LUCON: Data Flow Control for Message-Based IoT Systems

Process-driven applications evolve rapidly through the interaction between executable BPMN (Business Process Modeling and Notation) models, business tasks, and external services. Given these components operate on some shared process data, it is imperative to recover the latent data by visiting relation, which is known as data flow among these tasks. Data flow will benefit some typical applications including data flow anomaly checking and data privacy protection. However, in most cases, the complete data flow in a business process is not explicitly defined but hidden in model elements such as form declarations, variable declarations, and program code. Some methods to recovering data flow based on process model analysis of source code have some drawbacks; i.e., for security reasons, users do not want to provide source code but only encapsulated methods; therefore, data flows are difficult to analyze. We propose a method to generate running logs that are used to produce a complete data flow picture combined with the static code analysis method. This method combines the simple and easy-to-use characteristics of static code analysis methods and makes up for the shortcomings of static code analysis methods that cannot adapt to complex business processes, and as a result, the analyzed data flow is inaccurate. Moreover, a holistic framework is proposed to generate the data flow graph. The prototype system designed on Camunda and Flowable BPM (business process management) engine proves the applicability of the solution. The effectiveness of our method is validated on the prototype system.

The Constant Murmur of Data

The trend towards intelligent control processes has introduced the Internet of Things (IoT) and cloud computing technologies to factories. IoT devices can sense data and send it to a cloud for further analysis in a factory. Consequently, the quantity of such valuable data flowing in an industrial cyber‐physical system has gradually increased. Tailoring a risk assessment system for Industrial IoT (IIoT) is essential, particularly for a cloud platform that handles the IIoT data flow. In this study, we leverage analytic hierarchy processes (AHPs) and propose Hierarchical Risk Assessment Model (HiRAM) for an IIoT cloud platform. The proposed model allows the platform to self‐evaluate its security status. Furthermore, a modular and responsive Risk Assessment System based on HiRAM, called HiRAM‐RAS, is realized and evaluated in a real‐world IIoT cloud platform. We deploy HiRAM‐RAS to a sample application and introduce the practical deployment procedures. We then estimate the practicality of the HiRAM‐RAS by injecting different degrees of errors and launching Distributed denial‐of‐service (DDoS) attacks. The results demonstrate the changes in integrity and availability scores evaluated by HiRAM.

EU-UK Data Flows, Brexit and No-Deal: Adequacy or Disarray?

Cloud computing is synonym of high performance computing. It offers a very scalable infrastructure to deploy a huge number of systems and services without any impact of the performance. Such a wide distributed infrastructure, as for the traditional systems, requires authorization and access control mechanisms to ensure the basic security requirements to restrict the access to resources. To satisfy these requirements cloud providers still rely on the traditional authorization and access control systems that in some critical cases can generate performance issues. More the access control structure is complex (many authorization levels, many users and resources to protect), more the enforcement of the access control policies is slow. In this paper we propose a performance study for these traditional access control mechanisms demonstrating the overhead generated by the authorizations checking that can appear in extreme usage conditions. We propose a new approach to make access control systems more scalable and adapted to cloud computing high performance requirements. This approach is based on a high speed caching access control tree that accelerates the decision without impacting on the consistency of the rules. We compare the performance test results obtained by our solution to a traditional Access control system deployed in the cloud.

The Industrial Internet of Things (IIoT) plays an important role in Industry 4.0, but the existing IIoT systems could be vulnerable to a single point of failure and malicious attacks, failing to provide reliable services. IIoT devices have some particularities, such as mobility, limited performance, and distributed deployment, which are challenging to traditional centralized access control methods in the large-scale IIoT environment. To resolve the challenges, we propose an access control system for the Industrial Internet of Things. The system contains three smart contracts: device contract (DC), policy contract (PC), and access contract (AC). The device contract provides a method of storing the URL of the resource data generated by the equipment and a query method. The policy contract provides the function of managing the attribute-based access control (ABAC) of the administrator user. The access contract is the core program that implements the access control method for ordinary users. Combining ABAC and blockchain technology provides decentralized, fine-grained, and dynamic access control management for IIoT.

