Event-based Simulation Research Articles

Modelling elevator traffic with social distancing in a university classroom building

Social distancing standards implemented during the COVID-19 pandemic may have negative effects on vertical traffic. We describe a model and use it to predict the elevator traffic under social distancing in a university classroom building, and study the effects of four interventions aimed at improving this traffic. Discrete event-based simulation is used to study whether the lift group meets the forecasted demand when the car capacity is restricted far below its ordinary value to accommodate social distancing. Four low-cost interventions are simulated alone and in combination to quantify the improvements they offer. All four interventions show some improvement, and the combination of all four interventions gives the greatest improvement. Practical application: Implementing social distancing standards may disproportionately lower the car capacity relative to the building population and thus negatively affect vertical traffic. Building managers seeking to implement low-cost measures to improve elevator traffic under these conditions may look to the percentage improvements described here to aid them in selecting interventions appropriate to their buildings.

Selecting Efficient and Reliable Preservation Strategies

This article addresses the problem of formulating efficient and reliable operational preservation policies that ensure bit-level information integrity over long periods, and in the presence of a diverse range of real-world technical, legal, organizational, and economic threats. We develop a systematic, quantitative prediction framework that combines formal modeling, discrete-event-based simulation, hierarchical modeling, and then use empirically calibrated sensitivity analysis to identify effective strategies.
 Specifically, the framework formally defines an objective function for preservation that maps a set of preservation policies and a risk profile to a set of preservation costs, and an expected collection loss distribution. In this framework, a curator’s objective is to select optimal policies that minimize expected loss subject to budget constraints. To estimate preservation loss under different policy conditions optimal policies, we develop a statistical hierarchical risk model that includes four sources of risk: the storage hardware; the physical environment; the curating institution; and the global environment. We then employ a general discrete event-based simulation framework to evaluate the expected loss and the cost of employing varying preservation strategies under specific parameterization of risks.
 The framework offers flexibility for the modeling of a wide range of preservation policies and threats. Since this framework is open source and easily deployed in a cloud computing environment, it can be used to produce analysis based on independent estimates of scenario-specific costs, reliability, and risks.
 We present results summarizing hundreds of thousands of simulations using this framework. This exploratory analysis points to a number of robust and broadly applicable preservation strategies, provides novel insights into specific preservation tactics, and provides evidence that challenges received wisdom.

The impacts of shared autonomous vehicles on car parking space

It is anticipated that Shared Autonomous Vehicles (SAV) may revolutionise the way people travel as it may lead to less ownership of vehicles and a gradual increase in demand responsive mobility. These vehicles are likely to able to park far away from popular destinations after they drop off their clients. It is, therefore, capable of releasing parking spaces around central business districts while cutting down parking search time and cost. But the magnitude at which these spaces will be released in relation to SAV fleet size is not currently known. This work therefore seeks to understand this relationship using the University of the West of England, Frenchay campus characterised by 23 carparks of varying size – as a case study – to quantitatively understand these impacts. This was done by simulating 2181 parking slot within the campus; motor traffic (SAVs and non-SAVs); and the route networks from the 3 entry stations – East, North and Longmead entrances – to each parking slot. Carpark preference data was obtained and analysed. It formed the context on which drivers choose a parking space within the system. These data alongside with the graphical features of the Frenchay campus were imported into ARENA simulation toolkit: a discrete event-based simulation software. Results indicated the obvious that as SAV fleet size increases, spaces released also increases but in addition, individual carpark reacted dynamically in response to the demand on neighbouring carparks. It also showed that there is currently an excess of parking supply compared to the number of vehicles entering the campus daily. It further discusses the potential of multiple occupancy SAVs to further release all parking spaces on the Frenchay campus. The work concludes with the potentials for this research to be extended to a citywide approach with a special interest in the ability for SAV to release kerb parking spaces.

Efficient discrete-event based particle tracking simulation for high energy physics

This work presents novel discrete event-based simulation algorithms based on the Quantized State System (QSS) numerical methods. QSS provides attractive features for particle transportation processes, in particular a very efficient handling of discontinuities in the simulation of continuous systems. We focus on High Energy Physics (HEP) particle tracking applications that typically rely on discrete time-based methods, and study the advantages of adopting a discrete event-based numerical approach that resolves efficiently the crossing of geometry boundaries by a traveling particle. For this purpose we follow two complementary strategies. First, a new co-simulation technique connects the Geant4 simulation toolkit with a standalone QSS solver. Second, a new native QSS numerical stepper is embedded into Geant4. We compare both approaches against the latest Geant4 default steppers in different HEP setups, including a complex realistic scenario (the CMS particle detector at CERN). Our techniques achieve relevant simulation speedups in a wide range of scenarios, particularly when the intensity of discrete-event handling dominates performance in the solving of the continuous laws of particle motion.

