Over the past few years, the transition from centralized to distributed industrial control systems (ICS) has introduced new challenges related to coordination, communication reliability, and cybersecurity. These challenges include conditions such as deadlocks and livelocks, which adversaries can exploit to compromise ICS safety and availability. To ensure secure and resilient operations in distributed ICS, run-time monitoring must go beyond detection to include responsive recovery. In this paper, we extend the ASM2S framework, a model-based inline security monitoring approach, by integrating recovery capabilities directly into the monitoring loop. Our approach uses formal specifications to allow system behavior, threat conditions, and recovery actions to be explicitly defined and evaluated at run-time. We demonstrate the approach using a water distribution system use case. Our work enhances the run-time assurance of distributed ICS by enabling automatic detection and recovery from security violations, offering a robust foundation for self-healing critical infrastructure.

Industrial Control Systems (ICS) increasingly face cybersecurity threats due to their distributed architecture and critical role in infrastructure operations. We adopt inline security monitoring as a practical run-time verification strategy to address these risks. However, authoring formal specifications remains time-consuming and error-prone, requiring deep domain expertise. In this paper, we explore how large language models (LLMs) can support the synthesis of inline security monitors by generating Java Modeling Language (JML) specifications for distributed ICS applications. We use a water distribution system (WDS) as our testbed and FormalBench to generate prompts to guide the GPT-4o model in producing JML annotations. We then evaluate these outputs using the FormalBench framework. Our findings show that LLMs capture key security properties and generate context-aware assertions with minimal intervention, taking a first step toward automating the specification process and enhancing the security and resilience of distributed ICS environments.

The increasing complexity and decentralization of Industrial Control Systems (ICS) have expanded the attack surface for cyber-security threats, particularly in critical infrastructure domains. Inline monitoring using formal annotations like the Java Modeling Language (JML) offers a lightweight yet precise method to detect behavioral anomalies. However, the manual creation of such specifications is resource-intensive and requires domain expertise. This paper explores generative artificial intelligence (AI), specifically large language models (LLMs), to automate the synthesis of inline formal security monitors. We benchmark three state-of-the-art LLMs (GPT-4o, DeepSeek-V3, and Gemini 2.5 Flash) on their ability to generate JML annotations for ICS software drawn from the ASM2S water distribution system. Our evaluation across five dimensions (syntax, semantics, property coverage, alarm semantics, and effort savings) reveals distinct trade-offs. GPT-4o demonstrates strong syntactic and structural alignment with ASM2S, while DeepSeek-V3 offers richer behavioral modeling. Gemini 2.5 Flash showcases conceptual depth but introduces non-verifiable constructs. These findings demonstrate the potential of LLMs as co-pilots in secure-by-design ICS development and underscore the need for syntax-aware fine-tuning and interactive verification workflows.

In the decentralized Industrial Control Systems (ICS) era, water distribution systems (WDS) are critical in ensuring water safe and reliable delivery. However, their growing complexity, connectivity, and distributed nature expose them to cybersecurity risks. Renowned WDS software, like EPANET, lacks features to address such risks, which, however, can be addressed by complementary solutions, like ASM2S. In this paper, we compare the capabilities offered by these two tools and make a first step towards exploring their combination, aiming to equip WDS tools with enhanced hydraulic, water quality, and cybersecurity modeling and monitoring characteristics.

In the decentralized Industrial Control Systems (ICS) domain, water distribution systems (WDS) are critical in ensuring water safe and reliable delivery. However, their growing complexity, connectivity, and distributed nature expose them to cybersecurity risks. Run-time inline monitoring can address such risks. This paper focuses on implementing run-time inline security monitoring for deadlocks in WDS, providing examples and simulation results. The results indicate the run-time detection of deadlocks, enhancing WDS’s overall reliability and efficiency.

Over the past few years, there has been a noticeable transition from centralized Industrial Control Systems (ICS) to distributed systems. However, the challenges of distributed systems (e.g., communication delays and packet loss) can give rise to undesired situations like deadlocks, which malicious actors may target. To address such conditions, implementing run-time security monitoring can ensure these systems’ reliable and secure operation. In this paper, we introduce a novel approach for run-time security monitoring for distributed ICS, extending previous works that focus on autonomous and centralized systems. Our approach makes it possible to specify physical and cyber resources and their changing limitations within a distributed environment. By doing so, our approach offers a valuable contribution to ICS security by tackling limitations introduced by distributed ICS. Furthermore, it provides an efficient mechanism for monitoring the security of these systems in real time.

Virtual and augmented reality are currently enjoying a great deal of attention from the research community and the industry towards their adoption within industrial spaces and processes. However, the current design and implementation landscape is still very fluid, while the community as a whole has not yet consolidated into concrete design directions, other than basic patterns. Other open issues include the choice over a cloud or edge-based architecture when designing such systems. Within this work, we present our approach for a monitoring intervention inside a factory space utilizing both Virtual Reality (VR) and Augmented Reality (AR), based primarily on edge computing, while also utilizing the cloud. We discuss its main design directions, as well as a basic ontology to aid in simple description of factory assets. In order to highlight the design aspects of our approach, we present a prototype implementation, based on a use case scenario in a factory site, within the context of the EnerMan H2020 project.

