Dependency on the correct operation of embedded systems is rapidly growing, mainly due to their wide range of applications. Their structures are becoming more complex and currently require multi-core processors with scalable shared memory, signal-processing pipelines, and sophisticated software modules to meet increasing computational power, flexibility demands. Additionally, interaction with real-world entities and modern communication capabilities further enhance the mentioned features and give rise to the embedded and cyber-physical systems (ECPS). As a consequence, the reliability of ECPS becomes a key issue during system development. Generally, state-of-the-art verification methodologies for ECPS generate test vectors and use assertion-based verification and high-level processor models, during simulation; however, new challenges arose, such as need for meeting time and energy constraints, handling concurrent software, evaluating implementation-structure choices, ensuring correct system behavior together with physical plants, and supporting new software architectures and legacy designs. This survey deals with the mentioned issues, reviews related literature, and discusses recent advances in symbolic model checking techniques and their applications to control synthesis. Additionally, challenges, problems, and recent advances to ensure correctness and timeliness, regarding ECPS, are discussed. Reliability issues, when developing ECPS, are then considered, as a prominent verification and synthesis application for achieving correct-by-construction systems.

In order to provide intelligent services, the Internet of Things (IoT) facilitates millions of smart cyber-physical devices to be enabled with network connectivity to sense, collect, process, and exchange information. Unfortunately, the traditional communication infrastructure is vulnerable to cyber attacks and link failures, so it is a challenging task for the IoT to explore these applications. In order to begin research and contribute into the IoT-based cyber-physical digital world, one will need to know the technical challenges and research opportunities. In this study, several key technical challenges and requirements for the IoT communication systems are identified. Basically, privacy, security, intelligent sensors/actuators design, low cost and complexity, universal antenna design, and friendly smart cyber-physical system design are the main challenges for the IoT implementation. Finally, the authors present a diverse set of cyber-physical communication system challenges such as practical implementation, distributed state estimation, real-time data collection, and system identification, which are the major issues require to be addressed in implementing an efficient and effective IoT communication system.

Recently, the commercial market has seen an increase in the availability of smart wearable Internet of things (IoT) devices (wearables) including such items as: smart shoes, smart watches, wrist bands, and pendants. Many of these devices are part of the human-in-the-loop cyber-physical systems. In this research, the authors have designed and developed an embedded sensory IoT system with a low-power Bluetooth communication module to collect body single node voltage using a smartphone. Their approach for sensing the user's movement builds on work in the electric field sensing. Experimentation and verification have been conducted on a group of test subjects with different test scenarios including remaining at rest, walking, jumping, running, hand waving, eating, and bending over. They designed and developed their sensor to detect body motion data, and then used their algorithm to analyse the collected data. This study introduces the use of signal processing techniques for sensor data analytics to detect human body motion. The system can detect activity with a high degree of accuracy (∼ 87%).

Owing to the advantages of burning low-quality coal (coal slime and coal gangue), furnace desulfurisation, low emission and deep load adjustment, the circulating fluidised bed (CFB) combustion technology becomes one of the few fossil fuel utilisation technologies funded continuously by the Chinese government. However, compared with the pulverised coal boiler, the combustion process of CFB boiler is more complicated because of the larger time delay, significant uncertainty and more coupled variables. In this study, a data-driven proportional–integral-derivative (DD-PID) control strategy is presented for the combustion control of CFB boiler to improve the operating performance under full operating conditions. By analysing the running mechanism of combustion process, an inverse decoupler is introduced to transfer the combustion object to the generalised controlled object, which has relatively independent input–output relationship. After that, a normative procedure of DD-PID, including PID-parameter database establishment, information-vector neighbourhood selection, active PID-parameter determination, database update, and redundant vector deletion, is given. Finally, a series of case study, including numerical tests applied to the proposed combustion model and application test employed on 330 MW CFB simulation platform proves the feasibility of DD-PID control strategy.