An analysis and simulation tool of real-time communications in on-chip networks

This paper presents Real-Time Network-on-chip-based architecture Analysis and Simulation tool (ReTiNAS), with a special focus on real-time communications. It allows fast and precise exploration of real-time design choices onto NoC architectures. ReTiNAS is an event-based simulator written in Python. It implements different real-time communication protocols and tracks the communications within the NoC at cycle level. Its modularity allows activating and deactivating different NoC components and easily extending the implemented protocols for more customized simulations and analysis. Further, we use ReTiNAS to perform a comparative study of analysis and simulation for different communication protocols using a wide set of synthetic experiments.

Event-Based Simulation of a Decentralized Protection System Based on Secured GOOSE Messages

A new simulation library is developed on OMNeT++ to model faults in distribution systems. The proposed library makes it possible to calculate the status of lines and busbars from the point of view of a protection system, enabling the modeling of overcurrents, power outages and fault passage indicators. The library is applied to model a decentralized protection system based on the exchange of IEC 61850 Generic Object Oriented Substation Events (GOOSE) messages between intelligent electronic devices responsible for the operation of circuit breakers and disconnectors. The time needed to secure and transmit GOOSE messages over the Internet is characterized and included in the model. Several studies are carried out to analyze the effect of different parameters, such as GOOSE retransmission times and failure rates of switching devices and communication channels, on the performance of the protection system.

Predicting the performance of big data applications on the cloud

Data science applications have become widespread as a means to extract knowledge from large datasets. Such applications are often characterized by highly heterogeneous and irregular data access patterns, thus often being referred to as big data applications. Such characteristics make the application execution quite challenging for existing software and hardware infrastructures to meet their resource demands. The cloud computing paradigm, in turn, offers a natural hosting solution to such applications since its on-demand pricing model allows allocating effectively computing resources according to application’s needs. However, these properties impose extra challenge to the accurate performance prediction of cloud-based applications, which is a key step to adequate capacity planning and managing of the hosting infrastructure. In this article, we tackle this challenge by exploring three modeling approaches for predicting the performance of big data applications running on the cloud. We evaluate two queuing-based analytical models and dagSim, a fast ad-hoc simulator, in various scenarios based on different applications and infrastructure setups. The considered approaches are compared in terms of prediction accuracy and execution time. Our results indicate that our two best approaches, one analytical model and dagSim, can predict average application execution times with only up to a $$7\%$$ relative error, on average. Moreover, a comparison with the widely used event-based simulator available with the Java Modeling Tool (JMT) suite demonstrates that both the analytical model and dagSim run very fast, requiring at least two orders of magnitude lower execution time than JMT while providing slightly better accuracy, being thus practical for online prediction.

Adaptive Discrete Event Simulation Systems to Embrace Changes of Requirements Using Event Control Models

During the development or deployment of discrete event simulation systems, many sudden changes in requirements emerge. To embrace the changes rapidly and reduce development costs, attaching reusable, and black-boxed components to existing systems has been regarded as one of the most effective approaches because modifications at the code level are generally costly and risky. However, since this approach requires components that are an exact fit, it may not be easy to avoid the modifications at the source code level actually. Moreover, the required components may not exist. Hence, this paper applies black-box extensibility to simulation systems in order to avoid the modifications. This paper proposes adaptive discrete event simulation systems using event control models for extending and modifying semantics through event-based simulation interface. With the proposed work, the simulation events are modulated, deleted, and generated by the event-oriented control functions in the event control model to embrace the changes of requirements. It can substitute for the modifications at the code level and extend the existing behavioral semantics. As a result, the proposed work provides a new alternative step in the development process with reusable components to avoid modifications at the source code level. The new step will lead to rapid adaptations of existing simulation systems. To support the effectiveness of this approach, this paper will describe applicable examples based on our empirical studies.

Evaluation of Actual Timetables and Utilization Levels of West Midlands Metro Using Event-Based Simulations

The performance of the West Midlands Metro in the United Kingdom is analyzed in the present study by evaluating the existing timetables of the metro system. Using SIMUL8 computer software, a discrete event-based simulation prototype modeling the metro system is developed and implemented. The model adequately describes the performance of the West Midlands Metro system. By running simulations, the overall utilization level of the metro system is calculated. The results of the simulation model indicate that the metro system is being underutilized. The low utilization rates indicate a potential for the introduction of new services capable of exploiting the existing infrastructure and improving the utilization levels of the existing metro system; For example, the potential of using the current metro system for urban freight transport could be a new service of interest and provide scope for further research.

