (LPWAN) is proposed in this paper. This approach adopts a multichannel LoRa and NB-IoT air-blast gateway (M-CLNAG) and multiple FPGA-based wireless pressure LoRa nodes (FWPLNs) to construct a large-scale LPWAN for blast wave monitoring. The empirical models of dynamic parameter calculation (peak overpressure, duration of the positive phase and impulse) on the basis of D-LSA sensing system are redesigned for blast wave monitoring as well. Furthermore, we have evaluated the errors between the measured data from D-LSA sensing system and data from the redesigned empirical models. Finally, the wireless quality performance in terms of received signal strength indication (RSSI) and packet receive rate (PDR) for blast wave monitoring is also verified. This paper is conducted to provide new insights into how a sensing system integrating with LPWAN is designed in blast wave monitoring for acquiring dynamic parameters accurately and carrying out remote network communication efficiently, and further opening a door for wireless sensor network (WSN) in more blast wave monitoring scenarios.

Motion tracking is attractive in what concerns a smart city environment, where citizens have to interact with Internet of Things (IoT) infrastructures spread all around one particular city. Motion tracking is important for smart services and location-based services in smart cities, since it provides natural ways for users to interact with the IoT infrastructures, such as the ability to recognize of a wide range of hand motion in real-time. Compared with dedicated hardware devices, ubiquitous devices with reliable speakers and microphones can be developed to achieve cheap acoustic-based motion tracking, which is appropriate for low-power and low-cost IoT applications. However, for complex urban environments, it is very difficult for acoustic-based methods to achieve accurate motion tracking due to multipath fading and limited sampling rate at mobile devices. In this paper, a new parameter called multipath dispersion vector (MDV) is proposed to estimate and mitigate the impact of multipath fading on received signals using extreme learning machine. Based on MDV, a robust acoustic motion tracking (RAMTEL) method is proposed to calculate the moving distance based on the phase change of acoustic signals, and track the corresponding motion in 2-D plane by using multiple speakers. The method is then proposed and implemented on standard Android smartphones. Experiment results show, without any specialized hardware, RAMTEL can achieve an impressive millimeter-level accuracy for localization and motion tracking applications in multipath fading environments. Specifically, the measurement errors are less than 2 and 4 mm in 1-D and 2-D scenarios, respectively.

Through offloading the computing tasks of the task nodes (TNs) to the fog nodes (FNs) located at the network edge, the fog network is expected to address the unacceptable processing delay and heavy link burden existed in current cloud-based networks. Unlike most existing researches based on the command-mode offloading and full capability report, this paper develops a general analytical model of the task scheduling among voluntary nodes (VNs) in fog networks, wherein the VNs voluntarily contribute their capabilities for serving their neighboring TNs. A novel delay-optimal task scheduling (DOTS) algorithm is proposed to obtain the delay-optimal offloading solution according to the reported capabilities of the VNs. Extensive simulations are carried out in a fog network, and the numerical results indicate that the proposed DOTS algorithm can effectively provide the optimal set of the helper nodes, subtask sizes, and the TN transmission power to minimize the overall task processing delay. Moreover, compared with the command-mode offloading, the voluntary-mode achieves more balanced offloading and a higher fairness level among the FNs.

Wireless networks attract increasing interests from a variety of industry communities. However, the wide applications of wireless industrial networks are still challenged by unreliable services due to severe multipath fading effects. Such effects are not only caused by massive metal surfaces but also moving operators and logistical vehicles, which will lead to temporal fading effects. A three-layer impulse response framework is proposed to characterize such effects, in which both the specular and scattered components vary with the spacial movement of nearby objects. In this context, a received signal strength indicator will be a noisy estimation only on the specular power and fail to describe the link quality accurately without the aid of scattered power. Consequently, an augmented Kalman-filter-based link quality estimator has been designed to track both the specular and scattered power in the distribution parameter space with constant noise covariance matrices. Experiments from industrial sites show significantly increased accuracy.

As a key enabling technology in mission-critical Wireless Sensor Networks (WSNs), time synchronization provides a common timescale for distributed sensor nodes in many wireless applications, such as coordinated control and underwater navigation and tactical surveillance. Inspired by the behaviour of fireflies, along with mathematical model, Pulse-Coupled Oscillators (PCO), has been proposed to enable synchronization in complex networks, where all the PCO's firing signal Pulses are broadcasted simultaneously when synchronization is achieved. The requirement of zero-drift clock oscillators, fully-connected network and concurrent transmission of Pulses are, in reality, impossible to achieve. To avoid transmission collision and enable the PCO extension in the multi-hop WSNs, the desynchronization mechanism is adopted to enable the Pulse packets to be transmitted to the wireless channel in a uniformly distributed fashion. Due to the contention-free period's feature of low-latency, thereby avoiding the need to wait for a random and potentially long period until the channel is available, the PCO's Pulse packets are transmitted in the contention-free period of IEEE 802.15.4-2015 superframe. Thus, a novel state-space model for desynchronization-based pulse-coupled nonidentical oscillators is proposed to model a realistic drifting clock oscillator. Moreover, the timestamped Pulse packets are transmitted to determine the offset of connected sensor nodes, and an attenuated clock correction scheme is adopted to correct the local drifting clocks by using measured offset and skew. The intensive simulations of the three-hop three-cluster wireless network and the seven-hop linear network have been carried out to evaluate performance of timestamped PCO with desyn-chronization method.

