Wide deployment of wireless sensor and actuator networks in cyber-physical systems requires systematic design tools to enable dynamic tradeoff of network resources and control performance. In this paper, we consider three recently proposed aperiodic control algorithms which have the potential to address this problem. By showing how these controllers can be implemented over the IEEE 802.15.4 standard, a practical wireless control system architecture with guaranteed closed-loop performance is detailed. Event-based predictive and hybrid sensor and actuator communication schemes are compared with respect to their capabilities and implementation complexity. A two double-tank laboratory experimental setup, mimicking some typical industrial process control loops, is used to demonstrate the applicability of the proposed approach. Experimental results show how the sensor communication adapts to the changing demands of the control loops and the network resources, allowing for lower energy consumption and efficient bandwidth utilization.
Communication systems supporting cyber-physical production applications should satisfy stringent delay and reliability requirements. Diversity techniques and power control are the main approaches to reduce latency and enhance the reliability of wireless communications at the expense of redundant transmissions and excessive resource usage. Focusing on the application layer reliability key performance indicators (KPIs), we design a deep reinforcement learning orchestrator for power control and hybrid automatic repeat request retransmissions to optimize these KPIs. Furthermore, to address the scalability issue that emerges in the per-device orchestration problem, we develop a new branching soft actor-critic framework in which a separate branch represents the action space of each industrial device. Our orchestrator enables near real-time control and can be implemented in the edge cloud. We test our solution with a 3GPP-compliant and realistic simulator for factory automation scenarios. Compared to the state-of-the-art, our solution offers significant scalability gains in terms of computational time and memory requirements. Our extensive experiments show significant improvements in our target KPIs, over the state-of-the-art, especially for 5th percentile user availability. To achieve these targets, our framework requires substantially less total energy or spectrum, thanks to our scalable RL solution.
Chip multiprocessor (CMP) suffers from growing threats on hardware security in recent years, such as side channel attack, hardware Trojan infection, chip clone, etc. In this paper, we propose a security-aware (SA) task mapping method to reduce the information leakage from CMP thermal side channel. First, we construct a mathematical function that can estimate the CMP security cost corresponding to a given mapping result. Then, we develop a greedy mapping algorithm that automatically allocates all threads of an application to a set of proper cores, such that the total security cost is optimized. Finally, we perform extensive experiments to evaluate our method. The experimental results show that our SA mapping effectively decreases the CMP side channel leakage. Compared to the two existing task mapping methods, Linux scheduler (LS; a standard Linux scheduler) and NoC-Sprinting (NS; a thermal-aware mapping technique), our method reduces side-channel vulnerability factor by up to 19 & x0025; and 7 & x0025;, respectively. Moreover, our method also gains higher computational efficiency, with improvement in million instructions per second achieving up to 100 & x0025; against NS and up to 33 & x0025; against LS.
European regulations on information exchange have put new requirements on analysis tools, the main one being the adoption of the IEC Common Information Model (CIM) that may help interoperability across applications. This paper proposes the use of Model-Driven Software Engineering (MDSE) methods to meet these new requirements. Specifically, this paper shows how to apply Model-to-Model (M2M) transformations. The M2M method presented herein allows to work directly with the information and mathematical description and computer implementation of dynamic models, independent from specific analysis tools. The M2M method proposed requires the development of a mapping between CIM/UML and the Modelica language, which allows to derive Modelica models of physical power systems for dynamic simulations.
A novel cyber physical method is proposed and experimentally verified for reliable distributed estimation of vehicle longitudinal velocity, robustly to road friction condition variations. In this method, the vehicle speed estimated at each of the four corners of the vehicle, using a linear parameter-varying observer in the physical layer, and speed data measured by a conventional low-cost GPS are incorporated in a distributed structure (in the cyber layer) to enhance the reliability of the estimate. The method minimizes a cost function quantizing the effect of disturbances on each corner's estimation and adversaries due to occasional GPS signal drops. A fault-tolerant estimation policy is integrated to deal with large deviations in corner estimations, which have unexpectedly high levels of confidence. The main advantages of the proposed method are increased reliability on various road surface conditions and robustness to faults, as confirmed by road tests. Several experimental tests, including lane change and low-excitation maneuvers, with various powertrain configurations on dry and slippery roads demonstrate the efficiency of the algorithm.