Sobol Global Sensitivity Analysis of a Coupled Surface/Subsurface Water Flow and Reactive Solute Transfer Model on a Real Hillslope

The migration and fate of pesticides in natural environments is highly complex. At the hillslope scale, the quantification of contaminant fluxes and concentrations requires a physically based model. This class of model has recently been extended to include coupling between the surface and the subsurface domains for both the water flow and solute transport regimes. Due to their novelty, the relative importance of and interactions between the main model parameters has not yet been fully investigated. In this study, a global Sobol sensitivity analysis is performed on a vineyard hillslope for a one hour intensive rain event with the CATHY (CATchment HYdrology) integrated surface/subsurface model. The event-based simulation involves runoff generation, infiltration, surface and subsurface solute transfers, and shallow groundwater flow. The results highlight the importance of the saturated hydraulic conductivity K s and the retention curve shape parameter n and they reveal a strong role for parameter interactions associated with the exchange processes represented in the model. The mass conservation errors generated by the model are lower than 1% in 99.7% of the simulations. Boostrapping analysis of sampling methods and errors associated with the Sobol indices highlights the relevance of choosing a large sampling size (at least N = 1000) and raises issues associated with rare but extreme output results.

An IoT Architecture for Continuous Livestock Monitoring Using LoRa LPWAN

The Internet of Things (IoT) architecture is quickly becoming popular even outside of its originating scenario of home automation. This paper reports the design, implementation, and performance of an IoT hardware and software architecture conceived for the continuous monitoring of livestock located in barns and during grazing. We have adopted the LoRa low power wide area network (LPWAN) technology to cover the diverse environments, and a suitable configuration of web services to perform data storage, analysis, and visualization. Since the LoRa LPWAN (LoRaWAN) medium access control (MAC) layer does not provide a listen-before-talk (LBT) mechanism, we propose a custom MAC layer with LBT-based carrier-sense multiple access with collision avoidance (CSMA/CA). The devised system has been implemented using off-the-shelf hardware, and its performance has also been estimated with the help of a C++ event-based simulator. The preliminary results of our HW implementation on the field confirm the stability of the conceived system and its reliability.

Multiple Event-Based Simulation Scenario Generation Approach for Autonomous Vehicle Smart Sensors and Devices.

Nowadays, deep learning methods based on a virtual environment are widely applied to research and technology development for autonomous vehicle’s smart sensors and devices. Learning various driving environments in advance is important to handle unexpected situations that can exist in the real world and to continue driving without accident. For training smart sensors and devices of an autonomous vehicle well, a virtual simulator should create scenarios of various possible real-world situations. To create reality-based scenarios, data on the real environment must be collected from a real driving vehicle or a scenario analysis process conducted by experts. However, these two approaches increase the period and the cost of scenario generation as more scenarios are created. This paper proposes a scenario generation method based on deep learning to create scenarios automatically for training autonomous vehicle smart sensors and devices. To generate various scenarios, the proposed method extracts multiple events from a video which is taken on a real road by using deep learning and generates the multiple event in a virtual simulator. First, Faster-region based convolution neural network (Faster-RCNN) extracts bounding boxes of each object in a driving video. Second, the high-level event bounding boxes are calculated. Third, long-term recurrent convolution networks (LRCN) classify each type of extracted event. Finally, all multiple event classification results are combined into one scenario. The generated scenarios can be used in an autonomous driving simulator to teach multiple events that occur during real-world driving. To verify the performance of the proposed scenario generation method, experiments using real driving video data and a virtual simulator were conducted. The results for deep learning model show an accuracy of 95.6%; furthermore, multiple high-level events were extracted, and various scenarios were generated in a virtual simulator for smart sensors and devices of an autonomous vehicle.

Demir Çelik Endüstrisinde Torpido Taşıma Sistemi Benzetimi

Demir-Çelik endüstrisinde sürekli üretim söz konusudur. Ham demir cevherini katı çelik formuna getiren birçok firma tam gün aralıksız çalışmaktadır. Bu tür firmaların üretim süreçleri oldukça maliyetli ve zahmetlidir. Sıvı ham demirin sürekli olarak döküm operasyonları arasında taşınması gerekmektedir. Taşımalarda yaşanan aksaklıklar üretici firmanın büyük mali kayıplar yaşamasına sebep olmaktadır. Bu çalışmada; bir demir-çelik firmasında üretilen sıvı ham demirin proses içerisinde taşınması için kullanılan torpidoların verimliliğinin artırılması amaçlanmıştır. Üretim sisteminin olay artırımlı benzetimi yapılmıştır. Üretimde darboğaz yaratan torpidoların kullanılmaması önerilmiştir. Üretime yönlendirilecek torpidolara, çok kriterli karar verme yöntemlerinden biri olan Topsis metodu ile karar verilmiştir. Mevcut sistem ile önerilen yöntem benzetim ortamında kıyaslandığında zaman kaybı %66,35 oranında azalırken üretim miktarında %9,06 (ayda ortalama 13.215,33 ton sıvı ham demire karşılık gelen) bir artış sağlanmıştır.

