Simulation Modelling Practice and Theory最新文献

{"title":"Simulation-based evaluation of advanced threat detection and response in financial industry networks using zero trust and blockchain technology","authors":"Clement Daah,&nbsp;Amna Qureshi,&nbsp;Irfan Awan,&nbsp;Savas Konur","doi":"10.1016/j.simpat.2024.103027","DOIUrl":"10.1016/j.simpat.2024.103027","url":null,"abstract":"<div><div>The financial sector is increasingly facing advanced cyber threats, necessitating a shift from traditional security measures to more dynamic frameworks. This study presents a novel integration of Zero Trust architecture with hybrid access control system and blockchain technology to enhance security in financial institutions. Zero Trust enforces continuous authentication and dynamic access controls, while blockchain secures digital identities and transaction logs through its immutable ledger, ensuring data integrity and non-repudiation. The proposed framework, evaluated using OMNeT++ simulations enhanced by Ethereum-Ganache, shows improved detection accuracy, reduced false positives, and increased resistance to insider threats and other attacks. It also strengthens compliance with regulatory requirements through robust audit trails, providing enhanced protection for sensitive financial data.</div></div>","PeriodicalId":49518,"journal":{"name":"Simulation Modelling Practice and Theory","volume":"138 ","pages":"Article 103027"},"PeriodicalIF":3.5,"publicationDate":"2024-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142572104","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Simulation-based adaptive optimization for passenger flow control measures at metro stations","authors":"Yiqi Zhou ,&nbsp;Maohua Zhong ,&nbsp;Zhongwen Li ,&nbsp;Xuan Xu ,&nbsp;Fucai Hua ,&nbsp;Rongliang Pan","doi":"10.1016/j.simpat.2024.103021","DOIUrl":"10.1016/j.simpat.2024.103021","url":null,"abstract":"<div><div>Effective passenger flow control measures are essential for the safe operation of metro stations. Existing in-station control measures include adjusting the operation mode of escalators and setting up temporary fences. However, in practice, metro operators often adopt fixed operation modes during fixed periods, indicating that the current passenger flow control measures at metro stations are overly rigidified. Therefore, developing an adaptive control strategy to constantly balance the wildly fluctuating passenger flow and optimize the operation performance is a key issue in current research. In this study, transportation efficiency and congestion risk are selected as evaluation objectives for passenger transportation risk, and passenger flow feature, station structure, and passenger flow control measures are considered key influential factors. Subsequently, an adaptive optimization method integrating simulation and data interpolation is proposed. The software Legion is used to conduct 150 orthogonal simulations, and prediction models for passenger transportation risk are obtained by performing data interpolation on the simulation results. Finally, taking a certain metro station as a case study, the optimal passenger flow control strategy under any passenger flow composition is obtained by scenario acquisition, risk identification, and adaptive decision-making. The results show that setting up temporary fences can reduce the passenger density near the fare gates, while adjusting the running direction of escalators can reduce overcrowding on the platform. Under varying passenger flow composition, the optimal strategy for the current scenario can be obtained, controlling passenger transportation risk within an acceptable range and providing assistance for metro operators in decision-making.</div></div>","PeriodicalId":49518,"journal":{"name":"Simulation Modelling Practice and Theory","volume":"138 ","pages":"Article 103021"},"PeriodicalIF":3.5,"publicationDate":"2024-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142535349","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Dual-timescale resource management for multi-type caching placement and multi-user computation offloading in Internet of Vehicle","authors":"Dun Cao ,&nbsp;Bo Peng ,&nbsp;Yubin Wang ,&nbsp;Fayez Alqahtani ,&nbsp;Jinyu Zhang ,&nbsp;Jin Wang","doi":"10.1016/j.simpat.2024.103025","DOIUrl":"10.1016/j.simpat.2024.103025","url":null,"abstract":"<div><div>In Internet of Vehicle (IoV), edge computing can effectively reduce task processing delays and meet the real-time needs of connected-vehicle applications. However, since the requirements for caching and computing resources vary across heterogeneous vehicle requests, a new challenge is posed on the resource management in the three-tier cloud–edge–end architecture, particularly when multi users offload tasks in the same time. Our work comprehensively considers various scenarios involving the deployment of multiple caching types from multi-users and the distinct time scales of offloading and updating, then builds a joint optimization caching placement, computation offloading and computational resource allocation model, aiming to minimize overall latency. Meanwhile, to better solving the model, we propose the Multi-node Collaborative Caching, Offloading, and Resource Allocation Algorithm (MCCO-RAA). MCCO-RAA utilizes dual time scales to optimize the problem: employing a Bellman optimization idea-based multi-node collaborative greedy caching placement strategy at large time scales, and a computational offloading and resource allocation strategy based on a two-tier iterative Deep Deterministic Policy Gradient (DDPG) and cooperative game at small time scales. Experimental results demonstrate that our proposed scheme achieves a 28% reduction in overall system latency compared to the baseline scheme, with smoother latency variations under different parameters.