This paper proposes an algorithm, including a multiagent-based system architecture, for distributed inference of power grid topologies. The core algorithm is based on processing measurement time series, or fingerprints, using statistical correlation to determine the connectivity between nodes. Notably, the method and proposed system architecture is completely distributed. The solution has been developed with contemporary IEC 61850 compliant substation automation systems in mind. This paper includes a presentation of the theoretical foundation for the distributed topology inference algorithm as well as the proposed system architecture. A case study utilizing the RBTS bus 4 model is included to demonstrate the capabilities of the method under static as well as transient situations. Furthermore, the results and analysis of the performance and scalability of the algorithm are presented. The main contribution of this paper is an application to infer the electrical connectivity of power grids without central functionality.
Wireless communication is evolving to support critical control in automation systems. The fifth-generation (5G) mobile network air interface New Radio adopts a scalable numerology and mini-slot transmission for short packets that make it potentially suitable for critical control systems. The reliable minimum cycle time is an important indicator for industrial communication techniques but has not yet been investigated within 5G. To address such a question, this article considers 5G-based industrial networks and uses the delay optimization based on data-driven channel characterization (CCDO) approach to propose a method to evaluate the reliable minimum cycle time of 5G. Numerical results in three representative industrial environments indicate that following the CCDO approach, 5G-based industrial networks can achieve, in real-world scenario, millisecond-level minimum cycle time to support several hundred nodes with reliability higher than 99.9999%.
Wireless communication is gaining popularity in the industry for its simple deployment, mobility, and low cost. Ultralow latency and high reliability requirements of mission-critical industrial applications are highly demanding for wireless communication, and the indoor industrial environment is hostile to wireless communication due to the richness of reflection and obstacles. Assessing the effect of the industrial environment on the reliability and latency of wireless communication is a crucial task, yet it is challenging to accurately model the wireless channel in various industrial sites. In this article, based on the comprehensive channel measurement results from the National Institute of Standards and Technology at 2.245 and 5.4 GHz, we quantify the reliability degradation of wireless communication in multipath fading channels. A delay optimization based on the channel characterization is then proposed to minimize packet transmission times of a cyclic prefix orthogonal frequency division multiplexing system under a reliability constraint at the physical layer. When the transmission bandwidth is abundant and the payload is short, the minimum transmission time is found to be restricted by the optimal cyclic prefix duration, which is correlated with the communication distance. Results further reveal that using relays may, in some cases, reduce end-to-end latency in industrial sites, as achievable minimum transmission time significantly decreases at short communication ranges.
Look-ahead unit commitment (LAUC) is recently introduced among independent system operators (ISOs) in the U.S. to increase generation capacity by committing more generators after day-ahead unit commitment when facing various uncertainties in the power system operations. However, as the share of intermittent renewable energy increases significantly in the power generation portfolio, the load continues to fluctuate, and unexpected events and market behaviors happen nowadays, the ISOs are facing new critical challenges to maintain the reliability of power system. To systematically manage these uncertainties and corresponding challenges, new advanced approaches are urgently required to improve current LAUC models and solution methods. Therefore, in this paper, we first propose a new formulation to represent forbidden zones and dynamic ramping rate limits, which help capture the system operation status more accurately and hedge against the uncertainties more effectively, and then correspondingly propose a data-driven risk-averse LAUC model. Our computational experiments show how the size of data influences operational decisions and how the inclusion of forbidden zones and dynamic ramping provide better decisions.