The Industrial Internet of Things (IIoT) is a key element of industry 4.0, bringing together modern sensor technology, fog and cloud computing platforms, and artificial intelligence to create smart, self-optimizing industrial equipment and facilities. Though, the scale and sensitivity degree of information continuously increases, giving rise to serious privacy concerns. The scope of this article is to provide efficient privacy preservation techniques, by tracking the correlation of multivariate streams recorded in a network of IIoT devices. The time-varying data covariance matrix is used to add noise that cannot be easily removed by filtering, generating obfuscated measurements and, thus, preventing unauthorized access to the original data. To improve communication efficiency between connected IoT devices, we exploit inherent properties of the correlation matrices, and track the essential correlations from a small subset of correlation values. Extensive simulation studies using constrained IIoT devices validate the robustness, efficiency, and effectiveness of our approach.

The Industrial IoT (IIoT) is a key element of Industry 4.0, bringing together modern sensor technology, fog - cloud computing platforms, and artificial intelligence (AI) to create smart, self-optimizing industrial equipment and facilities. Though, the scale and sensitivity degree of information continuously increases, giving rise to serious privacy concerns. In this work we address the problem of efficiently and effectively tracking the structure of multivariate streams recorded in a network of IIoT devices. The time varying correlation data values are used to add noise which maximally preserves privacy, in the sense that it is very hard to be removed. To improve communication efficiency between connected IoT devices, we exploit low rank properties of the correlation matrices, and track the essential correlations from a small subset of correlation values estimated by a subset of network nodes. Extensive simulation studies, validate the correctness, efficiency, and effectiveness of our approach in terms of computational complexity, transmission energy efficiency and privacy preservation.

Software models that simulate the energy performance and behavior of buildings provide experts, engineers and building administrators with a powerful predictive tool that allows users to analyze and predict the future impact of a possible energy saving measure without any costly physical intervention or the need for measurements. To achieve this, models have to be validated and tested for precision robustness, in order to be able to maintain an acceptable predictive capability. The present work deals with the implementation a calculation-based monthly quasi-steady state simulation model for energy use in buildings based on the ISO 13790 standard methodologies and presents its robustness testing methodology.

The Industrial Internet of Things (IIoT) has emerged as a concept for the integration of the Internet of Things (IoT) in the industrial domain, posing a variety of challenges and emphasizing among others in interoperability and integration aspects with Shop Floor and Plant layer systems. Present work builds on top of previous work related to the integration of the different systems in the manufacturing environment automation pyramid. This classical hierarchy involves three layers: the Enterprise Resource Planning (ERP), the Manufacturing Execution System (MES) and the Shop Floor. A further issue is relevant to the semantic integration of IoT sensors with manufacturing systems. The present paper presents a complex system architecture which allows both the aggregation of data generated by IIoT devices, and the automated reaction of the manufacturing environment to either detected and diagnosed, or predicted events based on this data.

Usage of raw data as an asset, from which value can be created to support business and manufacturing decision making, motivated a lot of scientists to explore the challenges on how to exploit this value. Such efforts are concentrated on the framework of “data value chain”, where approaches of architectures and applications aim at equipping enterprises with tools that gather, process and extract knowledge from raw data generated by their internal or external processes. In this context, the present paper deals with the way the production processes in a factory can be adapted to changes that are detected by the processing of raw data which are aggregated by IoT devices installed in the manufacturing environment. The proposed approach introduces a complex system that combines a network of IoT sensors and a high-level multi-agent system that contributes to the vertical integration of all the systems residing in the Enterprise/Factory.

The improvement of the energy efficiency of a complex system such as an existing large non-residential building, requires multi-disciplinary engineering knowledge, practices and teams, as well as support from equally complex software tools and modelling methodologies, especially during the initial planning and feasibility assessment phases of relevant projects. In this paper, the peculiarities of the special case of hospital buildings are discussed and a number of use cases are described, identifying significant components and setting the basic needed tooling interventions and methodology steps in order to address the complexity of assessment of different energy conservation measures.

The evaluation of the energy consumption or the energy performance of buildings, from a complex engineering system point of view, is a multi-parametric problem where different static or dynamic methods are being used. Quantitative analysis is usually required, not only in the context of building regulations, but also as an important engineering tool for planning energy efficiency measures or interventions. In this paper a quasi-steady state model for the energy use in buildings is presented, which combines simplicity, minimum requirements for data input and adequate accuracy. This model is based on the methodologies described in the ISO 13790 standard. It combines a basic steady state physical model, where dynamic effects are also taken into account by introducing different correlation factors and reference parameters. It's main advantage over more complex dynamic models requiring detailed data and high expertise, is that it can be used not only from a small number of experts but also from engineers involved in energy management or energy efficiency projects. This model is validated according to the procedures of the EN 15265 standard, in order to investigate the dependence of the results on the different parameters used.

