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Existing formal languages for the specification of self-adaptive cyber-physical systems focus on re-configuring the system-to-be depending on its current context, to satisfy the user’s requirements, that is by dynamically composing the software’s structure and behavior. While these approaches specify context-sensitive requirements, they rarely consider their run-time dynamic and scalable nature. The State-Constraint Transition (SCT) modeling language, introduced in this paper, provides an answer to the problems linked to the specification of dynamic requirements by introducing the concept of configuration states, in which requirements are translated into constraints. The expressiveness of existing approaches is thus extended, combining the ease of use of well-established notations, notably those based on characteristics, and those based on Finite-state Machines (FSM), with the computational power and expressiveness of the constraint programming approach. The paper briefly presents the results of the preliminary evaluation, which assesses the expressiveness, scalability, and domain independence of the SCT language.

Nowadays, data collection has become an activity inherent in the emergence of any organization. The digital age has enabled the development of mobile data collection apps that are becoming increasingly common around the world. But faced with the growing number of apps offered, Data Managers are often challenged by with the choice of the solution that best suits their case.This study meets this need by providing clear, precise and verified information on each of the selected solutions. The study presents, analyzes and compares four mobile data collection solutions. To achieve an effective comparison, we first chose to collect and select papers on each of the solutions, and then to install and test each of them by executing a data collection process, all the way from the form creation to the visualization of collected data. The comparison presented in this paper was based on technical aspects but also on other important aspects to help users make a good decision.

Nowadays, cybersecurity data management has become a challenging issue especially with the emergence of Big Data. This paper introduces the System of Systems (SoS) paradigm to design a new generation SIEM POC (Security Information Event Management Proof Of Concept) made up of an open source Big Data platform ELK and integrated with other open source security and load-balancing tools. To do this, we first focused on the Big Data and Smart Data requirements to model a data lifecycle from the literature named Smart DLC to the System of 7 Systems, So7S. Second, we used the proposed cycle as SoS tools design, implement and test the proposed SIEM POC by matching the cybersecurity tools to each system of the SoS modeled. The proposed open source SIEM is operational and meets all cybersecurity monitoring requirements with challenging results and may interest small and medium-sized companies dealing with cybersecurity issues.

Being a socio-economic lever, the digital transformation presents several opportunities to companies of any sector. Nevertheless, aspiring to digitalization, companies are faced with several challenges, the main one is, the digitalization process. In lack of a digital strategy, companies tend to equate digitalization with IT integration, which implies confusion between the digitalization process and the IT integration process. The paper’s aim is to question this observation, and call for a reorientation of company’s digitalization vision from technology to strategy. In this paper, we contrast a study on the dimensions of digital transformation, supported by a survey on the strategic orientations of digital transformation, and the practices on the ground of this transformation, in order to highlight; the widespread confusion between digital transformation and ICT integration and the limitations of an IT-oriented understanding of the digital transformation.

To evaluate and improve the delivery of health care services and programs; to collect and share information on agro-biodiversity; to fill the gaps in the data of artisanal or industrial fisheries, the use of computer tools of data collection is a major asset. It is now obvious that most of these tools are difficult to use in developing countries. And this is because of the complexity of use, either because they are commercial solutions or simply because these tools require a stable internet connection. This study is the result of thorough research on Open Data Kit (ODK), a suite of tools fully compatible with the context of developing countries and fully open source software. This article traces the research work done for the design, implementation, and improvement of ODK. A description of the different ODK extensions that can be found in the literature is given in addition to the reasons that explain the success of ODK in developing countries. To highlight the utility of ODK, an analysis of the areas of use of ODK has been made. This analysis concludes that ODK is mainly used to improve the intervention effectiveness of health system agents in developing countries.

Several organizations from different sectors depend increasingly on knowledge extracted from huge volumes of data generated by different sources, such as IoT, sensors and databases. At the core of data lifecycle, data reliability, analytics, security, scalability and use are important concerns. Coping with these issues in handling data requires understanding the challenges associated with it. Analysis process and storage devices have been widely studied. However, very few studies have explored the collect data phase. In this study we aim to analyse more the collect phase of data lifecycle to provide an optimized and smart approach. This paper aim to provide the right method to follow in data collect phase within different domain according to client needs and requirements. It provides not only a detailed view of the main steps, but also based on a prior literature review on different existing methods. This allowed us subsequently to establish a correspondence with the SLR method on which we based our method. We use an explicit example to illustrate the steps of our method.

