Simulation Tools and Techniques

The Internet of Things (IoT) has emerged as a promising technology with diverse applications across industries, including smart homes, healthcare services, and manufacturing. However, despite its potential, IoT presents unique challenges, such as interoperability, system complexity, and the need for efficient development and maintenance. This paper explores a model-driven development (MDD) approach to design IoT applications by employing high-level models to facilitate abstraction and reusability. Specifically, we adopt a methodology based on Discrete Event System Specification (DEVS), a modular and hierarchical formalism for MDD of IoT. In our work, different DEVS models are developed to address distinct functional aspects of the devices, encompassing data retrieval, data serialization/deserialization, and network connectivity. The developed models, along with a DEVS simulator, are then used for both simulation and deployment. To create a comprehensive simulation environment, the paper introduces two additional models for simulating the MQTT protocol, including its Quality of Service (QoS) mechanism.

A challenging aspect in optimizing legacy distributed systems with strict real-time requirements is how to evaluate the performance of the system running in a production environment without disrupting its regular operation. The challenge is even greater when the System Under Evaluation (SUE) runs within a resource-sharing environment and, thus, is affected by the resource usage of other software running in the same environment. Current performance evaluation methods dealing with this challenge rely on data collected by Application Performance Monitoring (APM) tools that are not always available in existing systems and hard to establish when the system is already in production. In this paper, we improve the initial, proof-of-concept implementation of our RAST (Regression Analysis, Simulation, and load Testing) approach to evaluate the response time of a distributed system using the available system’s request logs. In particular, we greatly improve the prediction model based on machine learning. Our use case is a commercial alarm system in productive use, developed and maintained by the GSelectronic company in Germany. We experimentally demonstrate that our improvements significantly enhance RAST’s capability to adequately predict the system performance and verify the strict requirements on the response time. We make our model and software freely available in order to enable reproducing our experiments.

The growth of home automation makes it necessary to train qualified personnel in the knowledge and use of the most important standards, among which KNX is the leader in Europe. The programming of home automation services with KNX is based on the concept of group addresses, which allow defining the behavior of the domotic devices for a previously defined facility. This training is complex and usually requires a physical domotic facility where the previously programmed design can be tested, which makes the learning process difficult for the students. In this article we show the development and characteristics of a multi-platform simulator that recreates the real operation of an automated facility for any programming scheme defined by the student, validating it by means of a virtual installation that includes different devices usually involved in home automation. This simulator has allowed the generation of a wide range of cases of use in the training of the most usual domotic services.

Nowadays, Unnamed Aerial Vehicles (UAVs) are used in many different fields, ranging from agriculture and entertainment to parcel delivery, among others. For several of these tasks, the UAVs are programmed to follow a specific path, as defined in their flight missions. In addition, as more sophisticated solutions begin to be adopted, new protocols should be developed to handle possible collisions among UAVs, as well as to create UAV swarms. However, directly testing new protocols on UAVs can be hazardous and time consuming. Therefore, many investigators first perform simulations. Although different UAV simulators exists, not all of them offer a 3D rendering of the UAVs in the target flight environment. Hence, in this work, we present a real-time 3D visualization interface that can be easily coupled to any simulator. In this way, we offer developers a powerful way to validate their solutions and make in-depth analysis.

The simulation of robots behavior and the use of robust models are very important for building controllers. Testing is an important aspect in this process. In this paper, a test-driven approach for designing robot controllers based on enzymatic numerical P systems models is introduced. Four such models are defined and tested using three distinct scenarios. The paper reveals an effective way of using modelling, simulation and testing in a coherent way.

Stochastic Petri Nets (SPNs) are a powerful formalism, widely used for modeling complex systems in various domains, ranging from manufacturing and logistics to healthcare and computer networks. In this paper, we introduce PySPN, a flexible and easily extendable Python library for Modeling & Simulation (M &S) of SPNs. PySPN aims to provide researchers, engineers, and simulation practitioners with a user-friendly and efficient toolset to model, simulate, and analyze SPNs, facilitating the understanding and optimization of stochastic processes in dynamic systems.

We provide the definitive implementation of the seminal agent-based societal simulation Sugarscape. Our implementation is fully-featured open source software [5] that aims to make Sugarscape available for use by researchers across disciplines. It includes an extensive validation of all results shown in Growing Artificial Societies [2]. We also discuss the significant challenges in modernizing this groundbreaking body of work.

