Manufacturing industry profoundly impact economic and societal progress. As being a commonly accepted term for research centers and universities, the Industry 4.0 initiative has received a splendid attention of the business and research community. Although the idea is not new and was on the agenda of academic research in many years with different perceptions, the term “Industry 4.0” is just launched and well accepted to some extend not only in academic life but also in the industrial society as well. While academic research focuses on understanding and defining the concept and trying to develop related systems, business models and respective methodologies, industry, on the other hand, focuses its attention on the change of industrial machine suits and intelligent products as well as potential customers on this progress. It is therefore important for the companies to primarily understand the features and content of the Industry 4.0 for potential transformation from machine dominant manufacturing to digital manufacturing. In order to achieve a successful transformation, they should clearly review their positions and respective potentials against basic requirements set forward for Industry 4.0 standard. This will allow them to generate a well-defined road map. There has been several approaches and discussions going on along this line, a several road maps are already proposed. Some of those are reviewed in this paper. However, the literature clearly indicates the lack of respective assessment methodologies. Since the implementation and applications of related theorems and definitions outlined for the 4th industrial revolution is not mature enough for most of the reel life implementations, a systematic approach for making respective assessments and evaluations seems to be urgently required for those who are intending to speed this transformation up. It is now main responsibility of the research community to developed technological infrastructure with physical systems, management models, business models as well as some well-defined Industry 4.0 scenarios in order to make the life for the practitioners easy. It is estimated by the experts that the Industry 4.0 and related progress along this line will have an enormous effect on social life. As outlined in the introduction, some social transformation is also expected. It is assumed that the robots will be more dominant in manufacturing, implanted technologies, cooperating and coordinating machines, self-decision-making systems, autonom problem solvers, learning machines, 3D printing etc. will dominate the production process. Wearable internet, big data analysis, sensor based life, smart city implementations or similar applications will be the main concern of the community. This social transformation will naturally trigger the manufacturing society to improve their manufacturing suits to cope with the customer requirements and sustain competitive advantage. A summary of the potential progress along this line is reviewed in introduction of the paper. It is so obvious that the future manufacturing systems will have a different vision composed of products, intelligence, communications and information network. This will bring about new business models to be dominant in industrial life. Another important issue to take into account is that the time span of this so-called revolution will be so short triggering a continues transformation process to yield some new industrial areas to emerge. This clearly puts a big pressure on manufacturers to learn, understand, design and implement the transformation process. Since the main motivation for finding the best way to follow this transformation, a comprehensive literature review will generate a remarkable support. This paper presents such a review for highlighting the progress and aims to help improve the awareness on the best experiences. It is intended to provide a clear idea for those wishing to generate a road map for digitizing the respective manufacturing suits. By presenting this review it is also intended to provide a hands-on library of Industry 4.0 to both academics as well as industrial practitioners. The top 100 headings, abstracts and key words (i.e. a total of 619 publications of any kind) for each search term were independently analyzed in order to ensure the reliability of the review process. Note that, this exhaustive literature review provides a concrete definition of Industry 4.0 and defines its six design principles such as interoperability, virtualization, local, real-time talent, service orientation and modularity. It seems that these principles have taken the attention of the scientists to carry out more variety of research on the subject and to develop implementable and appropriate scenarios. A comprehensive taxonomy of Industry 4.0 can also be developed through analyzing the results of this review.

Literature review of Industry 4.0 and related technologies

There has been a tremendous increase in research in the area of cyber security to support cyber applications and to avoid key security threats faced by these applications. The goal of this study is to identify and analyze the common cyber security vulnerabilities. To achieve this goal, a systematic mapping study was conducted, and in total, 78 primary studies were identified and analyzed. After a detailed analysis of the selected studies, we identified the important security vulnerabilities and their frequency of occurrence. Data were also synthesized and analyzed to present the venue of publication, country of publication, key targeted infrastructures and applications. The results show that the security approaches mentioned so far only target security in general, and the solutions provided in these studies need more empirical validation and real implementation. In addition, our results show that most of the selected studies in this review targeted only a few common security vulnerabilities such as phishing, denial-of-service and malware. However, there is a need, in future research, to identify the key cyber security vulnerabilities, targeted/victimized applications, mitigation techniques and infrastructures, so that researchers and practitioners could get a better insight into it.

Cyber Security Threats and Vulnerabilities: A Systematic Mapping Study

The complexity of information systems is increasing in recent years, leading to increased effort for maintenance and configuration. Self-adaptive systems (SASs) address this issue. Due to new computing trends, such as pervasive computing, miniaturization of IT leads to mobile devices with the emerging need for context adaptation. Therefore, it is beneficial that devices are able to adapt context. Hence, we propose to extend the definition of SASs and include
context adaptation. This paper presents a taxonomy of self-adaptation and a survey on engineering SASs. Based on the taxonomy and the survey, we motivate a new perspective on SAS including context adaptation.

