Virtual Communication Stack: Towards Building Integrated Simulator of Mobile Ad Hoc Network-based Infrastructure for Disaster Response Scenarios

Responses to disastrous events are a challenging problem, because of possible damages on communication infrastructures. For instance, after a natural disaster, infrastructures might be entirely destroyed. Different network paradigms were proposed in the literature in order to deploy adhoc network, and allow dealing with the lack of communications. However, all these solutions focus only on the performance of the network itself, without taking into account the specificities and heterogeneity of the components which use it. This comes from the difficulty to integrate models with different levels of abstraction. Consequently, verification and validation of adhoc protocols cannot guarantee that the different systems will work as expected in operational conditions. However, the DEVS theory provides some mechanisms to allow integration of models with different natures. This paper proposes an integrated simulation architecture based on DEVS which improves the accuracy of ad hoc infrastructure simulators in the case of disaster response scenarios.Comment: Preprint. Unpublishe

Hybrid Multiresolution Simulation & Model Checking: Network-On-Chip Systems

abstract: Designers employ a variety of modeling theories and methodologies to create functional models of discrete network systems. These dynamical models are evaluated using verification and validation techniques throughout incremental design stages. Models created for these systems should directly represent their growing complexity with respect to composition and heterogeneity. Similar to software engineering practices, incremental model design is required for complex system design. As a result, models at early increments are significantly simpler relative to real systems. While experimenting (verification or validation) on models at early increments are computationally less demanding, the results of these experiments are less trustworthy and less rewarding. At any increment of design, a set of tools and technique are required for controlling the complexity of models and experimentation. A complex system such as Network-on-Chip (NoC) may benefit from incremental design stages. Current design methods for NoC rely on multiple models developed using various modeling frameworks. It is useful to develop frameworks that can formalize the relationships among these models. Fine-grain models are derived using their coarse-grain counterparts. Moreover, validation and verification capability at various design stages enabled through disciplined model conversion is very beneficial. In this research, Multiresolution Modeling (MRM) is used for system level design of NoC. MRM aids in creating a family of models at different levels of scale and complexity with well-formed relationships. In addition, a variant of the Discrete Event System Specification (DEVS) formalism is proposed which supports model checking. Hierarchical models of Network-on-Chip components may be created at different resolutions while each model can be validated using discrete-event simulation and verified via state exploration. System property expressions are defined in the DEVS language and developed as Transducers which can be applied seamlessly for model checking and simulation purposes. Multiresolution Modeling with verification and validation capabilities of this framework complement one another. MRM manages the scale and complexity of models which in turn can reduces V&V time and effort and conversely the V&V helps ensure correctness of models at multiple resolutions. This framework is realized through extending the DEVS-Suite simulator and its applicability demonstrated for exemplar NoC models.Dissertation/ThesisDoctoral Dissertation Computer Science 201

A modular hybrid simulation framework for complex manufacturing system design

For complex manufacturing systems, the current hybrid Agent-Based Modelling and Discrete Event Simulation (ABM–DES) frameworks are limited to component and system levels of representation and present a degree of static complexity to study optimal resource planning. To address these limitations, a modular hybrid simulation framework for complex manufacturing system design is presented. A manufacturing system with highly regulated and manual handling processes, composed of multiple repeating modules, is considered. In this framework, the concept of modular hybrid ABM–DES technique is introduced to demonstrate a novel simulation method using a dynamic system of parallel multi-agent discrete events. In this context, to create a modular model, the stochastic finite dynamical system is extended to allow the description of discrete event states inside the agent for manufacturing repeating modules (meso level). Moreover, dynamic complexity regarding uncertain processing time and resources is considered. This framework guides the user step-by-step through the system design and modular hybrid model. A real case study in the cell and gene therapy industry is conducted to test the validity of the framework. The simulation results are compared against the data from the studied case; excellent agreement with 1.038% error margin is found in terms of the company performance. The optimal resource planning and the uncertainty of the processing time for manufacturing phases (exo level), in the presence of dynamic complexity is calculated

Verification and Validation of D2FD Method

International audience—D2FD (Data to Fuzzy-DEVS) method provides a solution for the problem of system inference. This method is well designed and implemented as an available and dedicated plug-in within the process mining framework (ProM). This plug-in is also integrated with the simulation tool SimStudio. However, the last step of the process of inferring models and simulations, which is verification and validation, is missing. This paper proposes a new paradigm of verification and validation in system inference. The case study uses the method of comparing with other models as the main validation technique. Based on the same data source from the Dutch Employee Insurance Agency, it attempts to compare the previous results with other studies

