Internet of Things (IoT) and cyber-physical systems (CPS) technologies can be applied to many application domains. Examples include intelligent green house, intelligent transportation system, power distribution grid, smart home, smart building, and smart city. Among these application domains, some of them have been extensively studied, e.g., smart home and intelligent transportation systems. In the meantime, smart buildings and smart cities attract researchers and industries to investigate these two use scenarios. Well-designed IoT/CPS can reduce energy consumption, enhance safety in buildings and cities, or can increase the comfortability in the building. In the last few years, the research communities and industrial partners started to study and investigate these two use scenarios to develop prototype or commercial services for these two scenarios. Although many works have been conducted on these two scenarios, many challenges remain open. In this study, the authors study the development and challenges in five topics. They are middleware, computation model, fault tolerance, quality of data, and virtual run-time environment.

The smart grid is arguably one of the most complex cyber-physical systems (CPS). Complex security challenges have been revealed in both the physical and the cyber parts of the smart grid, and an integrative analysis on the cyber-physical (CP) security is emerging. This paper provides a comprehensive and systematic review of the critical attack threats and defence strategies in the smart grid. We start this survey with an overview of the smart grid security from the CP perspective, and then focuses on prominent CP attack schemes with significant impact on the smart grid operation and corresponding defense solutions. With an in-depth review of the attacks and defences, we then discuss the opportunities and challenges along the smart grid CP security. We hope this paper raises awareness of the CP attack threats and defence strategies in complex CPS-based infrastructures such as the smart grid and inspires research effort toward the development of secure and resilient CP infrastructures.

The increased penetration of distributed energy resources (DER) will significantly increase the number of devices that are owned and controlled by consumers and third-parties. These devices have a significant dependency on digital communication and control, which presents a growing risk from cyber-attacks. This study proposes a holistic attack-resilient framework to protect the integrated DER and the critical power grid infrastructure from malicious cyber-attacks, helping ensure the secure integration of DER without harming the grid reliability and stability. Specifically, the authors discuss the architecture of the cyber-physical power system with a high penetration of DER and analyse the unique cybersecurity challenges introduced by DER integration. Next, they summarise important attack scenarios against DER, propose a systematic DER resilience analysis methodology, and develop effective and quantifiable resilience metrics and design principles. Finally, they introduce attack prevention, detection, and response measures specifically designed for DER integration across cyber, physical device, and utility layers of the future smart grid.

There is a growing emphasis to find alternative non-traditional ways to manage patients to ease the burden on health care services largely fuelled by a growing demand from sections of population that is ageing. In-home remote patient monitoring applications harnessing technological advancements in the area of Internet of things (IoT), semantic web, data analytics, and cloud computing have emerged as viable alternatives. However, such applications generate large amounts of real-time data in terms of volume, velocity, and variety thus making it a big data problem. Hence, the challenge is how to combine and analyse such data with historical patient data to obtain meaningful diagnoses suggestions within acceptable time frames (considering quality of service (QoS)). Despite the evolution of big data processing technologies (e.g. Hadoop) and scalable infrastructure (e.g. clouds), there remains a significant gap in the areas of heterogeneous data collection, real-time patient monitoring, and automated decision support (semantic reasoning) based on well-defined QoS constraints. In this study, the authors review the state-of-the-art in enabling QoS for remote health care applications. In particular, they investigate the QoS challenges required to meet the analysis and inferencing needs of such applications and to overcome the limitations of existing big data processing tools.

Due to the ever increasing complexity of embedded and cyber-physical systems, corresponding design solutions relying on modelling languages such as Unified Modelling Language (UML)/Object Constraint Language (OCL) find increasing attention. Due to the recent success of formal verification techniques, UML/OCL models also allow to verify and/or check certain properties of a given model in early stages of the design phase. To this end, different approaches for verification and validation have been proposed. In this work, the authors motivate, define, and describe different verification tasks for structural, as well as behavioural UML/OCL models that can be solved using solvers for Boolean satisfiability. They describe how these verification tasks can be translated into a symbolic formulation which is passed to off-the-shelf solvers afterwards. The obtained results enable designers to draw conclusions about the correctness of the considered model.

Intelligent traffic lights are critical cyber-physical systems that help smart cities to cut road congestion and vehicle emissions. However, they also open a new frontier of cybersecurity. Security researchers have demonstrated ways to compromise the traffic lights to cause potential traffic disruption and public safety degradation. This study aims to raise the public awareness of the cybersecurity issues in traffic light systems. The authors present a bi-level game-theoretic framework for assessing cybersecurity risks of traffic light systems, as the first step towards understanding and mitigating the security vulnerabilities. Additionally, they propose a minimax-regret-based methodology to guide the deployment of defensive measures in traffic light systems against cyberattacks.

