CPS Systems Research Articles

ADEROS: Artificial Intelligence-Based Detection System of Critical Events for Road Security

The deployment of artificial intelligence (AI) in Intelligent Transportation Systems (ITS), especially in the field of Intelligent Transportation Cyber-Physical Systems (ITCPS) has a strong potential to achieve higher efficiency, reliability, and increased safety in both transportation and traffic. This work focuses on the real-world implementation of ITCPS, in which structure and elements in combination with advanced image processing methods increase safety and fluidity of road traffic at crossroads and railway crossings. In this work, we present a novel system called Artificial Intelligence-based Detection System for Road Security (ADEROS), which combines elements of CPS systems, object detection, and classification, computer vision (CV) which analyzes vehicle trajectory tracking, vehicle and pedestrian presence, light signaling systems, railway barriers at railway crossings, and railway warnings. The presented system is based on a camera module that is suitably positioned to capture the entire scene. The module uses graphics processing units (GPU) for accelerated image processing techniques and the YOLOv4 deep neural network model to detect traffic participants and then dangerous situations in various crossroads and railway crossings. Our improved unique detector can distinguish between individual types of road users and the status of several safety devices at crossroads and railway crossings (for example, the state of traffic lights (TL) or rail barriers). Furthermore, we present experimental implementation details of the ADEROS system, which includes a central server web interface for live traffic situation monitoring, various communication channels for the camera module, and a central server based on.NET core, Cassandra DB, and different security protocols. All data from risky situations are evaluated and transferred to the central server securely without human intervention. The central server aggregates and archives all risky situational data from connected cameras. Finally, we present our experimental results from a real-world pilot project that consists of a camera module prototype deployed in a real crossroad and an operational central web server.

A risk source identification model for network security of power CPS system (CPS) based on fuzzy artificial neural network

In order to improve the effect and accuracy of risk source identification, this paper studied the network security risk source identification model of power CPS system based on fuzzy artificial neural network. The network security risk source index system of power CPS system was constructed, and the dimension of index data was reduced by principal component analysis. Fuzzy theory is used to process the index data after dimension reduction, and the comprehensive membership vector of each index is obtained. The dynamic clustering algorithm is used to determine the number of hidden layer units of radial basis function neural network, and the network security risk source identification model is established. Finally, the quantitative value of risk source identification is output. The experimental results show that the model can effectively reduce the dimension of the network security risk source index data of the power CPS system. The optimal distance threshold of the hidden layer is 4.2, and the optimal number of units is 6. In the final identification results, four severe risk sources and five moderate risk sources were obtained, and the quantitative values of risk source identification of each index were 63, 70, 71, 77, 65, 89 and 96, respectively, indicating that the model can effectively identify network security risk sources of power CPS systems. With the increase of the proportion of communication nodes removed, when there are various types of security vulnerability information, the mean square error value of the model is always lower than the set threshold, indicating that the model has high recognition accuracy.

An Investigation of Artificial Life to Examine Systems Related to Natural Life, Its Processes, And Its Evolution, Through the Use of Simulations with Computer Models, Robotics, and Biochemistry

This article is based on the CPS or cyber-physical system or intelligent system. The term “physical” is associated with natural and also human man-made systems that have been governed by the laws that are connected with physics while operating in continuity. CPS is described as the network of some embedded systems which establishes an interaction with both physical inputs as well as output. CPS faces the challenges of cyber and physical threats as these systems have been found to be of heterogeneous nature that has a reliance on both private and sensitive data. This study has focused on the CPS and CPS components as well. This is the integration of sensing, controlling, networking and computation into some physical objects as well as infrastructures that connect them through the help of the internet with each other. There are some CPS components that have been utilized for sensing different types of information where the CPS components can be classified into Sensing Components (SC) and also Controlling Components (CC). this study also shed light on the “Programmable Logic Controllers” (PLC) have been created for replacing hard-wired relays and have been assumed in the form of “industrial digital computers” controlling manufacturing processes including the performance of robotic performance. This study has also shed light on the improvements in unsupervised intrusion detection within CPS. Intrusion Detection Systems (IDSs) have been becoming certain building blocks for developing the CPS while detecting its potential threats and also triggering the modules to block and also mitigate those adverse impacts from cyber threats. Development via digital twin through CPS. The CPS paradigm has been involved with physical and software components that have been deeply intertwined with each other where each component has been operating on both spatial and temporal scales possessing multiple behavioural modalities and also interacting with each other. Keywords : CPS systems, intelligent algorithms running, intelligent interfaces, open architecture system CPS, RFID- based position detection.