Time synchronization in wireless sensor networks,aiming to provide a common sense of timing among distributed sensor nodes, is a key enabling technology for many applications, such as collaborative condition monitoring, time-of-flight localization and underwater navigation and tactical surveillance. In order to solve the challenges of the manufacturing tolerance and working condition variations in any real-world environments, a novel state-space model for pulse-coupled non-identical oscillators is proposed to model a realistic clock oscillator with nonidentical and time-varying frequency. A state feedback correction, referred to as hybrid coupling mechanism, is also proposed to ensure the system move into steady state, thus achieving time synchronization in wireless sensor networks. Furthermore, the intensive simulations of single-hop wireless sensor networks have been carried out to evaluate the performance of proposed pulsecoupled non-identical oscillators. It is shown that a partially connected wireless network consisting of 50 non-identical pulsecoupled oscillators can achieve the synchronization with the precision of $40us.$

In traditional airplane monitoring system (AMS), data sensed from strain, vibration, ultrasound of structures or temperature, and humidity in cabin environment are transmitted to central data repository via wires. However, drawbacks still exist in wired AMS such as expensive installation and maintenance, and complicated wired connections. In recent years, accumulating interest has been drawn to performing AMS via airborne wireless sensor network (AWSN) system with the advantages of flexibility, low cost, and easy deployment. In this review, we present an overview of AMS and AWSN and demonstrate the requirements of AWSN for AMS particularly. Furthermore, existing wireless hardware prototypes and network communication schemes of AWSN are investigated according to these requirements. This paper will improve the understanding of how the AWSN design under AMS acquires sensor data accurately and carries out network communication efficiently, providing insights into prognostics and health management (PHM) for AMS in future.

Non-stationarity is a key feature of wireless channels in high-mobility environments, such as high speed railway, which is one of the barriers on improving the communication quality if not being well treated in channel estimation. Traditional Kalman Filter (KF)- based channel estimation methods that assume constant time correlation coefficients based on Jakes model are no longer valid in non-stationary channel. To address the challenges of channel estimation in high-mobility OFDM communications, a joint design of channel estimation, detection and decoding is proposed on the basis of Extended Kalman Filter (EKF) and Iterative Detector & Decoder (IDD) technology in this paper. The dynamics of non-stationary channel is characterized as a time-varying autoregressive (AR) process and a Decision-directed EKF is developed to track both the channel frequency response (CFR) and the time correlation coefficients of the time-varying AR process. In order to mitigate the error propagation with Decision-directed at data symbols, the IDD is adopted and integrated with the EKF (IDD-EKF). And, instead of Decision-directed, this paper proposes a novel log-likelihood ratio (LLR)-directed mechanism, in which the a posteriori LLRs are utilized to update the weighting matrix of EKF. The simulation results of estimating a typical non-stationary WINNER-II channel show that, compared with traditional methods, the proposed method effectively overcomes the error propagation problems and improves the channel estimation accuracy. The overall system performance in terms of signal-to-noise ratio (SNR) gain is about 3dB at 200km/h.

Fault detection (FD) is the process of monitoring a system to identify any malfunction occurring in the system. In model based fault detection, a state estimator or observer is usually designed in order to provide an estimated output which is compared with the measured output and evaluated with a decision making algorithm to identify a fault. In this paper, a new approach for fault detection is proposed based on the design of an output observer, instead of a state observer, using an input/output system representation. The proposed output observer is employed to estimate the output of the system only in order to generate a set of residuals that are indicative of the presence of a fault. The convergence of the observer is proven for any initial condition and a fault detectability condition is set. This FD scheme is validated by simulating a model of a three wheels robot.

Wireless Sensor Actuator Networks (WSANs) have shown its advantages of flexibility and cable-free deployment in industrial applications. However, the sensors explore directly to the harsh environment and are the most prone to faults and malfunctioning, which poses a big challenge to the WSAN's applicability in applications with high safety requirements. This paper focuses on highly reliable WSANs equipped with double-sensing system and proposes a model-based on-line sensor fault detection scheme by exploiting information redundancy between sensors and actuators. An optimization method is developed to make the proposed scheme robust to the largely fluctuating end-to-end packet transmission delays due to wireless communications. The performance of the proposed sensor fault detection scheme is demonstrated by the simulation of a pick and place robot.