Sensitivity analysis plays a vital role in building energy analysis. It is done to clarify which are the crucial variables affecting building thermal performance and to evaluate quantitatively those effects. It can be conducted both through energy simulation models and real case observations. The present paper is devoted to the description of the sensitivity analysis techniques that are able to extract the most effective parameters on the energy consumption of a commercial building, particularly medical centers. Energy consumption in medical centers, generally, depends on several parameters ranging from technical and geometrical aspects to climatic conditions. This paper is focused on the application of sensitivity analysis in term of energy consumption in medical centers through a benchmark simulation model which is developed by National Renewable Energy Laboratory (NREL) in order to classify the most effective parameters on energy consumption of a large hospitals.

In this paper, a methodology towards a hardware/software implementation of an Elliptic Curve Diffie Hellman (ECDH) scheme is proposed in an effort to overcome the design problems of Elliptic Curve Cryptography (ECC) systems stemming from the highly constrained embedded system hardware and software environment (restricted RAM, storage and processing power). To achieve that, instead of the excessively slow software ECDH implementations or monolithic, not flexible hardware implementations, we propose the use of a flexible, scalar multiplication (SM) accelerator connected to the main embedded system processor in order to speed up ECDH functionality without downgrading the overall main processor performance. The proposed solution can be used for a wide variety of GF(2k) based Elliptic Curves (EC) and is capable of shifting from one EC to another EC at runtime (flexibility). The proposed architecture was implemented and tested in Xilinx Virtex 5 technology by realizing the proposed SM accelerator unit interconnected with a Xilinx microblaze softcore processor.

Keccak hash function has been submitted to SHA-3 competition and it belongs to the final five candidate functions. In this paper FPGA implementations of Keccak function are presented. The designs were coded using HDL language and for the hardware implementation, a XILINX Virtex-5 FPGA was used. Some of the proposed implementations use DSP48E blocks in order to accelerate the designs execution. So, comparisons between the proposed designs in terms of time performance and FPGA resources are given in order to examine the efficiency of the using DSP48E blocks. Also, comparisons with previous published works are provided.

Critical characteristics of wireless sensor networks, as being autonomous and comprising small or miniature devices are achieved at the expense of very strict available energy related limitations. Therefore, it is apparent that optimal resource management is among the most important challenges in WSNs development and success. However, energy management requires in depth knowledge and detailed insight concerning the factors contributing to the overall power consumption of a WSN mote. To achieve such awareness, appropriate measuring test-beds and methodologies are needed, enabling reliable and accurate power consumption measurements of critical functionalities. Moving towards that direction, the contribution of this paper is twofold. On one hand, the design and implementation of a system is presented, capable of accurately measuring and displaying a wide range of power consumption thresholds. On the other hand, the elementary functionalities of a WSN platform are identified, isolated and measured with respect to their contribution to the overall mote power consumption. Valuable conclusions are extracted and analyzed.

In this paper, the architecture and the operation of a cut-through forwarding device to be used in broadcasting, hybrid wired/wireless fieldbus systems are described. Analytical models of the delay overhead introduced due to frame forwarding are presented. It is shown that the usage of cut-through forwarding devices relaxes the bit-rate requirements in the radio segments, while it drastically improves the inherent advantages and reduces the drawbacks of hybrid transmission media architectures which are based on a single MAC domain.

This paper addresses the assembly-line scheduling problem with concurrent operations per stage and parallel machines. This problem can be briefly described as the problem of sequencing a predetermined set of parts with known processing-time requirements through anr-station assembly line so that the makespan is minimized. A set of concurrent operations must be performed at each station on each part. This set of operations is usually partdependent since different parts have different processing requirements. In this paper, we construct schedules in quadratic time for this strongly NP-hard problem with an absolute performance guarantee independent of the number of jobs. This makes our schedules asymptotically optimal as the number of jobs becomes very large. The implementation of our schedules facilitates the efficient use of concurrent operations and prevents the assembly line from degenerating into performing operations serially, which would result in low production rates. We utilize compact vector summation techniques and first construct a schedule for the case where each concurrent operation is performed by a single machine. We then generalize our schedule to the case where each concurrent operation is performed by a set of identical parallel machines.

The benefits of wireless extensions in industrial networks are well recognized as long as the integration of the wireless and fieldbus domains will be capable to offer the realtime and dependability quality of the current wired industrial networking solutions. This paper presents a study, from a performance point of view, for the possible integration of the most popular 3G and WLAN radio technologies with fieldbus technologies. A brief examination of a number of dominant wireless protocols against the basic wireless fieldbus requirements is presented. For the case study, Profibus is used, on which, the basic message cycle performance is calculated for each of the UNITS, 802.11b and HIPERLAN protocols, under different integration approaches.