A literature review of contemporary industrial revolutions as decision support resources

Today's industrial revolutions radically transform our understanding of technological possibilities and the importance of human creativity. All these contribute to changes in the process and tools of decision support. In this literature review, I will discuss the technological opportunities that can be applied in Industry 4.0 and Industry 5.0 and the related role of human resources. Industry 4.0 is not just about an explosion in the industry, but about the digitalization of the entire economy. Digitalisation solutions include CPS systems that integrate computing, networking and physical processes. CPS systems are based on the networking of the machines that make up the system, with the ultimate goal of developing a learning algorithm that can autonomously perform optimization tasks. In the centre of Industry 5.0 is people-centredness, sustainability and resilience. Along with Industry 5.0, human-machine interfaces have developed. Its aim is to help the employee to work in a way that takes into account his or her abilites. Harnessing the potential of the human-robot interface to improve economic and social processes will be a crucial factor in the coming years. Moreover, supporting the technological lagging is also a priority in the modern industrial revolutions. In additional to financial incentives, availability of technical support can be a critical factor during the implementation procedure and the use of these new technologies. This can have a big impact on the daily lives of businesses and households.

Biocompatibility and degradation comparisons of four biodegradable copolymeric osteosynthesis systems used in maxillofacial surgery: A goat model with four years follow-up.

Applying biodegradable osteosyntheses avoids the disadvantages of titanium osteosyntheses. However, foreign-body reactions remain a major concern and evidence of complete resorption is lacking. This study compared the physico-chemical properties, histological response and radiographs of four copolymeric biodegradable osteosynthesis systems in a goat model with 48-months follow-up. The systems were implanted subperiosteally in both tibia and radius of 12 Dutch White goats. The BioSorb FX [poly(70LLA-co-30DLLA)], Inion CPS [poly([70–78.5]LLA-co-[16–24]DLLA-co-4TMC)], SonicWeld Rx [poly(DLLA)], LactoSorb [poly(82LLA-co-18GA)] systems and a negative control were randomly implanted in each extremity. Samples were assessed at 6-, 12-, 18-, 24-, 36-, and 48-month follow-up. Surface topography was performed using scanning electron microscopy (SEM). Differential scanning calorimetry and gel permeation chromatography were performed on initial and explanted samples. Histological sections were systematically assessed by two blinded researchers using (polarized) light microscopy, SEM and energy-dispersive X-ray analysis. The SonicWeld Rx system was amorphous while the others were semi-crystalline. Foreign-body reactions were not observed during the complete follow-up. The SonicWeld Rx and LactoSorb systems reached bone percentages of negative controls after 18 months while the BioSorb Fx and Inion CPS systems reached these levels after 36 months. The SonicWeld Rx system showed the most predictable degradation profile. All the biodegradable systems were safe to use and well-tolerated (i.e., complete implant replacement by bone, no clinical or histological foreign body reactions, no [sterile] abscess formation, no re-interventions needed), but nanoscale residual polymeric fragments were observed at every system's assessment.

Proposal of a Tool for Determining Sub- and Main Dimension Indicators in Assessing Internal Logistics Readiness for Industry 4.0 within a Company

Key elements of Industry 4.0 are the digitization of products and production, enterprise information systems, robotic workplaces, communication infrastructure, and of course, employees. Industry 4.0 transforms production from stand-alone automated units to fully integrated automated and continuously optimized production environments. According to the prediction of Industry 4.0, new global networks will be created based on the interconnection of production equipment into CPS systems. These systems will be the basic building block of the so-called “smart factories”, and will be able to exchange information autonomously, trigger the necessary actions in response to current conditions and mutually independent inspections. The aim of this article is to describe the issue of readiness models for the Industry 4.0 concept, which are commonly used as tools for conceptualizing and measuring the maturity of an organization or process related to a specific target state. Characteristic for the models is their use because, on this basis, it is possible to identify the current readiness for the concept of Industry 4.0 comprehensively in the whole company or in various sub-areas.

