Modern modelling and simulation techniques allow us to safely test the policies used to mitigate disasters. We show how the DEVS formalism can be used to ease the modelling process by exploiting its modularity. We show how a policymaker's existing models of any type can be recreated with DEVS so they may be reused in any new models, decreasing the number of new models that need to be made. We recreate a sequential decision model of an arctic major maritime disaster developed by the Canadian government as a DEVS model to demonstrate the method. The case study shows how DEVS allows policymakers to create models for studying emergency policies with greater ease. This work shows a method that can be used by policymakers, including models of emergency scenarios, and how they can benefit from creating equivalent DEVS models, as well as exploiting the beneficial properties of the DEVS formalism.

The COVID-19 pandemic has highlighted the importance of defining sound policies to make attending workplaces safer. Sometimes, deciding on different policies is challenging as this highly depends on the behavior of the individuals. This research introduces a Discrete Event-based methodology and a prototype implementation to study such policies, including human behavior along with information about the workplace layout and building characteristics such as ventilation rate or room capacity. The method is based on a combination of agent-based models, diffusion processes and discrete-event simulation. We exemplify how to use this method using a case study based on Carleton University's Campus, in which we use the methodology and tools to study the effect of ventilation, as well as the application of a policy where sick students are denied entry to the campus on the number of disease cases on campus.

This study introduces a quantitative framework for assessing natural complexity in adaptive systems, based on connection measures weighted by sensitivity indices. The methodology integrates system modeling, sensitivity analysis, and complexity assessment, enabling continuous monitoring and decision support in dynamic environments. Natural complexity is defined as an optimal level at which the system behaves in accordance with its nature, sustaining coherence between structure and function. By employing sensitivity-weighted connections, the framework captures both internal organization and adaptive dynamics, overcoming limitations of traditional metrics such as Shannon entropy and fractal dimension, which often neglect interaction intensity and temporal variability. The framework is validated through two case studies: a computational model of an Intensive Care Unit and a real-world startup acceleration ecosystem. In the Intensive Care Unit, periods of overload were identified through peaks in complexity, associated with an increased number of highly sensitive parameter connections. In contrast, in the startup ecosystem, systemic idleness was reflected by lower complexity levels, driven by weakly influential interactions among actors. These findings highlight the responsiveness and interpretability of the proposed metric compared to conventional approaches, particularly in tracking adaptive states over time. This connection-based framework supports the management of adaptive information systems, offering a dynamic and scalable complexity assessment tool. Its applicability spans medical informatics, business management, and distributed systems optimization, providing real-time insights that improve resilience and efficiency. In addition, the approach aligns with industry 4.0 paradigms, facilitating preventive analyses and adaptive decision-making in advanced technological environments. By offering a unified methodology for complexity evaluation, this research advances understanding and control of complex adaptive systems.

Simulation has become, in many application areas, a sine-qua-non. Most recently, COVID-19 has underlined the importance of simulation studies and limitations in current practices and methods. We identify four goals of methodological work for addressing these limitations. The first is to provide better support for capturing, representing, and evaluating the context of simulation studies, including research questions, assumptions, requirements, and activities contributing to a simulation study. In addition, the composition of simulation models and other simulation studies' products must be supported beyond syntactical coherence, including aspects of semantics and purpose, enabling their effective reuse. A higher degree of automating simulation studies will contribute to more systematic, standardized simulation studies and their efficiency. Finally, it is essential to invest increased effort into effectively communicating results and the processes involved in simulation studies to enable their use in research and decision-making. These goals are not pursued independently of each other, but they will benefit from and sometimes even rely on advances in other subfields. In the present paper, we explore the basis and interdependencies evident in current research and practice and delineate future research directions based on these considerations.

The study of infectious disease models has become increasingly important during the COVID-19 pandemic. The forecasting of disease spread using mathematical models has become a common practice by public health authorities, assisting in creating policies to combat the spread of the virus. Common approaches to the modeling of infectious diseases include compartmental differential equations and cellular automata, both of which do not describe the spatial dynamics of disease spread over unique geographical regions. We introduce a new methodology for modeling disease spread within a pandemic using geographical models. We demonstrate how geography-based Cell-Discrete-Event Systems Specification (DEVS) and the Cadmium JavaScript Object Notation (JSON) library can be used to develop geographical cellular models. We exemplify the use of these methodologies by developing different versions of a compartmental model that considers geographical-level transmission dynamics (e.g. movement restriction or population disobedience to public health guidelines), the effect of asymptomatic population, and vaccination stages with a varying immunity rate. Our approach provides an easily adaptable framework that allows rapid prototyping and modifications. In addition, it offers deterministic predictions for any number of regions simulated simultaneously and can be easily adapted to unique geographical areas. While the baseline model has been calibrated using real data from Ontario, we can update and/or add different infection profiles as soon as new information about the spread of the disease become available.