A survey on engineering approaches for self-adaptive systems (extended version)

Code smells are indicators of potential problems in software. They tend to have a negative impact on software quality. Several studies use machine learning techniques to detect bad smells. The objective of this study is to systematically review and analyze machine learning techniques used to detect code smells to provide interested research community with knowledge about the adopted techniques and practices for code smells detection. We use a systematic literature review approach to review studies that use machine learning techniques to detect code smells. Seventeen primary studies were identified. We found that 27 code smells were used in the identified studies; God Class and Long Method, Feature Envy, and Data Class are the most frequently detected code smells. In addition, we found that 16 machine learning algorithms were employed to detect code smells with acceptable prediction accuracy. Furthermore, we the results also indicate that support vector machine techniques were investigated the most. Moreover, we observed that J48 and Random Forest algorithms outperform the other algorithms. We also noticed that, in some cases, the use of boosting techniques on the models does not always enhance their performance. More studies are needed to consider the use of ensemble learning techniques, multiclassification, and feature selection technique for code smells detection. Thus, the application of machine learning algorithms to detect code smells in systems is still in its infancy and needs more research to facilitate the employment of machine learning algorithms in detecting code smells.

Bad Smell Detection Using Machine Learning Techniques: A Systematic Literature Review

Developing Internet of Things (IoT) systems has to cope with several challenges mainly because of the heterogeneity of the involved sub-systems and components. With the aim of conceiving languages and tools supporting the development of IoT systems, this paper presents the results of the study, which has been conducted to understand the current state of the art of existing platforms, and in particular low-code ones, for developing IoT systems. By analyzing sixteen platforms, a corresponding set of features has been identified to represent the functionalities and the services that each analyzed platform can support. We also identify the limitations of already existing approaches and discuss possible ways to improve and address them in the future.

/pdf/low-code-engineering-for-internet-of-things-a-state-of-2dj8kptaig.pdf

Low-code engineering for internet of things: a state of research

This paper proposes CAPS, an architecture-drivenmodeling framework for the development of Situational AwareCyber-Physical Systems Situational Awareness involves being aware of what ishappening in the surroundings, and using this informationto decide and act It has been recognized as a critical, yet often elusive, foundation for successful decision-makingin complex systems With the advent of cyber-physical systems(CPS), situational awareness is playing an increasinglyimportant role especially in crowd and fleets management, infrastructure monitoring, and smart city applications Whilespecializing cyber physical systems, Situational Aware CPSrequires the continuous monitoring of environmental conditionsand events with respect to time and space New architecturalconcerns arise, especially related to the sense, compute &communication paradigm, the use of domain-specific hardwarecomponents, and the cyber-physical space dimension This work illustrates the CAPS modeling languages usedto describe the software architecture, hardware configuration, and physical space views for a situational aware CPS

CAPS: Architecture Description of Situational Aware Cyber Physical Systems

Along with the rapid growth of IoT technologies and devices, their solutions are currently being applied on various domains such as health-care, transportation and agriculture, but mainly on crowd monitoring and emergency handling. The latter is a safety critical IoT system based on collecting and analyzing the real-time data to perform proper actuation. In order to engineer such a high quality IoT application, a proper software architecture should be designed. In order for the software architecture to be able to optimize critical requirements such as fault-tolerance, performance and energy consumption, it ought to: i) adapt itself to real-time environment transformation, ii) be designed in a proper level of elements distribution. In this paper, we critically analyze a set of IoT distribution and self-adaptation patterns to identify their suitable architectural combinations. Further, we use our IoT modeling framework (CAPS) to model an emergency handling system. Based on these, we design two quality driven architectures to be used for a forest monitoring and evacuation example and qualitatively evaluate and compare them.

Self-adaptive IoT architectures: an emergency handling case study

Situational Aware (SiA) Cyber-physical systems (CPS) harmoniously integrate computational and physical components to being aware of what is happening in the surroundings and using this information to decide and act. Architecture description of SiA-CPS can be a valuable tool to reason about the selected solutions, and to enable code generation and simulation. This paper presents an architecture framework that automatically generates from a SiA-CPS architecture description, an executable code used to simulate the architecture model and evaluate it in terms of data traffic load, battery level and energy consumptions. The framework makes use of a model transformation approach where, three SiA-CPS domain-specific modeling views are automatically transformed into the input language of CupCarbon, an open source tool supporting the simulation of sensor network architectures.

An Architecture Framework for Modelling and Simulation of Situational-Aware Cyber-Physical Systems

Cyber-physical systems (CPSs) are deemed as the key enablers of next generation applications. Needless to say, the design, verification and validation of cyber-physical systems reaches unprecedented levels of complexity, specially due to their sensibility to safety issues. Under this perspective, leveraging architectural descriptions to reason on a CPS seems to be the obvious way to manage its inherent complexity.A body of knowledge on architecting CPSs has been proposed in the past years. Still, the trends of research on architecting CPS is unclear. In order to shade some light on the state-of-the art in architecting CPS, this paper presents a preliminary study on the challenges, goals, and solutions reported so far in architecting CPSs.

/pdf/a-preliminary-study-on-architecting-cyber-physical-systems-q1ksx6oig6.pdf

A Preliminary Study on Architecting Cyber-Physical Systems

In the Internet of Things (IoT) every physical device has an embedded technology that interacts with internal and external states. The heterogeneity of devices and networks complicates the mission of implementing and integrating the objects in IoT systems. In this paper, we present our model driven code generation framework, called CAPSml. The framework enables IoT designers and architects who are using CAPS environment to transform CAPS software model into ThingML model. CAPS is an architecture-driven modeling framework for the development of IoT Systems. ThingML includes modeling language and framework designed for IoT systems to support code generation for multi-platform targets.

Modeling and Code Generation Framework for IoT

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