Tackling Uncertainty for the Development of Efficient Decision Support System in Air Traffic Management

Airport capacity has become a constraint in the air transportation networks due to the growth of air traffic demand and the lack of resources able to accommodate this demand. This paper presents the algorithmic implementations of a decision support system for making a more efficient use of the airspace and ground capacity. The system would be able to provide support for air traffic controllers in handling large amount of flights while reducing to a minimum potential conflicts. In this framework, airspace together with ground airport operations is considered. The conflicts are defined as separation minima violation between aircraft for what concerns airspace and runways and as capacity overloads for taxiway network and terminals. The methodology proposed in this paper consists of an iterative approach that couples optimization and simulation to find solutions that are resilient to perturbations due to the uncertainty present in different phases of the arrival and departure process. An optimization model was employed to find a (sub)optimal solution, while a discrete event-based simulation model evaluated the objective function. By coupling simulation with optimization, we generate more robust solutions resilient to variability in the operations, and this is supported by a case study of Paris Charles de Gaulle Airport.

Exploiting Orbital Data and Observation Campaigns to Improve Space Debris Models

The European Space Agency (ESA) has been developing the Meteoroid and Space Debris Terrestrial Environment Reference (MASTER) software as the European reference model for space debris for more than 25 years. It is an event-based simulation of all known individual debris-generating events since 1957, including breakups, solid rocket motor firings and nuclear reactor core ejections. In 2014, the upgraded version of the Debris Risk Assessment and Mitigation Analysis (DRAMA) tool suite was released. In the same year an ESA instruction made the standard ISO 24113:2011 on space debris mitigation requirements, adopted via the European Cooperation for Space Standardization (ECSS), applicable to all ESA missions. In order to verify the compliance of a space mission with those requirements, the DRAMA software is used to assess collision avoidance statistics, estimate the remaining orbital lifetime and evaluate the on-ground risk for controlled and uncontrolled re-entries. In this paper, the approach to validate the MASTER and DRAMA tools is outlined. For MASTER, recent observation campaign results shall be discussed. In DRAMA, the assessment of collision avoidance statistics is based on orbit uncertainty information derived from Conjunction Data Messages (CDM) provided by the Joint Space Operations Center (JSpOC). The way this information is going to be used in a future DRAMA version is outlined and the comparison of estimated manoeuvre rates with real manoeuvres from the operations of ESA spacecraft is shown.

A Systems Design Study Introducing a Collection Point for Baggage Transfer Services at a Railway Station in the UK

Railway transport has shown a steady growth in passenger numbers over the past 20 years across the UK. Passengers travel with luggage. It has been forecasted that there will be a reduction of “luggage racks-to-seats” ratio in the future passenger train fleet. There are currently no baggage transfer systems at any of the train stations as part of the urban rail transit system in the UK. Hence, the purpose of this study is to investigate the possibility of having a baggage transfer service at Newcastle Central Station, which can serve different travel destinations. A simulation modelling study of different parts of the baggage check-in shop within the railway station is offered. Check-in point, movement of luggage around Newcastle Central, baggage reclaims and storage areas have been contemplated and evaluated using SIMUL8 event-based simulation package. The results for the simulation model developed show that a baggage transfer service at Newcastle Central Station is possible with a mixture of walk-in and online check-in options.

Simulation of watershed-scale practices for mitigating stream thermal pollution due to urbanization