ABSTRACT Emerging wireless sensor/actuator network (WSAN) technology has the potential to enable semi‐autonomous airflow control to improve the aerodynamic performance of aircraft. In this paper, a WSAN comprising of multiple linear sensor clusters terminated by actuators is proposed for active airflow control with the objective of minimizing convergecast latency. Here, the convergecast delay is defined as the time required from the beginning of a sampling period to all all sensor's data of this sampling period is received by the actuator. The objective is achieved by minimizing the separation distance of concurrent data transmission so that the number of nodes sending data in the same time slot is maximized. The problem turns into a scheduling problem with a proper selection of interference separation. However, most existing work on the scheduling in linear networks use the minimum separation of two hops to avoid collisions. This paper examines the relationship between the hop separation, signal‐to‐noise ratio, and the latency to make a selection of interference separation. A new interference aware hybrid line scheduling (HLS) algorithm is proposed and its energy consumption is analyzed. Compared with other line scheduling policies, the analysis and simulation results show that, at moderately high node densities, the proposed HLS with carefully selected hop separation is able to reduce both the delay by up to 15% and the energy consumption somehow. Copyright © 2012 John Wiley & Sons, Ltd.

This paper demonstrates the possibility of increasing energy performance in Wireless Sensor Networks by the adaptation of spreading code length. Through the experimental approach, it is shown that a WSN device can achieve higher data rate with adaptive spreading code length, which can increase the throughput and reduce energy usage. Based on our findings from experiment, we propose to actively adjust the transmission power to enable higher transmission rate, which will significantly decrease the device active time. As a result, the overall energy efficiency can be increased. Such scheme has been validated using simulation, which shows that the network life time can be increased by 36%.

In many Wireless Sensor Network (WSN) applications, the availability of a simple yet accurate estimation of the RF channel quality is vital. However, due to measurement noise and fading effects, it is usually estimated through probe or learning based methods, which result in high energy consumption or high overheads. We propose to make use of information redundancy among indicators provided by the IEEE 802.15.4 system to improve the estimation of the link quality. A Kalman filter based solution is used due to its ability to give an accurate estimate of the un-measurable states of a dynamic system subject to observation noise. In this paper we present an empirical study showing that an improved indicator, termed Effective-SNR, can be produced by combining Signal to Noise Ratio (SNR) and Link Quality Indicator (LQI) with minimal additional overhead. The estimation accuracy is further improved through the use of Kalman filtering techniques. Finally, experimental results demonstrate that the proposed algorithm can be implemented on resource constraints devices typical in WSNs.

This review paper is to give a full picture of fault detection and diagnosis (FDD) in complex systems from the perspective of data processing. As a matter of fact, an FDD system is a data-processing system on the basis of information redundancy, in which the data and human's understanding of the data are two fundamental elements. Human's understanding may be an explicit input–output model representing the relationship among the system's variables. It may also be represented as knowledge implicitly (e.g., the connection weights of a neural network). Therefore, FDD is done through some kind of modeling, signal processing, and intelligence computation. In this paper, a variety of FDD techniques are reviewed within the unified data-processing framework to give a full picture of FDD and achieve a new level of understanding. According to the types of data and how the data are processed, the FDD methods are classified into three categories: model-based online data-driven methods, signal-based methods, and knowledge-based history data-driven methods. An outlook to the possible evolution of FDD in industrial automation, including the hybrid FDD and the emerging networked FDD, are also presented to reveal the future development direction in this field.

This study is motivated by the onboard fault detection of gas turbine engines (GTEs), where the computation resources are limited and the disturbance is assumed to be band-limited. A fast Fourier transformation (FFT)-based disturbance frequency estimation approach is proposed and performance indexes are improved by integrating such frequency information. Furthermore, in the left eigenvector assignment, both eigenvalues and free parameters are optimized. As illustrated in the application to the actuator fault detection of a GTE, significant improvements are achieved compared to the existing methods. By combining the frequency estimation and eigenvalue optimization, the main contribution of the paper is the reduction of the computation complexity and the avoidance of the local optimal solution due to fixed eigenvalues.

In this study, a novel identification technique, that is high-gain observer-based identification approach, is proposed for systems with bounded process and measurement noises. For system parameters with abnormal changes, an adaptive change detection and parameter identification algorithm is next presented. The presented technique and algorithm are finally applied to the parameter identification of the gas turbine engine by using the recorded input data from the engine test-bed. The identified parameters and the response curves are desirable. The simulations have proved the effectiveness of the proposed procedure compared with the previous identification approach.