</div></div>","PeriodicalId":49518,"journal":{"name":"Simulation Modelling Practice and Theory","volume":"138 ","pages":"Article 103025"},"PeriodicalIF":3.5,"publicationDate":"2024-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142572105","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Underground rescue path planning based on a comprehensive risk assessment approach","authors":"Li Zhou ,&nbsp;Jinqiu Zhao ,&nbsp;Binglei Xie ,&nbsp;Yong Xu","doi":"10.1016/j.simpat.2024.103022","DOIUrl":"10.1016/j.simpat.2024.103022","url":null,"abstract":"<div><div>Fire incidents in underground environments, such as subway stations and shopping malls, pose significant hazards due to restricted ventilation and confined spaces. These conditions complicate rescue operations, particularly given the unpredictable nature of fires. Effective integration of fire risk assessment into rescue path planning is essential for ensuring both safety and operational efficiency. However, fire risk is inherently complex, varying across both temporal and spatial dimensions, and accurate assessment depends on precise fire situation inference. Despite advancements in fire simulation technologies, inconsistencies in geometric structures between computational units limit seamless integration with path planning models. Consequently, many existing studies rely on simplistic and less reliable linear fire inference models, compromising the safety of rescue operations. This paper addresses these challenges by proposing an underground rescue path planning method based on a comprehensive fire risk assessment, aimed at enhancing both safety and operational efficiency. A fire risk assessment approach, driven by fire situation inference, is introduced, employing a novel grid-matching transformation to capture the spatio-temporal dynamics of fire conditions using high-precision simulation software. Additionally, an improved A* algorithm is developed for real-time rescue path optimization, minimizing path risk based on the results of the risk assessment. The proposed method is validated through a detailed case study, demonstrating its effectiveness and reliability.</div></div>","PeriodicalId":49518,"journal":{"name":"Simulation Modelling Practice and Theory","volume":"138 ","pages":"Article 103022"},"PeriodicalIF":3.5,"publicationDate":"2024-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142535350","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Security-enabled optimal placement of drone-assisted intelligent transportation systems in mission-critical zones","authors":"Anu Monisha,&nbsp;K. Murugan","doi":"10.1016/j.simpat.2024.103023","DOIUrl":"10.1016/j.simpat.2024.103023","url":null,"abstract":"<div><div>A massive increase in highly dynamic vehicular nodes has resulted in network instability. Owing to the heterogeneous vehicular environment requires a multi-objective solution using a meta-heuristic optimization algorithm in the event of mission-critical zones with poor signal and secured quick decision-making system. Developed a security-enabled optimal placement of Drones or unmanned aerial vehicles (UAV) in mission-critical zones aims to achieve two primary objectives: 1) Maximizing the effectiveness of the intelligent transportation system (ITS) for traffic management and ubiquitous connectivity in mission-critical zones. 2) Ensuring robust security measures to protect sensitive data and infrastructure. This approach represents a cutting-edge solution for optimizing transportation systems in high-risk environments while safeguarding against potential security threats. The pre-deployment of drones and vehicles (V_OBU) parameter occurs during the registration phase, and then the mission-critical zone (MCZ) is identified and stored. The optimal position for drones in MCZs is determined by mathematically modeling a golden eagle optimization (GEO), which is inspired by varying the speed at different stages along their spiral trajectory for cruising and hunting. Furthermore, the robustness of the sensitive data and the real identity is ensured by using a biometric-based AKA algorithm utilizing the prevalent real-or-random (ROR) model and the formal security analysis. Based on a comparison of the simulation results, the proposed SDV-GEOAKA scheme outperforms the existing system- STPTC-A2 G, IoDAV, and IMOC with 99.36 % of PDR approximately, whereas, SDV-GEOAKA has maintained a load balancing factor with 0.01 to 0.1 when the transmission range between 0 and 60. When it comes to network coverage, proposed work outperforms with 99.95 % during the transmission range of 50 mW means it uses a minimum number of drones with maximum connectivity within the coverage range and also has significantly reduced the computation overhead and an increase in anomaly detection rate.