As power systems mature into smart grid entities, they face new challenges toward online monitoring and control of the system's behaviour. Burgeoning classes of cyber-attacks are observed which may cause instability of the power grid and system blackouts if not identified. In this study, the authors propose an ensemble Kalman filter based anomaly detector using a relaxation-based solution. Performance of the proposed method is tested with Chi-Square detector and Largest Normalised Residual test. Results of simulations based on real-world data, up to 5000 bus system, demonstrate the effectiveness of the proposed framework over traditional bad data detection in presence of false data injection attack.

With the increasing number of services and industries including nuclear, chemical, aerospace, and automotive sectors in cyber-physical systems (CPSs), systems are being severely overloaded. CPSs comprises mixed-critical tasks which are of either safety-critical (high) or non-safety critical (low). In traditional task scheduling, most of the existing scheduling algorithms provide poor performance for high-criticality tasks when the system experiences overload and do not show explicit separation among different criticality tasks to take advantage of using cloud resources. Here, we propose a framework to schedule the mixed-criticality tasks by analyzing their deadlines and execution times which leverage the performance of parallel processing through OpenMP. The proposed framework introduces a machine learning-based prediction for a task offloading in the cloud. Moreover, it illustrates to execute a selected number of low-criticality tasks in the cloud while the high-criticality tasks are run on the local processors during the system overload. As a result, the high-criticality tasks meet all their deadlines and the system achieves a significant improvement in the overall execution time and better throughput. In addition, the experimental results employing OpenMP show the effectiveness of using the partitioned scheduling over the global scheduling method upon multiprocessor systems to achieve the tasks isolation.

Due to the complexity and interdependencies in the cyber-physical power system, the co-simulation is a good choice to validate the research and model of the smart grid with existing simulators in different domains. A novel co-simulation platform, LIghtweight Co-simulation of Power and Information and communication technology (ICT) systems for performance Evaluation of smart grid (LICPIE), is developed based on the concepts of software bus and middleware in the study. Compared with other co-simulation platforms, the advantage of LICPIE is that it has a distributed architecture including sub-simulators; management layer based on software bus and middleware, and defines its own standard interface specialist embedded in middleware. The standard interface specialist relieves the limitation of the interface types supported by sub-simulators, which makes the LICPIE more extendible and has wide scope of applications. What's more, benefiting from the distributed architecture and the standard interface specialist, sub-simulators' heterogeneity is avoided and multi-domain simulators could be combined freely according to simulation requirements. An exemplary application is used to describe how sub-simulators with different types of interfaces hang themselves on LICPIE. At last, two co-simulation applications with different combinations of power and ICT systems' simulators show the effectiveness, flexibility and extendibility of the LICPIE.

The continued growth of distributed generation (DG) in the electrical grid has led to the expansion of microgrids. Microgrids contain distributed power generation units, energy storage devices, and controllable loads with the capability to operate in both grid-connected and island modes. The economic operation of a microgrid is achieved through an energy management system that optimally schedules DGs and storage devices and continuously balances supply and demand. In this study, a formulation of optimal unit commitment (UC) and dispatch scheduling of DGs in a grid-connected microgrid system is presented. Mixed-integer linear programming is used to implement the optimal UC and dispatch scheduling model. The objective is to minimise the overall operating cost of the system by optimally utilising an energy storage device and a combined heat and power (CHP) generation unit using load and renewable energy generation prediction. Operational constraints such as generation limits of DGs, battery charging/discharging limits and state-of-charge limits are to be satisfied during all intervals of operation. Simulation results indicate that the operational cost of the system is significantly reduced through optimal scheduling of an energy storage system and a CHP unit using the proposed strategy.

Cooperative vehicular cyber physical systems build a group of entities that can accomplish a cooperative task using the distributed control approaches. To this end, such a cooperative task can be coordinated and managed by a distributed control plane that will be able to encapsulate all the required functionality in a layered architecture providing the required interoperability. Here, the authors propose such a distributed control plane that consists of the cooperative awareness layer, the communication layer and the distributed control layer. Wireless communications play an important role for the mobility provision, taking into account different constraints in order to provide high reliability and low latency. A simulation environment is considered with a leader–follower control format, where the reliability is evaluated. Further, a distributed safety monitoring approach is devised, given a control diagram and mapping of the events to the different components. The event monitoring relies on the self-triggered approach, where a use case is evaluated to highlight the impact of the input and output delays to the model predictive control component of the overall distributed control diagram including the calculation of the number of triggered events.

