Python-based eSES/MB Framework: Model Specification and Automatic Model Generation for Multiple Simulators

Folkerts, Hendrik; Pawletta, Thorsten; Deatcu, Christina; Hartmann, Sven

doi:10.11128/sne.29.tn.10497

Cited by 5 publications

(2 citation statements)

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“…That means that the SES does not define any dynamic behavior on its own. The SES is represented by a tree structure comprising entity nodes, descriptive nodes and attributes [ 51 ]. Different system structures can be coded in the SES trees.…”

Section: Ses/mb Frameworkmentioning

confidence: 99%

WSN-SES/MB: System Entity Structure and Model Base Framework for Large-Scale Wireless Sensor Networks

Nam¹,

Kim

2021

Sensors

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Large-scale wireless sensor networks are characterized by stringent energy and computation restrictions. It is exceedingly difficult to change a sensor network’s environment configurations, such as the number of sensor nodes, after deployment of the nodes. Although several simulators are able to variously construct simulation models for sensor networks before their deployment, the configurations should be modified with extra human effort as the simulators cannot freely generate diverse models. In this paper, we propose a novel framework, called a system entity structure and model base for large-scale wireless sensor networks (WSN-SES/MB), which is based on discrete event system specification formalism. Our proposed framework synthesizes the structure and models for sensor networks through our modeling construction process. The proposed framework achieves time and cost savings in constructing discrete event simulation-based models. In addition, the framework increases the diversity of simulation models by the process’s pruning algorithm. The simulation results validate that the proposed framework provides up to 8% time savings and up to 23% cost savings as compared to the manual extra effort.

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Section: Ses/mb Frameworkmentioning

confidence: 99%

WSN-SES/MB: System Entity Structure and Model Base Framework for Large-Scale Wireless Sensor Networks

Nam¹,

Kim

2021

Sensors

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“…This paper is based on [1] and [2]. It focuses on the more general approach using the tools for variability modeling introduced there by integrating with the Functional Mock-up Interface (FMI) instead of just offering simulator specific solutions.…”

Section: Introductionmentioning

confidence: 99%

Model Generation for Multiple Simulators Using SES/MB and FMI

Folkerts¹,

Pawletta²,

Deatcu³

2021

SNE

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This paper deals with the extension of a Python-based infrastructure for studying the characteristics and behavior of families of systems. The infrastructure allows automatic execution of simulation experiments with varying system structures as well as with varying parameter sets in different simulators. Special focus is put on the support of different simulation environments by creating models implementing the Functional Mockup Interface (FMI). Possible system structures and parameterizations are defined using a System Entity Structure (SES). The SES as a high level approach for variability modeling, particularly in simulation engineering, describes a set of system configurations, i.e. different system structures and parameter settings of system components. In combination with a Model Base (MB), executable models can be generated from an SES. Based on the extended SES/MB approach, tool-supported variability modeling and automatic model generation and execution in different simulation environments using FMI is described. This is done by means of an engineering application.

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