</div></div>","PeriodicalId":49518,"journal":{"name":"Simulation Modelling Practice and Theory","volume":"138 ","pages":"Article 103023"},"PeriodicalIF":3.5,"publicationDate":"2024-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142653011","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Driving simulator validation studies: A systematic review","authors":"Siyang Zhang ,&nbsp;Chi Zhao ,&nbsp;Zherui Zhang ,&nbsp;Yecheng Lv","doi":"10.1016/j.simpat.2024.103020","DOIUrl":"10.1016/j.simpat.2024.103020","url":null,"abstract":"<div><div>Driving simulators (DS) serve as pivotal platforms for the rigorous testing of transportation systems and vehicles, offering a safe, controllable experimental environment with features like design visualization, scenario virtualization, and test data quantification. The validation of simulator experiments relies on the realism of the driving experience and scenario fidelity, crucial for assessing data reliability and result credibility. With the advent of autonomous driving technologies, the frequency of DS utilization has seen a marked expansion. Nonetheless, the discourse surrounding DS validation remains nascent, lacking a consolidated framework of standards and evaluative methodologies. This review endeavors to synthesize existing scholarly discourse and reports on the validation of driving simulators, further probing into the suitability of various driving scenarios and tasks. Common scenarios include car-following, lane-changing, and acceleration/deceleration, while tasks encompass human-machine co-piloting, takeover scenarios, and emergency evasion, considering driver conditions such as fatigue and distraction. Extracting universal indicators from various scenarios, including longitudinal and lateral velocities, accelerations, and trajectories, the paper summarizes the experimental workflow and commonly used statistical testing methods and psychophysiological monitoring devices for driving simulator validation. Considering the multidimensional factors influencing validation, this study discusses the relationships between simulation fidelity, degrees of freedom (DOF), and simulator sickness, proposing reference standards for driving simulator validation. This effort aims to advance the establishment of evaluation norms for simulation-based transportation and vehicle research, ensuring scientific rigor and empirical validity.</div></div>","PeriodicalId":49518,"journal":{"name":"Simulation Modelling Practice and Theory","volume":"138 ","pages":"Article 103020"},"PeriodicalIF":3.5,"publicationDate":"2024-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142535348","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Machine learning-assisted microscopic public transportation simulation: Two coupling strategies","authors":"Younes Delhoum,&nbsp;Olivier Cardin,&nbsp;Maroua Nouiri,&nbsp;Mounira Harzallah","doi":"10.1016/j.simpat.2024.103019","DOIUrl":"10.1016/j.simpat.2024.103019","url":null,"abstract":"<div><div>Evaluating the performance of public transportation, such as bus lines for example, is a major issue for Public Transportation operators. To be able to integrate specific and local behaviors, microscopic line simulations, modeling each buses on a daily basis, provide actual added value in terms of precision and quality. Carrying out more realistic and accurate simulations requires the use of appropriate parameters. To achieve this, machine learning models trained on real-world data can be used to feed and parameterize simulation models. To address this scientific question, it is necessary to determine how to efficiently integrate machine learning and simulation models. This study aims to couple machine learning and microscopic simulation models using various strategies, evaluate their accuracy and performance and discuss the advantages and drawbacks of each. A case study involving three bus lines was conducted, with results validated against real-world data, showing a good fit for both online and offline strategies. With the best simulation time, good accuracy and adequate travel times and bus punctuality, an offline strategy seems to stand out from other coupling strategies.</div></div>","PeriodicalId":49518,"journal":{"name":"Simulation Modelling Practice and Theory","volume":"137 ","pages":"Article 103019"},"PeriodicalIF":3.5,"publicationDate":"2024-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142314142","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A novel energy-efficient and cost-effective task offloading approach for UAV-enabled MEC with LEO enhancement in Internet of Remote Things networks","authors":"Amir Masoud Rahmani ,&nbsp;Amir Haider ,&nbsp;Shtwai Alsubai ,&nbsp;Abdullah Alqahtani ,&nbsp;Abed Alanazi ,&nbsp;Mehdi Hosseinzadeh","doi":"10.1016/j.simpat.2024.103018","DOIUrl":"10.1016/j.simpat.2024.103018","url":null,"abstract":"<div><div>The Internet of Remote Things (IoRT) involves networks of devices deployed in extensive and often remote areas, collecting data for transmission and processing. In such networks, Unmanned Aerial Vehicles (UAVs) gather data, which is then sent to Low Earth Orbit (LEO) satellites for processing. These systems often face significant challenges, particularly in task offloading. Conventional methods typically rely on static routing and scheduling algorithms that do not adapt to changing conditions and usually overlook the complexity of dynamic decision-making in harsh or isolated environments, thus failing to address the critical challenges of energy efficiency and latency. In this paper, we introduce a method comprised of a three-layer architecture. The first layer, the IoRT computing layer, uses Deep Q-Network (DQN) to optimize local decisions based on device constraints and task urgency. The second layer features UAVs serve as Mobile Edge Computing (MEC), which not only processes data but also decides whether to process tasks locally or offload them to LEO satellites, utilizing the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) for this decision-making process. The third LEO Satellite Layer has a high computational capacity to handle offloaded tasks. Simulation results demonstrate notable improvements: compared to another method, the proposed model shows a 14.73 % reduction in energy consumption and a 23.13 % decrease in latency while reducing execution costs by an average of 28.7 %.