Simulation visualisation is an effective way of understanding and communicating complex systems and processes. Among other advantages, it increases model transparency and intelligibility for all categories of users including non-experts, and it can be used by modellers as a tool to debug models in development. However, simulation visualisation is often tightly coupled to specific simulators, and, therefore, there is no way to reuse visualisation tools efficiently. Here, we present a specification that can be used to decouple visualisation engines from simulators. The specification also considers storage optimisation to support web-based simulation applications. We also present an implementation that supports the web-based representation and animation of outputs issued from simulators based on the discrete event system specification (DEVS) and Petri Nets.

Internet-of-Things (IoT) networks provide massive connectivity for many application scenarios. Recently, much work has been dedicated to develop spectrum access strategies for IoT networks with a massive number of nodes and sporadic data traffic behavior. The case becomes more challenging in critical applications when Ultra-Reliable Low-Latency (URLL) transmissions are required. Such networks entail spectrum-efficient transmission schemes in which Non-Orthogonal Multiple-Access (NOMA) is considered a key enabler. We proposed a Distributed Queuing (DQ) approach in NOMA for critical massive IoT (mIoT) applications. More specifically, we introduce a frame structure to support DQ-based NOMA so that dynamic NOMA clustering (at the nodes) and dynamic Successive Interference Cancellation (SIC) ordering at the Base Station (BS) are supported. We also use adaptive power back-off strategy to reduce power collisions by utilizing both nodes' and clusters' activation index. We investigate network performance metrics, such as reliability, delay violation probability, and effective sum rate. These metrics are derived analytically, and the effect of different network parameters such as blocklength, active node arrival rate, and the number of contention subslots on the network metrics are investigated and compared with the S-ALOHA-TD benchmark.

The growth of real-time embedded applications has surged in recent years, marked by both an increase in the number and complexity of tasks performed across various industries. Modeling and simulation (M&S) has been used for enhancing product quality while reducing lifecycle costs, primarily through improved testability and maintainability of real-time embedded systems applications, as M&S-based design approach allows for early hardware functionality testing, fosters collaboration between hardware and software teams, and shortens the product development cycle. The discrete-event system specification (DEVS) framework has been used for developing such discrete-event M&S systems. This article starts by highlighting the various DEVS publications in our journal over the past 20 years, reflecting the evolving landscape of simulation methodologies, which we organized into four categories: theory, methodology, tools, and applications. This curated selection reflects the diversity of topics and the evolution of scholarship within the field, encouraging further exploration and innovation. We conclude with recent research in the field, including our own research in real-time embedded systems development using DEVS software for modeling, simulation, and real-time execution of models. This paves the way for future discussions in this important field of research.

Carbon dioxide concentration in enclosed spaces is an air quality indicator that affects occupants’ well-being. To maintain healthy carbon dioxide levels indoors, enclosed space settings must be adjusted to maximize air quality while minimizing energy consumption. Studying the effect of these settings on carbon dioxide concentration levels is not feasible through physical experimentation and data collection. This problem can be solved by using validated simulation models, generating indoor settings scenarios, simulating those scenarios, and studying results. In previous work, we presented a formal Cellular Discrete Event System Specifications simulation model for studying carbon dioxide dispersion in rooms with various settings. However, designers may need to predict the results of altering large combinations of settings on air quality. Generating and simulating multiple scenarios with different combinations of space settings to test their effect on indoor air quality is time-consuming. In this research, we solve the two problems of the lack of ground truth data and the inefficiency of producing and studying simulation results for many combinations of settings by proposing a novel framework. The framework utilizes a Cellular Discrete Event System Specifications model, simulates different scenarios of enclosed spaces with various settings, and collects simulation results to form a data set to train a deep neural network. Without needing to generate all possible scenarios, the trained deep neural network is used to predict unknown settings of the closed space when other settings are altered. The framework facilitates configuring enclosed spaces to enhance air quality. We illustrate the framework uses through a case study.