The rise of the Internet of Things has led to an explosion of sensor computing platforms. The complexity and applications of IoT devices range from simple devices in vending machines to complex, interactive artificial intelligence in smart vehicles and drones. Developers target more aggressive objectives and protect market share through feature differentiation; they just choose between low-cost, and low-performance CPU-based systems, and high-performance custom platforms with hardware accelerators including GPUs and FPGAs. Both CPU-based and custom designs introduce a variety of design challenges: extreme pressure on time-to-market, design cost, and development risk drive a voracious demand for new CAD technologies to enable rapid, low cost design of effective IoT platforms with smaller design teams and lower risk. In this article, we present a generic IoT device design flow and discuss platform choices for IoT devices to efficiently tradeoff cost, power, performance and volume constraints: CPU-based systems and custom platforms that contain hardware accelerators including embedded GPUs and FPGAs. We demonstrate this design process through a driving application in computer vision. We also present current critical design automation needs for IoT development and demonstrate how our prior work in CAD for FPGAs and SoCs begin to address these needs.

Road scene understanding and semantic segmentation is an on-going issue for computer vision. A precise segmentation can help a machine learning model understand the real world more accurately. In addition, a well-designed efficient model can be used on source limited devices. The authors aim to implement an efficient high-level, scene understanding model in an embedded device with finite power and resources. Toward this goal, the authors propose ApesNet, an efficient pixel-wise segmentation network which understands road scenes in near real-time and has achieved promising accuracy. The key findings in the authors’ experiments are significantly lower the classification time and achieving a high accuracy compared with other conventional segmentation methods. The model is characterised by an efficient training and a sufficient fast testing. Experimentally, the authors use two road scene benchmarks, CamVid and Cityscapes to show the advantages of ApesNet. The authors’ compare the proposed architecture's accuracy and time performance with SegNet-Basic, a deep convolutional encoder–decoder architecture. ApesNet is 37% smaller than SegNet-Basic in terms of model size. With this advantage, the combining encoding and decoding time for each image is 2.5 times faster than SegNet-Basic.

In this study the authors are interested in safety-critical real-time applications implemented on distributed architectures supporting the time-sensitive networking (TSN) standard. The on-going standardisation of TSN is an IEEE effort to bring deterministic real-time capabilities into the IEEE 802.1 Ethernet standard supporting safety-critical systems and guaranteed quality-of-service. TSN will support time-triggered (TT) communication based on schedule tables, audio-video-bridging (AVB) flows with bounded end-to-end latency as well as best-effort messages. The authors first present a survey of research related to the optimisation of distributed cyber-physical systems using real-time Ethernet for communication. Then, the authors formulate two novel optimisation problems related to the scheduling and routing of TT and AVB traffic in TSN. Thus, the authors consider that they know the topology of the network as well as the set of TT and AVB flows. The authors are interested to determine the routing of both TT and AVB flows as well as the scheduling of the TT flows such that all frames are schedulable and the AVB worst-case end-to-end delay is minimised. The authors have proposed an integer linear programming formulation for the scheduling problem and a greedy randomised adaptive search procedure-based heuristic for the routing problem. The proposed approaches have been evaluated using several test cases.

Model-based development frameworks for cyber-physical systems (CPSs) such as Simulink and Stateflow are popular for many applications. For safety and security concerns, verification and testing/validation must be performed on the model-based CPS designs. In this study, the authors present an automatic test generation approach for model-based CPS designs in Simulink/Stateflow based on its translation to input/output extended finite automata (I/O-EFA) developed in the authors’ prior works. The test generation problem requires identifying the executable paths of the I/O-EFA model and also generating a test input for those paths. To execute a path, a certain sequence of other paths must be executed first, which they automatically identify. The approach is implemented by applying two different techniques, model checking and constraint solving. Both test generation implementations are validated by a case study. The results show that both implementations can generate test cases, while the implementation based on constraint solving is in general faster. The approach is further extended to requirements-based test generation. These tests are then used for validation purposes, and the failed versus passed tests are used to localise the fault to plausible Simulink/Stateflow blocks using the notion of fault-seed used in their earlier work. The approaches are applied on a bounded counter and a thermal control of a house as two different case studies.