This paper aims to build a generic roadmap for implementing Lean in different domains in order to improve the business processes. The customization of this roadmap makes it possible to adapt the steps to be followed for Lean implementation, the process manager’s requirements and available resources. An application has been developed in order to facilitate the customization of this roadmap. An analysis of methods from a literature review for Lean implementation was conducted in order to identify the steps to be included in the roadmap. A rigorous approach based on set theory, was therefore proposed and followed to build the roadmap. This roadmap is defined as a set of phases associated with principles, and each phase is defined as a set of steps associated with tools. This roadmap shows to the process manager, the objectives to aim for, the Lean principles to satisfy, and the Lean tools to use.

Risk management is defined as an essential step in the improvement process proposed under the standard ISO 9001: 2015. The guidelines to the process of risk management have been proposed since 2009 by the International Standardization Organization (ISO). But before, organization and institutions have their own way to manage risk. An analysis of risk management processes in the literature has been realized in order to identify the standard process. Unified Modeling language (UML) was used to present this meta model. The purpose of this paper is to propose a risk management process model based on ISO 31000: 2018 and following the recommendations of ISO 9001: 2015 by integrating risk management modeling in process modeling.

Data collection is one of the first and main phases of the data life cycle. It enables improvements to be made across all phases of the data lifecycle. In this sense, we have proposed a data collection process qualified as Smart. For our smart data collection process, we have adopted the principles of the smart data approach allowing less data to be transmitted to the analysis and storage processes, while maintaining better data quality. In addition, we also used Edge computing since it provides services with faster response and better quality, compared to cloud computing. To experiment this process on mobile data, we propose to extend a mobile data collection software solution and adopt one of the key data collection methods. In this paper, we tested our smart data collection process via the ODK-X software suite and were able to identify the added value of our process compared to the one used by default during collection.

African swine fever (ASF), dengue fever, influenza, norovirus disease are names of outbreaks that have taken some parts of the world by surprise and caused several deaths. The advent of coronavirus disease at the end of 2019 is clear proof that the issue of outbreaks is more topical than ever. The Machine Learning (ML) comes to the rescue of medicine by offering predictive analysis methods to predict the reappearance of outbreaks and thus help the healthcare system and decisionmakers to take the necessary measures in advance. But which ML method should be used to predict an outbreak? Given the large number of ML methods, it is sometimes difficult to know which method to use. This is why we conducted this study. We looked at the above-mentioned outbreaks and identified the following ML methods as the most commonly used: LR, C4.5, NB, SVM, ANN and RF. Of these six methods, the random forest method (RF) stands out from the others because it was used in all four outbreaks considered, has the best accuracy for two outbreaks (ASF and dengue fever) and has been designated by several independent researchers as the most suitable ML method for outbreak prediction.

In the last five years, we witnessed the shift from the vision of the internet of things (IoT) to an actual reality. It is currently shifting again from specific and single applications to larger and more generic ones, which serves the needs of thousands of users across borders and platforms. To avoid losing the personification of applications, on account of genericity, new approaches and languages that use generic knowledge as a steppingstone while taking into consideration users and context's specific and evolutive needs are on the rise. This chapter aims to provide a framework to support the creation of such approaches (DSPL4IoT). It is later on used to assess notable IoT specification approaches and extract conclusion of the trends and persistent challenges and directions. An approach for the specification of natural language (NL) requirements for IoT systems is also provided to assist domain and application engineers with the formulations of such requirements.

The Internet of Things (IoT) enables interconnection between different types of devices using information and communication technologies. This interconnection generates a large amounts of data that require transmission, analysis and storage. The benefit of Cloud Computing has extended the capacities of devices since they are limited in terms of storage capacity and computing power. In order to ensure the integration between IoT and Cloud Computing, different architectures of an IoT system in a cloud environment have been proposed. The purpose of this article is to present these architectures, expose a synthesis of them to find out their emerging trends, and also to identify the layers that are common to them. The results presented are mainly the identification of these layers, and the emerging trends in IoT systems in a cloud environment, which aim mainly to bring the cloud resources more closely to devices, in order to reduce latency.

Supervision of a complex system involves studying and analyzing its functioning as well as a continuous search for its improvement and optimization. Simulation in general, and discrete event simulation in particular, is an appropriate tool to support the various management policies and strategies. In order to carry out a simulation project, particular importance must be given to the simulation process, which prompted us in this paper to ask the following question: is there a life cycle of a project of simulation or a referential of steps to follow? In this paper, we have used the simulation steps most cited in the literature to develop a life cycle for a simulation project called SPLC (Simulation Project Life Cycle) grouping the main steps. Then, we were interested in the analysis of the level of use of these different steps by the simulation processes, described in the literature review, to conduct a simulation project.