Falsification of Cyber-Physical Systems Using Deep Reinforcement Learning

A <i>Cyber-Physical System</i> (CPS) is a system which consists of software components and physical components. Traditional system verification techniques such as model checking or theorem proving are difficult to apply to CPS because the physical components have infinite number of states. To solve this problem, robustness guided falsification of CPS is introduced. Robustness measures how robustly the given specification is satisfied. Robustness guided falsification tries to minimize the robustness by changing inputs and parameters of the system. The input with a minimal robustness (counterexample) is a good candidate to violate the specification. Existing methods use several optimization techniques to minimize robustness. However, those methods do not use temporal structures in a system input and often require a large number of simulation runs to minimize the robustness. In this paper, we explore state-of-the-art <i>Deep Reinforcement Learning</i> (DRL) techniques, i.e., <i>Asynchronous Advantage Actor-Critic</i> (A3C) and <i>Double Deep Q Network</i> (DDQN), to reduce the number of simulation runs required to find such counterexamples. We theoretically show how robustness guided falsification of a safety property is formatted as a reinforcement learning problem. Then, we experimentally compare the effectiveness of our methods with three baseline methods, i.e., random sampling, cross entropy and simulated annealing, on three well known CPS systems. We thoroughly analyse the experiment results and identify two factors of CPS which make DRL based methods better than existing methods. The most important factor is the availability of the system internal dynamics to the reinforcement learning algorithm. The other factor is the existence of learnable structure in the counterexample.

Design of Scalable IoT Architecture Based on AWS for Smart Livestock.

Simple SummaryDue to the growing number of connected IoT devices, the scalability capacity and available computing power of the existing architectural frameworks would be reached. This necessitates finding a solution that meets the growing demands. Cloud-based IoT is becoming an increasingly popular and desirable solution. This work presents a specially designed architecture based on Amazon Web Services (AWS) for monitoring livestock using cyber–physical systems (CPS) and Internet of things (IoT) equipment and a wide range of cloud native services. Used services in AWS cloud are described in detail and their tasks according to the application area are clarified. A stress test to prove the ability of the developed architecture for data processing was completed. Experimental results showed that the proposed architecture with the services provided by Amazon is fully capable of processing the required amount of data and allows the CPS/IoT infrastructure to use automated scaling mechanisms.In the ecological future of the planet, intelligent agriculture relies on CPS and IoT to free up human resources and increase production efficiency. Due to the growing number of connected IoT devices, the maximum scalability capacity, and available computing power of the existing architectural frameworks will be reached. This necessitates finding a solution that meets the continuously growing demands in smart farming. Cloud-based IoT solutions are achieving increasingly high popularity. The aim of this study was to design a scalable cloud-based architecture for a smart livestock monitoring system following Agile methodology and featuring environmental monitoring, health, growth, behaviour, reproduction, emotional state, and stress levels of animals. The AWS services used, and their specific tasks related to the proposed architecture are explained in detail. A stress test was performed to prove the data ingesting and processing capability of the proposed architecture. Experimental results proved that the proposed architecture using AWS automated scaling mechanisms and IoT devices are fully capable of processing the growing amount of data, which in turn allow for meeting the required needs of the constantly expanding number of CPS systems.

Application of double loop electrochemical potentio-kinetic reactivation for characterizing the intergranular corrosion susceptibility of stainless steels and Ni-based alloys in solar nitrate salts used in CSP systems

In this study, the sensitivity of four corrosion resistant candidate alloys; austenitic stainless steels (AISI 321 and AISI 347) and Ni-based alloys (IN 625 and In 825) to degrade by intergranular corrosion when used with molten salts in conditions of CSP systems were investigated at different temperatures. This was achieved by implementing the Double loop electrochemical potentiokinetic reactivation (DL-EPR) technique to quantitatively measure the degree of sensitisation (DOS) in the selected alloys by aging in air with and without Solar Salts (60 wt% NaNO3 and 40 wt% KNO3) at 550–750 °C for 2 h. Electrochemical etching technique was implemented further to verify the results from DL-EPR. The results show that DL-EPR and microstructural evaluation techniques can be used to assess the synergy of temperature and molten salt electrochemical activities on the tendency for intergranular corrosion attack to occur in CPS systems. IN 625 showed the highest intergranular corrosion resistance among the alloys investigated in both air and molten salt; especially at temperatures higher than salt stability temperature. Samples aged in molten salts showed higher DOS than samples aged in air across all temperatures and linked to Cr-consuming corrosion and chemical reactions that drives molten salt – alloy interaction in CSP plants.