Thermally enriched runoff from urban impervious surfaces can be harmful to aquatic life; however, only limited information is available on how to mitigate these impacts at the watershed-scale. This study evaluates the effects of retrofitting an urban watershed with thermal mitigation practices (TMPs) relative to thermal toxicity thresholds for aquatic species. The Minnesota Urban Heat Export Tool (MINUHET) and Storm Water Management Model (SWMM) models were used to evaluate TMPs that help reduce temperature and total heat loads (THL) from the Stroubles Creek watershed in Blacksburg, Virginia. We used the aquatic health criteria for brook trout (Salvelinus fontinalis), the most sensitive species present downstream of the watershed, as a performance measure. TMPs included bioretention systems, methods for reducing the albedo of surfaces (cool surfaces), and increasing forest canopy. Performance metrics included Event Mean Temperature (EMT), and the Percentage of Time Temperature Exceeded the 21 °C Acute Toxicity Threshold [Percentage of Time above the Threshold (PTAT)] for brook trout; these metrics were used to quantify reductions in heat loads and temperatures. TMPs were evaluated during continuous simulation and selected storm events. Increased forest canopy alone produced the greatest reduction of stream temperature, as quantified by EMT and PTAT metrics during continuous and event-based simulations. In contrast, cool surfaces reduced THL more than any other individual TMP for the continuous simulation. A comprehensive mitigation plan (CMP) integrating all three TMPs reduced THL by 62.3%, and PTAT by approximately 12%, for the entire summer of 2015. The CMP was also applied to select storm events, during which streamflow EMT was reduced up to 9%, and PTAT was reduced nearly to zero. This study, which is the first to simulate watershed-scale TMPs for a large, complex urban area, demonstrates the application of appropriate strategies for restoring aquatic habitats in the thermally impacted Stroubles Creek.

Parameter Estimation and Uncertainty Analysis: A Comparison between Continuous and Event-Based Modeling of Streamflow Based on the Hydrological Simulation Program–Fortran (HSPF) Model

Hydrologic modeling is usually applied to two scenarios: continuous and event-based modeling, between which hydrologists often neglect the significant differences in model application. In this study, a comparison-based procedure concerning parameter estimation and uncertainty analysis is presented based on the Hydrological Simulation Program–Fortran (HSPF) model. Calibrated parameters related to base flow and moisture distribution showed marked differences between the continuous and event-based modeling. Results of the regionalized sensitivity analysis identified event-dependent parameters and showed that gravity drainage and storage outflow were the primary runoff generation processes for both scenarios. The overall performance of the event-based simulation was better than that of the daily simulation for streamflow based on the generalized likelihood uncertainty estimation (GLUE). The GLUE analysis also indicated that the performance of the continuous model was limited by several extreme events and low flows. In the event-based scenario, the HSPF model performances decreased as the precipitation became intense in the event-based modeling. The structure error of the HSFP model was recognized at the initial phase of the rainfall-event period. This study presents a valuable opportunity to understand dominant controls in different hydrologic scenario and guide the application of the HSPF model.

IB-MAC: Transmission Latency-Aware MAC for Electro-Magnetic Intra-Body Communications.

Intra-body Communication (IBC) is a communication method using the human body as a communication medium, in which body-attached devices exchange electro-magnetic (EM) wave signals with each other. The fact that our human body consists of water and electrolytes allows such communication methods to be possible. Such a communication technology can be used to design novel body area networks that are secure and resilient towards external radio interference. While being an attractive technology for enabling new applications for human body-centered ubiquitous applications, network protocols for IBC systems is yet under-explored. The IEEE 802.15.6 standards present physical and medium access control (MAC) layer protocols for IBC, but, due to many simplifications, we find that its MAC protocol is limited in providing an environment to enable high data rate applications. This work, based on empirical EM wave propagation measurements made for the human body communication channel, presents IB-MAC, a centralized Time-division multiple access (TDMA) protocol that takes in consideration the transmission latency the body channel induces. Our results, in which we use an event-based simulator to compare the performance of IB-MAC with two different IEEE 802.15.6 standard-compliant MAC protocols and a state-of-the art TDMA-based MAC protocol for IBC, suggest that IB-MAC is suitable for supporting high data rate applications with comparable radio duty cycle and latency performance.

Economic Evaluation of Maintenance Strategies for Offshore Wind Turbines Based on Condition Monitoring Systems

An offshore wind farm’s cost of energy is, to a large extent, driven by operation and maintenance costs. Through the optimization of the maintenance strategies for offshore wind farms, the Levelized Cost of Energy (LCOE) will be further reduced, which will ceteris paribus lead to a higher competitiveness of offshore wind farms with other energy sources. This study pro-poses an event-based simulation of an offshore wind farm comprising 400 MW. The aim of the model is to minimize the total cost, and thus to maximize the revenues from the wind farm. Therefore, corrective, condition-based, and scheduled maintenance strategies are compared, and constraints, such as weather conditions and service team shifts, are taken into consideration. When hourly electricity spot prices are applied instead of feed-in tariffs, results show that weekly scheduled maintenance on a Saturday dayshift (starting at 8 am) is the most cost-efficient scenario. Condition monitoring systems have been found to be advantageous and are set as a standard application regarding the turbines. The impact of scheduled maintenance frequency, the distance between the offshore site and the coast, the interest rate, and altering reliability data are further analyzed.