The introduction of information and communication technologies makes network environments increasingly open, leaving smart-grid control systems incredibly vulnerable to malicious attacks. False data injection (FDI) attacks stealthily tamper with measurement data, resulting in erroneous decisions made by the control center that greatly influence the normal operation of the power system. By taking advantage of real-time data acquisition with edge computing, in this article, we propose a scheme based on classification of predicted residuals (CPRs) for the FDI attack detection. The CPR scheme first predicts the acquired measurement data at the edge of the sensing network via developing an accurate prediction model. Followed the novel real-time classification method under the edge devices supporting, it classifies the predicted residuals independent of the false data to enhance the detection accuracy. Through these two steps, the detection rate of FDI attacks is greatly improved. The proposed scheme is validated in a real microgrid testbed. Experimental results show that the CPR scheme performs well in detecting FDI attacks and remains sensitive in injection attack probability and magnitude. The detection scheme even has effectiveness at low injection attack probability and magnitude (5% and 0.018 per thousand, respectively). Furthermore, it also proves that the proposed scheme has applicability in high real-time requirements at the edge of smart grids.
We consider optimized cooperation in joint orthogonal multiple access and nonorthogonal multiple access in industrial cognitive networks, in which lots of devices may have to share spectrum and some devices (e.g., those for critical control devices) have higher transmission priority, known as primary users. We consider one secondary transmitter (less important devices) as a potential relay between a primary transmitter and receiver pair. The choice of cooperation scheme differs in terms of use cases. With decode-and-forward relaying, the channel between the primary and secondary users limits the achievable rates especially when it experiences poor channel conditions. To alleviate this problem, we apply analog network coding to directly combine the received primary message for relaying with the secondary message. We find achievable rate regions for these two schemes over Rayleigh fading channels. We then investigate an optimization problem jointly considering orthogonalmultiple access and nonorthogonal multiple access, where the secondary rate is maximized under the constraint of maintaining the primary rate. We find both analytical solutions as well as solutions based on experiments through the time sharing strategy between the primary and secondary system and the transmit power allocation strategy at the secondary transmitter. We show the performance improvements of exploiting analog network coding and the impacts of cooperative schemes and user geometry on achievable rates and resource sharing strategies.
An event-triggered attitude control algorithm is developed for quadrotor unmanned aerial vehicles (UAVs) subject to external disturbances. In this article, first an event-triggered supertwisting stabilizing control strategy for a class of second-order nonlinear systems is proposed. Then, a Lyapunov-based stability analysis is provided for the closed-loop system, and the Zeno-free execution of triggering sequence is guaranteed via rigorous analysis. Furthermore, the proposed control strategy is applied on attitude control of UAVs to reduce the computing cost without degrading the performance of the system. Finally, the efficiency of the developed method is validated by numerical simulation.
Motivated by theproliferation of wireless building automation systems (BAS) and increasing security-awareness among BAS operators, in this paper, we propose a taxonomy for the security assessment of BASs. We apply the proposed taxonomy to Thread, an emerging native IP-based protocol for BAS. Our analysis reveals a number of potential weaknesses in the design of Thread. We propose potential solutions for mitigating several identified weaknesses and discuss their efficacy. We also provide suggestions for improvements in future versions of the standard. Overall, our analysis shows that Thread has a well-designed security control for the targeted use case, making it a promising candidate for communication in next generation BASs.
The next generations of industrial control systems will require high-performance wireless networks (named WirelessHP) able to provide extremely low latency, ultrahigh reliability, and high data rates. The current strategy toward the realization of industrial wireless networks relies on adopting the bottom layers of general purpose wireless standards and customizing only the upper layers. In this paper, a new bottom-up approach is proposed through the realization of a WirelessHP physical layer specifically targeted at reducing the communication latency through the minimization of packet transmission time. Theoretical analysis shows that the proposed design allows a substantial reduction in packet transmission time, down to 1 $\mu$ s, with respect to the general purpose IEEE 802.11 physical layer. The design is validated by an experimental demonstrator, which shows that reliable communications up to 20 m range can be established with the proposed physical layer.