ARTINALI++: Multi-dimensional Specification Mining for Complex Cyber-Physical System Security

Cyber-Physical Systems (CPSes) have been investigated as a key area of research since they are the core of Internet of Things. CPSs integrate computing and communication with control and monitoring of entities in the physical world. Due to the tight coupling of cyber and physical domains, and to the possible catastrophic consequences of the malicious attacks on critical infrastructures, security is one of the key concerns. However, the exponential growth of IoT has led to deployment of CPSes without support for enforcing important security properties. Specification-based Intrusion Detection Systems (IDS) have been shown to be effective for securing these systems. Mining the specifications of CPSes by experts is a cumbersome and error-prone task. Therefore, it is essential to dynamically monitor the CPS to learn its common behaviors and formulate specifications for detecting malicious bugs and security attacks. Existing solutions for specification mining only combine data and events, but not time. However, time is a semantic property in CPS systems, and hence incorporating time in addition to data and events, is essential for obtaining high accuracy.This paper proposes ARTINALI++, which dynamically mines specifications in CPS systems with arbitrary size and complexity. ARTINALI++ captures the security properties by incorporating time as a substantial property of the system, and generate a multi-dimensional model for the general CPS systems. Moreover, it enhances the model through discovering invariants that represent the physical motions and distinct operational modes in complex CPS systems. We build Intrusion Detection Systems based on ARTINALI++ for three CPSes with various levels of complexity including smart meter, smart artificial pancreas and unmanned aerial vehicle, and measure their detection accuracy. We find that the ARTINALI++ significantly reduces the ratio of false positives and false negatives by 23.45% and 73.6% on average, respectively, over other dynamic specification mining tools on the three CPS platforms.

ARTINALI#: An Efficient Intrusion Detection Technique for Resource-Constrained Cyber-Physical Systems

Cyber-Physical Systems (CPSes) are integrated into security-critical infrastructures such as medical devices, autonomous vehicles and smart grids. Unfortunately, the pervasiveness and network accessibility of these systems and their relative lack of security measures make them attractive targets for attacks. This makes building Intrusion Detection System (IDS) for CPSes a necessity. However, detecting intrusions requires collecting information about a system’s internal workings; this can be expensive both in runtime and memory consumption. According to prior research, fine-grain monitoring of a CPS maximizes the chance of intrusion detection but incurs overhead that can exceed the resource constraints of these systems. The objective of this study is to propose a solution for adapting IDSes for deployment on resource-limited CPSes without losing detection accuracy.We propose ARTINALI#; a Bayesian-based search and score technique that identifies the critical points at which to instrument a CPS. Given a set of security monitors that observe run-time behavior of the system, a set of specifications that verify the correct behavior of the system, and statistics gathered from fault injection, ARTINALI# discovers a small set of locations and a rich set of specifications that yield full attack coverage with low (memory and time) overhead. We deploy ARTINALI# to construct an IDS for two CPSes: a smart meter and a smart artificial pancreas. We demonstrate that our technique reduces the number of security monitors by 64% on average, leading to 52% and 69% reductions in memory and runtime overhead respectively, while still detecting over 98% of emulated attacks, on average. ARTINALI# enables the IDSes to be applicable to a wide range of CPS systems with different resource capacities. In addition, it accelerates the attack detection process which is significantly essential for safety-critical systems.

Replacing CPS: Issues in Building an Alternative System

There is now widespread agreement that the child protection system in all states needs major reform, There is consensus that current policies and services regarding maltreatment are not meeting the needs of many children. Given the failures of past reform efforts, there is a movement for substantially limiting the jurisdiction of CPS. Some commentators call for abolishing CPS entirely, which is seen as reflecting racial bias at its core, harming the children it seeks to help, undermining poor communities, and functioning in negative ways that are not amenable to reform. In this article, I examine the outcomes that should be the focus of government policies regarding child well-being. I then look at the proper role for CPS systems in achieving these outcomes, arguing that CPS intervention should be limited to situations involving parental behaviors that cause or threaten severe harm and may be repeated. These constitute less than twenty percent of the cases now seen by CPS; CPS should not be involved in at least 80 percent of the situations currently investigated by CPS agencies. Reducing the role of CPS should not, however, reduce efforts to help families where the parents are unable to provide the types of parenting needed to protect and promote the basic emotional and social development of children The article proposes a new system for helping parents and protecting children in most of the situations now referred to CPS. However, implementing alternative approaches is very challenging. The article examines a range of issues that must be addressed in designing a new system.