Digital embedded systems in safety-critical cyber-physical-systems (CPSs) require high levels of resilience and robustness against different fault classes. In recent years, self-healing concepts based on biological physiology have received attention for the design and implementation of reliable systems. However, many of these approaches have not been architected from the outset with safety in mind, nor have they been targeted for the safety-related automation industry where the significant need exists. This study presents a new self-healing hardware architecture inspired by integrating biological concepts, fault tolerance techniques, and IEC 61131-3 operational schematics to facilitate adaption in automation and critical infrastructure. The proposed architecture is organised in two levels: the critical functions layer used for providing the intended service of the application and the healing layer that continuously monitors the correct execution of that application and generates health syndromes to heal any failure occurrence inside the functions layer. Finally, two industrial applications have been mapped on this architecture to date, and the authors believe the nexus of its concepts can positively impact the next generation of critical CPSs in industrial automation.

The concept of cyber-physical systems (CPSs) is a core technology indispensable to Industry 4.0. The development of an intelligent automatic operational management platform is presented in this study. The platform is used to collect on-site data, provide predictive maintenance, and monitor and control the mobile robots. For predictive maintenance, it is targeted at products from manufacturing big data to cloud computing, and predictive maintenance for all factories around the world. The efficiency of the management system is improved by the performance evaluation strategy. In the experiments, the authors implement and discuss the predictive maintenance platform with generated synthetic data and real-world data collected from a gearbox plant. The results show that the important variables possess large drops according to mean decrease Gini. A warning generation for battery simulation and manufacturing execution is also presented to demonstrate the proposed system design. The results show that the discharge curve drops sharply at the last stage, and the warning value can be set accordingly.

The false data injection attack can tamper with the measurement information collected by the Supervisory Control and Data Acquisition, threat the security of state estimation. Therefore, the analysing methods and detection methods of false data injection attacks have important theoretical and practical significance for ensuring the safe operation of smart grids. This study uses the invertible automatic encoder to reduce the dimension of the original data and uses the long-short-term memory to detect false data injection attacks. This method overcomes the shortcomings of shallow algorithm and traditional machine learning algorithm for power big data training and avoiding the problems of gradient explosion and gradient disappearing during training. Finally, the authors perform a large number of experiments in the IEEE 118-node test system and the 300-node test system and verify the effectiveness of the proposed method.

The pervasive use of heterogeneous and non-proprietary information and communication technology exposes the power grid to cyber-attacks. In particular, monitoring-control attacks (MCA), which manipulate control decisions by fabricating measurements, are highly threatening, because MCA are difficult to detect and can coordinately inflict severe consequences at a large scale. To defend against MCA, a semantic analysis framework is proposed in complement to direct-setting intrusion detection. The proposed framework has the advantages of promising runtime and detection performance. The performance of the proposed framework is evaluated under different attack scenarios and compared with a direct-settings intrusion detection systems using a 6-bus test system and the New-England 39-bus test system.

Real-world autonomous systems operate under uncertainty about both their pose and dynamics. Autonomous control systems must simultaneously perform estimation and control tasks to maintain robustness to changing dynamics or modelling errors. However, information gathering actions often conflict with optimal actions for reaching control objectives, requiring a trade-off between exploration and exploitation. The specific problem setting considered here is for discrete-time non-linear systems, with process noise, input-constraints, and parameter uncertainty. This study frames this problem as a Bayes-adaptive Markov decision process and solves it online using Monte Carlo tree search with an unscented Kalman filter to account for process noise and parameter uncertainty. This method is compared with certainty equivalent model predictive control and a tree search method that approximates the QMDP solution, providing insight into when information gathering is useful. Discrete time simulations characterise performance over a range of process noise and bounds on unknown parameters. An offline optimisation method is used to select the Monte Carlo tree search parameters without hand-tuning. In lieu of recursive feasibility guarantees, a probabilistic bounding heuristic is offered that increases the probability of keeping the state within a desired region.