In systems engineering and model-based design, the complexity and interrelationships across different system elements always demand continuous elaboration and expansion in various overlapping domains. We examine how such a phenomenon can be assisted with generative AI and benefit from large language models (LLMs), such as GPT-4. We demonstrate ways of incorporating generated text and outputs from LLMs into the modeling process. The approach can customize the GPT-4 model with an activity metamodel specified in Eclipse Ecore or predefined activity diagrams encoded in a textual format for learning from instances. Alternatively, the descriptive text from the LLM can be provided as input to a parser, resulting in an activity that can be readily transformed into a discrete event system specification (DEVS) model with simulation capability. We will discuss how the process can be enhanced in a simulation environment, thus offering the opportunity to examine a variety of scenarios and arguments for incorporating generative AI or general AI as a collaborative agent in the domain of interest. One scenario could begin with a simplified text describing a generic process, yielding an approximate representation as a starting point for further elaboration by modelers to a complex specification through a systematic, guided, and well-defined framework. We demonstrate the approach with activity and flow-based diagrams in a manner applicable to SysML, UML, and systems engineering at large.

The management and prevention of forest fires are crucial in fire-prone regions such as Corsica, a French island in the Mediterranean. In this study, an approach to mapping wildfire vulnerability is presented using different data sources, including meteorological, temporal, geographical and economic datasets. These heterogeneous datasets are seamlessly integrated to produce a comprehensive forest fire vulnerability map for Corsica. The methodology involves the collection and pre-processing of a variety of data, such as historical forest fire events, meteorological variables, land cover data, socio-economic indicators and temporal factors. Machine learning models are used to visualise the complex relationships between these variables and predict wildfire susceptibility. Finally, we were able to create a daily fire susceptibility map for the island of Corsica.

To validate the alternative simulation models and select the most credible one when the models have multivariate and correlated outputs, an intelligent ranking evaluation method of simulation models based on Graph Neural Network (GNN) is proposed. The process of ranking evaluation is divided into three parts: graph structure conversion for evaluation data, feature extraction based on Graph Representation Learning (GRL) and ranking evaluation based on feature distance. A graph structure modeling method is presented to provide the pre-define graph structure for further GRL primarily. Next the interdependencies and dynamic evolutionary patterns among variables are captured by GNN so that the graph representations of evaluation data can be obtained. Then ranking evaluation is achieved by similarity measurement of the graph representations. In the end, the effectiveness of the proposed method on feature extraction of evaluation data and simulation models ranking is illustrated through an application example on a prediction model for aerodynamic parameters of a certain flight vehicle.

The development of control systems for critical infrastructures requires testing and validating the proposals before using them in real environments. This work proposes the development of a new control system with an approach based on sustainability, which uses multi-agent systems as a basis, and which breaks away from traditional proposals focused on optimising energy costs. This new approach requires a thorough validation before its possible deployment, as it is based on distributed components that make independent decisions to generate complex emergent behaviour. In order to test its viability, a simulator has also been developed alongside the control system, which allows the behaviour of each agent to be analysed by subjecting it to tests using real data from the scenario to be controlled. Through this tool it is possible to observe each agent in the fulfilment of its functions, validate its behaviour, and check that the control system guarantees the supply of drinking water to a city, using the data obtained from that city as input. Through the simulator it is possible to analyse and represent different configurations of the control system over an infrastructure, thus being able to select the best option for the environment.

With this paper, we aim to make two main contributions. Firstly, we present a detailed overview of performance metrics used for estimating traffic conditions in urban settings. Compared to highway situations with relatively stable traffic conditions, Traffic State Estimation in urban environments exhibits several challenges, which we discuss in depth. Secondly, through a simulation study, we utilize Eclipse MOSAIC to assess the capabilities and limitations of these metrics. Therefore, we have developed an open-source suite of applications and add-ons for MOSAIC, that will be documented in this paper. Utilizing the publicly available BeST traffic scenario, which encompasses 24 h of realistic urban traffic in Berlin, we present a comparative analysis of average speeds observed on various types of urban roads. Importantly, we made these implementations available to the open-source community, providing a valuable resource for traffic scientists and others who are interested in our contribution.

The electrification of road transportation requires the development of an extensive infrastructure of public charging stations (CSs). In order to avoid them contributing to increased traffic congestion and air pollution in a city, it is very important to optimize their deployment. To tackle this challenge, we present microscopic traffic simulations with a hybrid cellular automata and agent-based model to study different strategies to route electric vehicles (EVs) to CSs, when their battery level is low. EVs and CSs are modeled as agents with capability to demonstrate complex behaviors. Our models take into account the complex nature of traffic and decisions about routes and their predicted behavior. We show that a synthetic city is very useful for investigating the routing behavior and traffic patterns. We have found that a smart routing strategy can contribute to balancing the distribution of EVs among the different CSs in a distributed network, which is the CS layout that produces less traffic congestion. Contrary to our initial expectations, ensuring a balanced distribution throughout the city did not necessarily result in an increase in overall productivity. This observation led to a deeper exploration of the nuances of urban transport dynamics. Furthermore, our study emphasizes the superiority of time-based routing over its distance-based counterpart and highlights the inherent limitations of transportation within a city.