Bottlenecks are a major problem for manufacturing companies because they limit the throughput of the production line. Although analytical methods have been widely studied, these methods are impractical in many cases, and simulation-based approaches, where a model of the system is developed, are needed. In this work, we show how to use DEVS as a tool to model and simulate manufacturing systems. More specifically, we propose to use it to apply the Theory of Constraints, identifying bottlenecks in the manufacturing plants and large amounts of Work in Progress.

The process for the standardization of the exchange of dynamic models resulted in the development of a Functional Mockup Interface (FMI). In this paper, we present a method to import a continuous time model using the FMI standard in Cadmium, a DEVS simulator. The method of implementation aims to address the challenges in importing and simulating the system developed in other tools into Cadmium. We implemented the simulation of the continuous-time model in DEVS by using a QSS solver. The paper also discusses the C++ library developed to support the implementation of FMU import. The DEVS implementation of the library and the solver were tested by the import and simulation of ordinary differential equation models.

Intercommunication between processes in a distributed hard real-time system have to meet extremely tight timing deadlines. One of the bottlenecks in the communication process is the protocol used within the system. This paper presents a formal definition and implementation of a communication protocol that is reliable and predictable for use with distributed hard real-time systems and by extension, distributed real- time simulations. The protocol presented in this paper was modeled using the Discrete Event System specification and simulated using the Cadmium simulator. We present a case study wherein we distribute a centralized model using the protocol. Upon simulation, the behavior of the system before and after distribution were observed and their congruence determined.

The Internet of Things (IoT) has emerged as a transformative technology with a variety of applications across various industries. However, the development of IoT systems is hindered by challenges such as interoperability, system complexity, and the need for streamlined development and maintenance processes. In this study, we introduce a robust architecture grounded in discrete event system specification (DEVS) as a model-driven development solution to overcome these obstacles. Our proposed architecture utilizes the publish/subscribe paradigm, and it also adds to the robustness of the proposed solution with the incorporation of the Brooks–Iyengar algorithm to enhance fault tolerance against unreliable sensor readings. We detail the DEVS specification that is used to define this architecture and validate its effectiveness through a detailed home automation case study that integrates multiple sensors and actuators.

Discrete Event System Specification (DEVS) is a modeling and simulation of discrete event systems formalism. Most DEVS-based simulators are implemented as sequential programs. However, simulating large-scale complex models in a sequential simulator is impractical (if possible), as simulations may take a long time to execute. A usual technique to speed up simulations is the parallel execution of the simulator. Most parallel discrete-event simulation efforts focus on logical process approaches, resulting in complex simulation architectures. Recent parallelizing efforts lean towards executing the simulators in multicore architectures. Despite promising results, they are limited to the amount of CPU processing cores. In this work, we propose an algorithm to accelerate the execution of DEVS simulations on Graphical Processing Units (GPU) architectures. We show different case studies where the proposed algorithm achieved speedups of up to 12.29 and 16.53 compared to a sequential version.

Large scale geospatial simulation projects require multidisciplinary efforts by actors with highly variable skills and domains of expertise. Subject matter experts, modelers, developers, analysts, and decision makers must collaborate closely to model a real-world system, simulate it, analyze its results and disseminate them. Simulation environments, tailored to business scenarios, can provide the necessary support to facilitate their collaboration throughout the simulation lifecycle. Commercial modeling and simulation software can provide an environment to facilitate simulation studies for users but, they tend to be narrowly scoped. This research focuses on the different categories of users and introduces four business processes that carry those users across the simulation lifecycle. These concepts are translated into an architecture that facilitates the operationalization of geospatial simulation environments using modeling and simulation as a service and Discrete Event Systems Specification. Large scale geospatial simulation projects require multidisciplinary efforts by actors with highly variable skills and domains of expertise. Subject matter experts, modelers, developers, analysts, and decision makers must collaborate closely to model a real-world system, simulate it, analyze its results and disseminate them. This paper focuses on the different categories of users and introduces four business processes that carry those users across the simulation lifecycle. These concepts are translated into an architecture that facilitates the operationalization of geospatial simulation environments using modeling and simulation as a service and Discrete Event Systems Specification. image

Real-time systems are complex to design and implement. Various modelling and simulation techniques are employed to make this task more structured and efficient. However, there is often a disconnect between modeling for simulation and development for deployment. In this paper we discuss a technique to bridge this gap between simulation and deployment, specifically dealing with a framework to handle asynchronous inputs into a system developed using the Discrete Event System Specification. Further, this paper presents a case study that demonstrates the effectiveness of the framework, and the congruence between simulation and deployment of a real-time system is determined.