As many robot applications become more reliant on wireless communications, wireless network latency and reliability have a growing impact on robot control. This article proposes a network hardware-in-the-loop (N-HiL) simulation framework to evaluate the impacts of wireless on robot control more efficiently and accurately, and then improve the design by employing correlation analysis between communication and control performances. The N-HiL method provides communication and robot developers with more trustworthy network conditions, while the huge efforts and costs of building and testing the entire physical robot system in real life are eliminated. These benefits are showcased in two representative latency-sensitive applications: 1) safe multirobot coordination for mobile robots, and 2) human-motion-based teleoperation for manipulators. Moreover, we deliver a preliminary assessment of two new-generation wireless technologies, the Wi-Fi6 and 5G, for those applications, which has demonstrated the effectiveness of the N-HiL method as well as the attractiveness of the wireless technologies.
Wireless industrial cyber-physical systems are increasingly popular in critical manufacturing processes. These kinds of systems, besides high performance, require strong security and are constrained by low computational capabilities. Physical layer authentication (PHY-AUC) is a promising solution to meet these requirements. However, the existing threshold-based PHY-AUC methods only perform ideally in stationary scenarios. To improve the performance of PHY-AUC in mobile scenarios, this article proposes a novel threshold-free PHY-AUC method based on machine learning (ML), which replaces the traditional threshold-based decision-making with more adaptive classification based on ML. This article adopts channel matrices estimated by the wireless nodes as the authentication input and investigates the optimal dimension of the channel matrices to further improve the authentication accuracy without increasing too much computational burden. Extensive simulations are conducted based on a real industrial dataset, with the aim of tuning the authentication performance, then further field validations are performed in an industrial factory. The results from both the simulations and validations show that the proposed method significantly improves the authentication accuracy.
In cloud manufacturing systems, fault diagnosis is essential for ensuring stable manufacturing processes. The most crucial performance indicators of fault diagnosis models are generalization and accuracy. An urgent problem is the lack and imbalance of fault data. To address this issue, in this article, most of existing approaches demand the label of faults as a priori knowledge and require extensive target fault data. These approaches may also ignore the heterogeneity of various equipment. We propose a cloud-edge collaborative method for adaptive fault diagnosis with label sampling space enlarging, named label-split multiple-inputs convolutional neural network, in cloud manufacturing. First, a multiattribute cooperative representation-based fault label sampling space enlarging approach is proposed to extend the variety of diagnosable faults. Besides, a multi-input multi-output data augmentation method with label-coupling weighted sampling is developed. In addition, a cloud-edge collaborative adaptation approach for fault diagnosis for scene-specific equipment in cloud manufacturing system is proposed. Experiments demonstrate the effectiveness and accuracy of our method.
Edge computing (EC) is an essential component of large-scale intelligent manufacturing systems for Industry 4.0, which promises to provide a preprocessing platform for the massive data generated by the terminals and guarantee lower delay and more security compared to directly processing data in cloud computing. Nevertheless, access authentication is a crucial security issue of current EC systems, and, thus, this article presents a solution to enhance the access classification accuracy by exploiting the physical layer information. Our method employs a weighted voting scheme for channel state information based authentication using a single sample which includes sample segmentation, grouping, and weighted voting and finally achieves the fast and low complexity secure-access requirement of the EC system without increasing the individual devices' sample size and computational complexity. Experimental results utilizing public datasets and field-measured datasets demonstrate that the proposed weighted voting method has higher accuracy and robustness than existing methods.
The high penetration of power electronic converter loads in dc microgrid causes system stability issue, or also known as constant power load issue, due to their negative impedance characteristics. The stability concern will be more complicated for a self-disciplined microgrid that allows plug and play of various distributed generations (DGs). This article proposes a robust droop-based controller for decentralized power sharing in a dc microgrid considering large-signal stability. For each DG interface converter subsystem, the interactions with other DG interface converters and loads are estimated by a nonlinear disturbance observer (NDO) utilizing the subsystem's own information to achieve decentralized power sharing and fast voltage regulation. With the uncertainties of circuit parameters modeled as a lumped disturbance term and compensated by an NDO, the proposed controller can significantly enhance the robustness against the uncertainties of circuit parameters. The large-signal stability of the whole interconnected system is proved by the backstepping algorithm and Lyapunov theorem. The efficacy and large-signal stability of the proposed approach are verified by both simulations and experiments.