In this rapidly changing world, a new buzzword has lodged in our agenda for the last five years: Industry 4.0. A wide variety of definitions have been provided in order to specify this notion, but simply, Industry 4.0 could be defined as the adoption of automation and data exchange in manufacturing. When it comes to how to provide this automation and data flow, the Internet of things (IoT) stands at the centre of the system. It is possible to describe the IoT as the intelligent connectivity of smart devices where each item in the system can reach out and sense one another so they can provide decisions about our physical world. The intersection of the IoT and Industrial 4.0 composes an area of computational technology that is called the Industrial Internet of Things (IIoT), and it is applicable for industries to deal with connectivity for each element in the system. The IIoT increases efficiency and productivity while minimizing human-based errors in business processes. Companies can provide effective reporting by integrating this technology with their internal systems By creating an interface between devices connected to the Internet, the applicability of the data collected by IoT will increase and more flexibility and agility will be provided.Besides the applications in the manufacturing sector, the oil and gas, petrochemical, logistics, mining, aerospace, and pharmaceutical sectors also benefit from the IIoT since it provides real-time monitoring and data collection. The IoT continues to evolve and signals that business processes and operations in the industry will be smarter and more efficient in the future.

The article analyzes the existing methods of managing data flows in mobile radio communications. During the analysis, the peculiarities of the functioning and construction of these networks were considered. It has been established that such networks have features different from other networks, such as mobility, dynamic topology, independent organization, use of mobile means of communication, lack of fixed data transmission routes, etc. The task of providing routing in mobile radio communication devices, mobile radio networks, and the types of routing in communication networks are considered. The conditions for the implementation of the routing task and the features that characterize the routing process have been established. The levels of the OSI open systems model and the most well-known protocols that work at these levels and are used in data flow management are considered. Data flow management methods are classified by type and a list of requirements for the process of data flow transmission in mobile radio networks is defined, taking into account the peculiarities of the functioning of mobile radio communication means, which must be taken into account when building a mobile radio network management system. It has been established that at the current stage of information technology development, data flow management methods are mostly adapted for use in computer, wired or fixed networks and do not take into account the conditions of use in mobile radio communication devices. It has been established that the construction and operation of an effective data flow control subsystem in the control systems of mobile radio networks and mobile radio communication means requires the use of the latest technologies and modern approaches in the development of data flow control methods and methods that will ensure the functioning of the corresponding data flow control subsystem. When choosing protocols, methods, methods of managing data flows, it is proposed to focus on adaptation, modification of approaches or their combined use based on the target function, taking into account the peculiarities of the functioning of mobile radio networks and mobile radio communication means.

PurposeThe Internet of Things (IoT) provides exciting opportunities for the construction industry to solve its time and resource constraints and frequent defaults. This study seeks to identify and rank the perceived importance level of principal research areas associated with the IoT and the construction industry by utilising a scientific mapping tool (i.e. VOSviewer). Such knowledge would enable key drivers for successful adoption of the IoT and digitisation technologies to be outlined. An analysis of key drivers and research trends that facilitates the development of a roadmap for applying the IoT and digital technologies in the construction sector is therefore much needed.Design/methodology/approachAn interpretivist philosophical lens was adopted to analyse published work as secondary data, where each publication represented a unit of analysis. A total of 417 peer-reviewed journal review articles covering the IoT within the construction domain were systematically reviewed using a mixed-methods approach, utilising qualitative-scientometric analyses techniques.FindingsThe results revealed a field of study in a fledgling stage, with a limited number of experts operating somewhat in isolation and offering single-point solutions instead of taking an integrated “holistic” approach. Key publication outlets were identified and the main focus of research undertaken being in the technical areas of smart buildings, smart construction objects and environmental sustainability. The major effects of adopting the IoT within the construction industry were identified as high-speed reporting, complete process control, data explosion leading to deep data analytics, strict ethical and legal expectations. Key drivers of the IoT adoption were outlined: interoperability; data privacy and security; flexible governance structures; proper business planning and models.Practical implicationsThe study benefits researchers and industry practitioners alike. For researchers, the identified gaps reveal areas of high priority in future research. For construction companies, particularly small to medium-sized businesses, the study raises awareness of the latest developments and potential applicability of the IoT in the industry. For government agencies and policymakers, this study offers a point of reference in directing the adoption of the IoT smoothly in the construction sector and provides guidelines and standards for maximising the potential benefits.Originality/valueThe study is the first scientometric review of the existing body of knowledge in the context of application of the IoT in the construction industry. Findings expose knowledge gaps in contemporary research, specifically, a broader consideration of organisational adjustments needed to accommodate the IoT usage, economic analyses and impediments to wider acceptance.