The shift toward electric vehicles requires the development of an extensive public electric charging infrastructure. With the aim of simulating hundreds of configurations for charging stations, street directions, crossing, etc., we need to find the best solution in short periods of time to predict and prevent traffic congestion. Thus, we study different models to discretize and manage vehicle movements using a synchronous cellular automata, with an emphasis in reducing the amount of (frequently accessed) memory and execution time, and improving the thread parallelism. This is guided by the classical lemma of computer architecture “make the common case fast", thus optimizing those code sections where most of the execution time is spent. Experiments carried out for microscopic traffic simulations indicate that compiled languages increase run-time efficiency by more than 70\(\times \). Then several strategies are studied, such as storing future velocities of each vehicle so that neighbor vehicles can benefit from this information. Using a single 12-core PC, we get to a total run-time for a unidimensional simulation that is very close to that reached by supercomputers composed of thousands of cores that use interpreted languages. This may also greatly reduce the energy consumed. Although some performance degradation may occur when complex situations are introduced (crossroads, traffic lights, etc.), this degradation would not be significant if the length of the streets were large enough.

Autonomous and Guided Vehicles (AGVs) have long been employed in material handling but necessitate significant investments, such as designating specific movement areas. As an alternative, Autonomous and Intelligent Vehicles (AIVs) have gained traction due to their adaptability, intelligence, and capability to handle unexpected obstacles. Yet, challenges like optimizing scheduling and path planning, and managing routing conflicts persist. This study introduces a simulator tailored for AIV scheduling and path planning in various production systems. The simulator supports both predictive, where paths are pre-determined, and dynamic scheduling, with real-time optimization. Paths are determined using Dijkstra’s method, ensuring AIVs use the shortest route. When path-sharing conflicts arise, a multi-criteria priority system comes into play, and its impact on the makespan is assessed. Experimental results highlight the advantage of AIVs over AGVs in most scenarios and the simulator’s efficiency in generating effective schedules, incorporating the priority management system.

Sudden cardiac death events and fatal cardiac problems are a field of vital importance for physicians working with elite athletes. For this reason, it is common to periodically perform cardiac monitoring with professional ECG devices to detect certain risk markers. As these doctors often work with many athletes (as is the case with professional football teams), an artificial intelligence-based system would help mass screening and allow these exams to be carried out more regularly. Because physicians often evaluate the printed reports generated by ECG devices, few manufacturers provide powerful and configurable software tools. Moreover, for teaching purposes, a simulation tool that would allow working with previously collected ECG files would be very useful. In this paper, we present a software tool to be used with General Electric CardioSoft 12SL electrocardiograph. This tool allows importing the XML files generated by this device, perform a manual or automatic signal filtering process and PQRST peak detection, and finally generate a customisable report as a CSV file containing the features obtained after signal analysis. This pre-processed information can be used as input of ECG simulators and in artificial intelligence systems to develop diagnostic support systems.

In this work, we present a workflow aimed to help ear, nose, and throat (ENT) surgeons in the planning and analysis of inner ear and cochlear implant (CI) surgical interventions. The proposed workflow, OTOVIRT, is based on a multi-modal image registration process with both computer tomography (CT) and magnetic resonance images (MRI) of the patient, followed by the segmentation of anatomical relevant structures. The volumetric images and the 3D anatomic models developed are then used to create virtual surgical simulations of the CI intervention. OTOVIRT modelling workflow proves to be an efficient pipeline to improve surgical outcomes and train surgeons’ capabilities. Further advances in OTOVIRT workflow will hopefully allow multimodal data extraction and multiphysics simulation to be systematically conducted in daily clinical practice.

As we gradually embrace Smart Cities and the many advantages they can potentially offer, several technical issues arise that should be properly addressed, including security, efficiency, and performance, among others. In this regard, the massive deployment of IoT devices to support the numerous potential applications can consume excessive resources if their use is not optimized; this includes the sharing of some IoT devices whose content has the potential to be shared by many potential applications. One such example are CCTV smart cameras, as their deployment is costly, has a significant impact on urban aesthetics, and also the traffic flow they generate is high. To address such issue, in this paper we propose a novel SDN framework that enables the seamless sharing of streamed CCTV camera contents among multiple users, while adequately accounting for security and privacy restrictions. In particular, we adopt the Zero Trust paradigm to have a fine granularity control of the data-sensitive contents streamed by CCTV cameras. Experiments performed in Mininet using the Ryu controller evidence the potential of our solutions, which is able to achieve the target goals in a resource efficient manner, while introducing a low network updating overhead.