Teaching Modeling to Engineers in an Undergraduate Simulation Course

A significant challenge in teaching simulation to undergraduate students is to find a way to allow them to model a real world referent system within time and student skill constraints. Several research sources highlight not only the important challenge of model development (Garcia and Ceneno, 2009, Tako, 2011) but also the increased need for model development instruction among engineers (Grasas et. al., 2013, Saltzman and Roeder, 2013). One approach to this challenge is to use a general purpose discrete event simulation software package within the course, but this presents two challenges. Teaching the package to the students takes significant time, and the package introduces limitations which may restrict their ability to model certain real-world referents, particularly in the engineering domain. A conceptual approach to solving this problem is to use a model development paradigm that abstracts away the interface to the simulation infrastructure while still allowing the students to use the full expressive nature of a programming language. Two undergraduate courses at the United States Military Academy employed this strategy via the Discrete Events Specification System – Distributed Modeling Framework (DEVS-DMF) (Kewley et. al, 2016). The DEVS abstraction allowed students to think about their model as a simple state change function with defined inputs and outputs, and DMF allowed them to program in a cloud-based Jupyter Notebook using the Python language. Students in a combat modeling course employed a variety of models to understand drone jamming, and students in an engineering capstone project employed models to account for human factors in rifle marksmanship. The effectiveness of this approach was assessed through student grades, exit-interviews, and course-end surveys. These assessments showed an increased understanding of the model development process, and students also reported greater ownership of their models. However, this experiment also highlighted some weaknesses in their understanding of underlying methodologies and programming skills

Learning and testing stochastic discrete event

Dissertação de mestrado em Engenharia de InformáticaSistemas de eventos discretos (DES) são uma importante subclasse de sistemas (à luz da teoria dos sistemas). Estes têm sido usados, particularmente na indústria para analisar e modelar um vasto conjunto de sistemas reais, tais como, sistemas de produção, sistemas de computador, sistemas de controlo de tráfego e sistemas híbridos. O nosso trabalho explora uma extensão de DES com ênfase nos processos estocásticos, comummente chamado como sistemas de eventos discretos estocásticos (SDES). Existe assim a necessidade de estabelecer uma abstração estocástica através do uso de processos semi-Markovianos generalizados (GSMP) para SDES. Assim, o objetivo do nosso trabalho é propor uma metodologia e um conjunto de algoritmos para aprendizagem de GSMP, usar técnicas de model-checking estatístico para a verificação e propor duas novas abordagens para teste de DES e SDES (respetivamente, não estocasticamente e estocasticamente). Este trabalho também introduz uma noção de modelação, analise e verificação de sistemas contínuos e modelos de perturbação no contexto da verificação por model-checking estatístico.Discrete event systems (DES) are an important subclass of systems (in systems theory). They have been used, particularly in industry, to analyze and model a wide variety of real systems, such as production systems, computer systems, traffic systems, and hybrid systems. Our work explores an extension of DES with an emphasis on stochastic processes, commonly called stochastic discrete event systems (SDES). There was a need to establish a stochastic abstraction for SDES through generalized semi-Markov processes (GSMP). Thus, the aim of our work is to propose a methodology and a set of algorithms for GSMP learning, using model checking techniques for verification, and to propose two new approaches for testing DES and SDES (non-stochastically and stochastically). This work also introduces a notion of modeling, analysis, and verification of continuous systems and disturbance models in the context of verifiable statistical model checking

Simulation of a Clustering Scheme for Vehicular Ad Hoc Networks Using a DEVS-based Virtual Laboratory Environment

ANT 2018, The 9th International Conference on Ambient Systems, Networks and Technologies, Porto, PORTUGAL, 08-/05/2018 - 11/05/2018Protocol design is usually based on the functional models developed according to the needs of the system. In Intelligent Transport Systems (ITS), the features studied regarding Vehicular Ad hoc Networks (VANET) include self-organizing, routing, reliability, quality of service, and security. Simulation studies on ITS-dedicated routing protocols usually focus on their performance in specific scenarios. However, the evolution of transportation systems towards autonomous vehicles requires robust protocols with proven or at least guaranteed properties. Though formal approaches provide powerful tools for system design, they cannot be used for every types of ITS components. Our goal is to develop new tools combining formal tools such as Event-B with DEVS-based (Discrete Event System Specification) virtual laboratories in order to design the models of ITS components which simulation would allow proving and verifying their properties in large-scale scenarios. This paper presents the models of the different components of a VANET realized with the Virtual Laboratory Environment (VLE). We point out the component models fitting to formal modeling, and proceed to the validation of all designed models through a simulation scenario based on real-world road traffic data