</div></div>","PeriodicalId":49518,"journal":{"name":"Simulation Modelling Practice and Theory","volume":"137 ","pages":"Article 103018"},"PeriodicalIF":3.5,"publicationDate":"2024-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142311152","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An AI-driven solution to prevent adversarial attacks on mobile Vehicle-to-Microgrid services","authors":"Ahmed Omara,&nbsp;Burak Kantarci","doi":"10.1016/j.simpat.2024.103016","DOIUrl":"10.1016/j.simpat.2024.103016","url":null,"abstract":"<div><p>With the increasing integration of Artificial Intelligence (AI) in microgrid control systems, there is a risk that malicious actors may exploit vulnerabilities in machine learning algorithms to disrupt power generation and distribution. In this work, we study the potential impacts of adversarial attacks on Vehicle-to-Microgrid (V2M), and discuss potential defensive countermeasures to prevent these risks. Our analysis shows that the decentralized and adaptive nature of microgrids makes them particularly vulnerable to adversarial attacks, and highlights the need for robust security measures to protect against such threats. We propose a framework to detect and prevent adversarial attacks on V2M services using Generative Adversarial Network (GAN) model and a Machine Learning (ML) classifier. We focus on two adversarial attacks, namely inference and evasion attacks. We test our proposed framework under three attack scenarios to ensure the robustness of our solution. As the adversary’s knowledge of a system determines the success of the executed attacks, we study four gray-box cases where the adversary has access to different percentages of the victim’s training dataset. Moreover, we compare our proposed detection method against four benchmark detectors. Furthermore, we evaluate the effectiveness of our proposed method to detect three benchmark evasion attack. Through simulations, we show that all benchmark detectors fail to successfully detect adversarial attacks, particularly when the attacks are intelligently augmented, obtaining an Adversarial Detection Rate (ADR) of up to 60.4%. On the other hand, our proposed framework outperforms the other detectors and achieves an ADR of 92.5%.</p></div>","PeriodicalId":49518,"journal":{"name":"Simulation Modelling Practice and Theory","volume":"137 ","pages":"Article 103016"},"PeriodicalIF":3.5,"publicationDate":"2024-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1569190X24001308/pdfft?md5=9c9f6008f69a380b2cf7dbcf98131bfe&pid=1-s2.0-S1569190X24001308-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142172556","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Advancements in traffic simulation for enhanced road safety: A review","authors":"Aliyu Mustapha ,&nbsp;Ahmad Majdi Abdul-Rani ,&nbsp;Noorhayati Saad ,&nbsp;Mazli Mustapha","doi":"10.1016/j.simpat.2024.103017","DOIUrl":"10.1016/j.simpat.2024.103017","url":null,"abstract":"<div><p>Traffic simulation techniques play a crucial role in transportation engineering, offering a sophisticated framework for analysing the intricate dynamics within transportation systems. This paper thoroughly reviews the latest developments in traffic simulation techniques and their applications in improving road safety. Drawing from a comprehensive analysis of recent literature from the Scopus database spanning 2014 to 2024, this review highlights the various analytic methods employed in traffic simulation and their practical applications. Focusing mainly on microsimulation techniques, the study underscores their ability to provide proactive and reactive surrogate safety measures, offering stakeholders valuable insights into traffic safety dynamics. Leveraging methodologies such as microsimulation modelling, surrogate safety measures, statistical model creation, simulation-based conflict prediction, and sensitivity analysis, contemporary research aims to address safety concerns comprehensively. However, the absence of comprehensive crash simulation models presents a significant challenge, raising doubts about the efficacy of traffic simulation in road safety assessment. To overcome this challenge, interdisciplinary research is essential to develop practical solutions that harness technological advancements and foster collaboration across domains. By overcoming existing limitations and refining methodologies, researchers can pave the way for more robust and comprehensive approaches to traffic safety evaluation, contributing significantly to the global goal of enhancing road safety.</p></div>","PeriodicalId":49518,"journal":{"name":"Simulation Modelling Practice and Theory","volume":"137 ","pages":"Article 103017"},"PeriodicalIF":3.5,"publicationDate":"2024-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142230422","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}