In today's globalized business world, outsourcing, joint ventures, mobile and cross-border collaborations have led to work environments distributed across multiple organizational and geographical boundaries. The new requirements of portability, configurability and interoperability of distributed device networks put forward new challenges and security risks to the system's design and implementation. There are critical demands on highly secured collaborative control environments and security enhancing mechanisms for distributed device control, configuration, monitoring, and interoperation. This paper addresses the collaborative control issues of distributed device networks under open and dynamic environments. The security challenges of authenticity, integrity, confidentiality, and execution safety are considered as primary design constraints. By adopting policy-based network security technologies and XML processing technologies, two new modules of Secure Device Control Gateway and Security Agent are introduced into regular distributed device control networks to provide security and safety enhancing mechanisms. The core architectures, applied mechanisms, and implementation considerations are presented in detail in this paper.
In-home healthcare services based on the Internet-of-Things (IoT) have great business potential; however, a comprehensive platform is still missing. In this paper, an intelligent home-based platform, the iHome Health-IoT, is proposed and implemented. In particular, the platform involves an open-platform-based intelligent medicine box (iMedBox) with enhanced connectivity and interchangeability for the integration of devices and services; intelligent pharmaceutical packaging (iMedPack) with communication capability enabled by passive radio-frequency identification (RFID) and actuation capability enabled by functional materials; and a flexible and wearable bio-medical sensor device (Bio-Patch) enabled by the state-of-the-art inkjet printing technology and system-on-chip. The proposed platform seamlessly fuses IoT devices (e. g., wearable sensors and intelligent medicine packages) with in-home healthcare services (e. g., telemedicine) for an improved user experience and service efficiency. The feasibility of the implemented iHome Health-IoT platform has been proven in field trials.
In this paper, data-driven self-calibration al- gorithms for the low-cost gas sensors are designed. The sensor measurement errors happen due to the imperfect compensation for the variation of sensor component be- havior that is caused by changing of environmental fac- tors. To calibrate the sensors, the hidden Markov model is utilized to characterize the statistical dependency between the environmental factors and the variation of sensor com- ponent behavior. Considering the time-varying property of this dependency, a time-adaptive learning framework is further designed to update the hidden Markov model so that the time-varying drift process can be better tracked over a long term. More specifically, a time-adaptive expectation maximization learning approach is proposed to efficiently update the hidden Markov model parameters. A closed form of the convergence rate of this time-adaptive learning approach is derived, which provides a theoretical guaran- tee on the time efficiency as well as the computational efficiency. The performance of the scheme is illustrated in numerical experiments utilizing real data, which shows that long-term stable calibration performance can be achieved.
In an industrial automation system, one of the most important parts is control loop. Fog computing is a potential solution for industrial control in time-critical applications as it provides distributed computing services closer to the connected devices. However, a huge amount of data exchanging among fog nodes causes high communication load, which constrains the overall response time from fog nodes to actuators. In this paper, we consider the erasure environment, batched sparse (BATS) codes are applied to the Map and the Data Shuffling stages of distributed fog computing process to reduce both the communication and the computation loads. The communication loads of the uncoded, the coded, and the proposed BATS-based schemes over erasure channels are calculated, respectively. Numerical results show that the BATS-based scheme can reduce the communication and the computation loads simultaneously, and furthermore reduce the overall response time from fog nodes to the actuators.
To meet the extremely low latency constraints of industrial wireless control in critical applications, the wireless high-performance scheme (WirelessHP) has been introduced as a promising solution. The proposed design showed great improvements in terms of latency, but its performance in terms of reliability have not been fully tested yet. While traditional wireless systems achieve high reliability through packet retransmissions, this would impair the latency, and an approach based on channel coding is preferable in industrial applications. In this paper, a set of packet error rate (PER) tests is performed by applying concatenated Reed Solomon and convolutional codes to the WirelessHP physical layer, using a demonstrator based on a universal software radio peripheral platform. The effectiveness of channel coding to achieve 10-7 level PER without retransmissions is shown in typical laboratory and factory environments.