Reinforcement Learning for IoT Security: A Comprehensive Survey

The number of connected smart devices has been increasing exponentially for different Internet-of-Things (IoT) applications. Security has been a long run challenge in the IoT systems which has many attack vectors, security flaws and vulnerabilities. Securing billions of B connected devices in IoT is a must task to realize the full potential of IoT applications. Recently, researchers have proposed many security solutions for IoT. Machine learning has been proposed as one of the emerging solutions for IoT security and Reinforcement learning is gaining more popularity for securing IoT systems. Reinforcement learning, unlike other machine learning techniques, can learn the environment by having minimum information about the parameters to be learned. It solves the optimization problem by interacting with the environment adapting the parameters on the fly. In this paper, we present an comprehensive survey of different types of cyber-attacks against different IoT systems and then we present reinforcement learning and deep reinforcement learning based security solutions to combat those different types of attacks in different IoT systems. Furthermore, we present the Reinforcement learning for securing CPS systems (i.e., IoT with feedback and control) such as smart grid and smart transportation system. The recent important attacks and countermeasures using reinforcement learning B in IoT are also summarized in the form of tables. With this paper, readers can have a more thorough understanding of IoT security attacks and countermeasures using Reinforcement Learning, as well as research trends in this area.

Mega-modeling of complex, distributed, heterogeneous CPS systems

Model-Driven Design (MDD) has proven to be a powerful technology to address the development of increasingly complex embedded systems. Beyond complexity itself, challenges come from the need to deal with parallelism and heterogeneity. System design must target different execution platforms with different OSs and HW resources, even bare-metal, support local and distributed systems, and integrate on top of these heterogeneous platforms multiple functional component coming from different sources (developed from scratch, legacy code and third-party code), with different behaviors operating under different models of computation and communication. Additionally, system optimization to improve performance, power consumption, cost, etc. requires analyzing huge lists of possible design solutions. Addressing these challenges require flexible design technologies able to support from a single-source model its architectural mapping to different computing resources, of different kind and in different platforms. Traditional MDD methods and tools typically rely on fixed elements, which makes difficult their integration under this variability. For example, it is unlikely to integrate in the same system legacy code with a third-party component. Usually some re-coding is required to enable such interconnection. This paper proposes a UML/MARTE system modeling methodology able to address the challenges mentioned above by improving flexibility and scalability. This approach is illustrated and demonstrated on a flight management system. The model is flexible enough to be adapted to different architectural solutions with a minimal effort by changing its underlying Model of Computation and Communication (MoCC). Being completely platform independent, from the same model it is possible to explore various solutions on different execution platforms.

Water and fertilizer efficiency in a polyculture cropping system under three production systems

Abstract Approximately 40% of the water used in intensive agriculture is discarded as ‘drainage,’ which contains high amounts of ions that pollute the environment. This work aimed at investigating polyculture (tomatoes, cucumbers, and lettuce) under three production systems, namely, open (OPS), soil (SPS), and closed (CPS), in which the drainage water from the first crop was used to feed the second crop, and the water from the second crop was used to feed the third crop. The water and fertilizer efficiencies and some physical–chemical properties of the plants and soil were measured. The results showed no significant difference in the yield for polycultures between the CPS and OPS systems. The most efficient system for water use was the CPS, with 54.85 kg m−3, with a water savings of 55.69% compared to the OPS. The efficiency of fertilizers, such as N, P, K, Ca, and Mg, was statistically higher in the CPS, providing more kilograms of fruit per kilogram of nutrients. The reuse of drainage water in a polyculture not only increased the efficiency of the water and fertilizers but also increased the yields produced per cubic meter of water used, thereby minimizing environmental contamination.