Unmanned Aerial Vehicles (UAVs) offer promising potential as communications node carriers, providing on-demand wireless connectivity to users. While existing literature presents various wireless channel models, it often overlooks the impact of UAV heading. This paper provides an experimental characterization of the Air-to-Ground (A2G) and Ground-to-Air (G2A) wireless channels in an open environment with no obstacles nor interference, considering the distance and the UAV heading. We analyze the received signal strength indicator and the TCP throughput between a ground user and a UAV, covering distances between 50 m and 500 m, and considering different UAV headings. Additionally, we characterize the antenna’s radiation pattern based on UAV headings. The paper provides valuable perspectives on the capabilities of UAVs in offering on-demand and dynamic wireless connectivity, as well as highlights the significance of considering UAV heading and antenna configurations in real-world scenarios.

Despite the trend towards ubiquitous wireless connectivity, there are scenarios where the communications infrastructure is damaged and wireless coverage is insufficient or does not exist, such as in natural disasters and temporary crowded events. Flying networks, composed of Unmanned Aerial Vehicles (UAV), have emerged as a flexible and cost-effective solution to provide on-demand wireless connectivity in these scenarios. UAVs have the capability to operate virtually everywhere, and the growing payload capacity makes them suitable platforms to carry wireless communications hardware. The state of the art in the field of flying networks is mainly focused on the optimal positioning of the flying nodes, while the wireless link parameters are configured with default values. On the other hand, current link adaptation algorithms are mainly targeting fixed or low mobility scenarios.

We propose a novel rate adaptation approach for flying networks, named Trajectory Aware Rate Adaptation (TARA), which leverages the knowledge of flying nodes’ movement to predict future channel conditions and perform rate adaptation accordingly. Simulation results of 100 different trajectories show that our solution increases throughput by up to 53% and achieves an average improvement of 14%, when compared with conventional rate adaptation algorithms such as Minstrel-HT.

With the growing connectivity demands, Unmanned Aerial Vehicles (UAVs) have emerged as a prominent component in the deployment of Next Generation On-demand Wireless Networks. However, current UAV positioning solutions typically neglect the impact of Rate Adaptation (RA) algorithms or simplify its effect by considering ideal and non-implementable RA algorithms. This work proposes the Rate Adaptation aware RL-based Flying Gateway Positioning (RARL) algorithm, a positioning method for Flying Gateways that applies Deep Q-Learning, accounting for the dynamic data rate imposed by the underlying RA algorithm. The RARL algorithm aims to maximize the throughput of the flying wireless links serving one or more Flying Access Points, which in turn serve ground terminals. The performance evaluation of the RARL algorithm demonstrates that it is capable of taking into account the effect of the underlying RA algorithm and achieve the maximum throughput in all analysed static and mobile scenarios.

The increasing complexity of recent Wi-Fi amendments is making the use of traditional algorithms and heuristics unfeasible to address the Rate Adaptation (RA) problem. This is due to the large combination of configuration parameters along with the high variability of the wireless channel. Recently, several works have proposed the usage of Reinforcement Learning (RL) techniques to address the problem. However, the proposed solutions lack sufficient technical explanation. Also, the lack of standard frameworks enabling the reproducibility of results and the limited availability of source code, makes the fair comparison with state of the art approaches a challenge. This paper proposes a framework, named RateRL, that integrates state of the art libraries with the well-known Network Simulator 3 (ns-3) to enable the implementation and evaluation of RL-based RA algorithms. To the best of our knowledge, RateRL is the first tool available to assist researchers during the implementation, validation and evaluation phases of RL-based RA algorithms and enable the fair comparison between competing algorithms.

Several research works have applied Reinforcement Learning (RL) algorithms to solve the Rate Adaptation (RA) problem in Wi-Fi networks. The dynamic nature of the radio link requires the algorithms to be responsive to changes in link quality. Delays in the execution of the algorithm due to implementional details may be detrimental to its performance, which in turn may decrease network performance. These delays can be avoided to a certain extent. However, this aspect has been overlooked in the state of the art when using simulated environments, since the computational delays are not considered. In this paper, we present an analysis of computational delays and their impact on the performance of RL-based RA algorithms, and propose a methodology to incorporate the experimental computational delays of the algorithms from running in a specific target hardware, in a simulation environment. Our simulation results considering the real computational delays showed that these delays do, in fact, degrade the algorithm’s execution and training capabilities which, in the end, has a negative impact on network performance.