The Industrial Internet of Things (IIoT) is a crucial research field spawned by the Internet of Things (IoT). IIoT links all types of industrial equipment through the network; establishes data acquisition, exchange, and analysis systems; and optimizes processes and services, so as to reduce cost and enhance productivity. The introduction of edge computing in IIoT can significantly reduce the decision-making latency, save bandwidth resources, and to some extent, protect privacy. This paper outlines the research progress concerning edge computing in IIoT. First, the concepts of IIoT and edge computing are discussed, and subsequently, the research progress of edge computing is discussed and summarized in detail. Next, the future architecture from the perspective of edge computing in IIoT is proposed, and its technical progress in routing, task scheduling, data storage and analytics, security, and standardization is analyzed. Furthermore, we discuss the opportunities and challenges of edge computing in IIoT in terms of 5G-based edge communication, load balancing and data offloading, edge intelligence, as well as data sharing security. Finally, we introduce some typical application scenarios of edge computing in IIoT, such as prognostics and health management (PHM), smart grids, manufacturing coordination, intelligent connected vehicles (ICV), and smart logistics.

Several approaches have addressed the consistency and automatic enactment dimensions of CSCL scripts with data flow, but they have not appropriately tackled the problem of reusing such learning designs. For instance, workflow-based solutions such as LeadFlow4LD only capture particular case behaviors, instead of describing generic data flow situations. This limitation hinders the reusability of these designs because the workflow needs to be adapted for specific technical, teaching and social contexts. This adaptation is complex and time consuming, especially with a large number of students. In order to show the relevance of this problem, this paper analyzes the LeadFlow4LD approach through a real-world complex CSCL script. The study characterizes the reuse effort of CSCL scripts with and without data flow definition, in different social context settings. The findings illustrate how the data flow representation may affect the particularization of complex CSCL scripts, and pave the path for alternative, higher abstraction level representations of data flows, to reduce the reuse effort.

Analysis and mapping of national data flows and processes relevant to EFSA's business data collections is a long‐standing EFSA objective, originally launched with EFSA's SIGMA project in 2018‐2019 and further continued with the launch of a focused activity to “Develop, pilot and document a methodology for the data flow mapping and analysis in the European Member States” in 2023. The aim of latter activity is to apply a generic methodology for the analysis of any business data collection at Member State level with the ultimate purpose for countries and EFSA to increase understanding of data flows at national level, where existing challenges rely on and what common solutions could be implemented. This report presents results from the execution of the aforementioned methodology on Norway for the analysis of national data flows which contribute data to Food Consumption business data collection. The report contains information on the steps, participant organizations and actors, tools, systems, formats, structural and content metadata, as well as challenges and “pain points” identified in the country's internal data flows related to the collection of the selected data. The presented information has been generated based on the responses submitted by the participating Member State to a survey developed in the context of the data flow mapping and analysis methodology as well as relevant discussions held in a post‐survey interview with the country. Though the information presented on the current report represents real‐world data, it must be clarified that the survey and the follow‐up interview with the MS, have been both performed during the activity of the Data Flow Mapping methodology.

Reconfigurable computing refers to the notions of configware and flowware. Configware means structural programming, or programming in space to execute computation in space. Flowware means dataflow programming that schedules the data flow for output from or input to the configware architecture. In this paper, data flows of a synthesized computation are formalized. This means that data flow is specified as a behavioral stream function in stream calculus, which is used to underpin the semantics for Register Transfer Level (RTL) synthesis. A stream representation allows the use of coinductive principles in stream calculus. In particular, using the coinductive proof principle, we show that behavioral stream functions in the three-stage synthesis process (scheduling, register allocation and binding, allocation and binding of functional units) are always bisimilar regardless of changes in a scheduling, allocation or binding procedure. Our formalization makes pipelining possible, in which all functional units as well as registers of hardware resources are reused during different control steps (C-steps). Moreover, a coinductive approach to verifying flowware synthesis, which is independent of the heuristic during register allocating and binding step, is proposed as a practical technique.

Blockchain and federated learning-based intrusion detection approaches for edge-enabled industrial IoT networks: a survey

The Industrial Internet of Things (IIoT) is central to smart manufacturing, enabling real-time automation, data exchange, and system intelligence. However, the convergence of cyber-physical systems with legacy software and heterogeneous architectures introduces significant security challenges. This paper explores how software engineering principles can be strategically employed to enhance IIoT security by integrating threat modeling into the development lifecycle. In this study, we review classic models such as STRIDE, DREAD, and STPA-Sec, and evaluate their effectiveness when applied at various phases of the Secure Software Development Life Cycle (SSDLC). STRIDE focuses on classifying security threats, DREAD helps score the severity of risks, and STPA-Sec provides a safety-oriented approach to identifying unsafe control actions in IIoT environments. Additionally, we propose a secure development process to embed continuous security assurance during IIoT software deployment. This research highlights design-driven security patterns, model-driven engineering strategies, and secure API development best practices. This paper aims to support developers and architects in designing scalable and threat-aware IIoT systems through the alignment of software engineering with IIoT-specific threat vectors.