Security Analysis of CPS Systems Under Different Swapping Strategies in IoT Environments

In this paper, we give attention to the robustness of the Cyber-Physical System, which consists of interdependent physical resources and computational resources. Numerous infrastructure systems can evolve into the Cyber-Physical System, e.g., smart power grids, traffic control systems, and wireless sensor and actuator networks. These networks depend on their interdependent networks, which provide information or energy to function. In a Cyber-Physical System, a small failure could trigger serious cascading failures within the entire interdependent networks. In this paper, we try to alleviate these cascading failures between interdependent networks to reduce losses. We discuss the robustness of systems for random attacks by calculating the size of functioning components in entire networks. We change the inter-links topology of the coupled networks to enhance the reliability of the entire system. Then we get the most effective swapping strategy in enhancing the robustness of the Cyber-Physical System compared to previous studies. Different systems’ structures would influence the performance of swap inter links strategies on improving the reliability of networks. Moreover, our work could guide how to optimize a Cyber-Physical System topology by reducing the influence of cascading failures.

The Effects of Child Protective Services and Juvenile Justice System Involvement on Academic Outcomes: Gender and Racial Differences

The primary aim of this study was to examine gender and racial differences in the association between system involvement types (i.e., child protective services [CPS] only, juvenile justice system only, and dual involvement) and academic outcomes (i.e., grade failure, chronic absenteeism). This study used records from a linked database of public youth-serving institutional records within a county’s youth court, law enforcement agencies, and CPS. Multivariate logistic regression analyses were performed. The impact of systems involvement on academic outcomes was most salient among Black males, while none of the system involvement types were associated with chronic absenteeism or grade failure among White males. Findings highlight the need for the development of intervention strategies to address educational needs of youth involved in the CPS and juvenile justice systems, with a heightened attention to Black and male youth, to improve their attendance and academic performance.

A literature review on obsolescence management in COTS-centric cyber physical systems

Commercial off-the-shelf (COTS)-centric cyber physical systems often contain software and hardware elements with life-spans shorter than the systems’ intended life-span. Various studies have examined hardware obsolescence, although in most systems, software costs contribute as much, or more, to the total life cycle costs than hardware. The aim of our research effort is to explore, synthesize, and compile past research efforts on obsolescence in the context of COTS-based systems, and propose new ways to overcome related issues. This research effort suggests the need for systematic perspectives to streamline potentially overbearing acquisition processes while focusing on core critical aspects affecting systems sustainment and cost. Significant life cycle costs associated with obsolescence mitigation approaches, therefore, programmatic strategic planning should be adapted to include the context of obsolescence with the objective to improve the efficiency of new COTS-intensive CPS systems with enduring perspectives. The study reveals opportunities and challenges for obsolescence in COTS-based CPSs.

Testing and Validation of Modbus/TCP Protocol for Secure SCADA Communication in CPS using Formal Methods

Cyber-Physical Systems (CPS's) evident representation is Supervisory Control, and Data Acquisition (SCADA). As SCADA is being refurbished with advanced computing and communication technologies, the risk involved in adopting/updating to new technology needs to be validated and verified thoroughly. One of the greatest challenges is security testing of protocols. All CPS systems being live and attached to physical process can not be scheduled for penetration testing and verification. This paper presents design and implementation of industrial compliant SCADA test bed, the formal analysis of semantics and security of Modbus/TCP protocol using Coloured Petri Nets(CPN) tool. A novel method is proposed to differentiate attack vector by identifying influential nodes using formal concept analysis. Modbus/TCP conceptualized attack from analysis is tested and verified on the test bed.

Software Engineering for Smart Cyber-Physical Systems

Smart Cyber--Physical Systems (sCPS) are modern CPS systems that are engineered to seamlessly integrate a large number of computation and physical components; they need to control entities in their environment in a smart and collective way to achieve a high degree of effectiveness and efficiency. At the same time, these systems are supposed to be safe and secure, deal with environment dynamicity and uncertainty, cope with external threats, and optimize their behavior to achieve the best possible outcome. This "smartness" typically stems from highly cooperative behavior, self--awareness, self--adaptation, and selfoptimization. Most of the "smartness" is implemented in software, which makes the software one of the most complex and most critical constituents of sCPS. As the specifics of sCPS render traditional software engineering approaches not directly applicable, new and innovative approaches to software engineering of sCPS need to be sought. This paper reports on the results of the Second International Workshop on Software Engineering for Smart Cyber--Physical Systems (SEsCPS 2016), which specifically focuses on challenges and promising solutions in the area of software engineering for sCPS.