Simulation Modelling Practice and Theory最新文献

The rapid evolution of vehicular communication has led to numerous new algorithms and applications based on this technology. Neglecting issues arising from wireless communication, such as the loss of information and delays, can result in problems such as reduced performance or compromised safety. However, while simulating V2X demands significant computational resources, it proves unsuitable for complex testing setups, including mixed-reality testing. This paper enhances V2X simulation by relying on an ecosystem based on SUMO, OMNeT++, Veins, and INET simulation tools. The proposed novel method introduces mesoscopic simulation in Vehicular Ad-hoc Networks to increase simulation performance to a level where real-time behavior is achievable. Meanwhile, it can also be beneficial in the acceleration of regular simulations. The presented solution introduces Meso nodes that are capable of aggregating communication across an entire traffic area, facilitated by a neural network function approximator. Results showed substantial performance gain while simulation accuracy was preserved.

Public mass events require thorough planning on allocating resources such as paramedics, police officers, urban cleaning teams, and their equipment (ambulances, patrol cars, garbage collection trucks, and other urban cleaning vehicles). Testing different scenarios of event venue layout and crowd behavior at the end of an event might be useful to plan the event and said resource allocation.

Our main objective is to model the non-urgent egress of participants at the end of an event, with possible applications for event management. That is when some resources are released (police and paramedics) and others are requested (urban cleaning teams).

Using the agent-based GAMA platform, we implemented a spatially explicit simulation model upon an extension of the Social Force Model that considers group behavior, and a novel implementation of the “social retention” phenomenon, to simulate non-urgent egress from public space mass gathering events. Focus groups with architecture, geography, and urban ergonomics experts were conducted for face validation and improvement of the model.

We present the outcome of a series of simulations of a scenario mimicking a real-life music event that took place in a square in downtown Lisbon, Portugal. Cell phone data captured during the event was used to calibrate the model. We analyzed model performance when the number of pedestrian agents increases, to assess the feasibility of using our approach in participatory discussions with stakeholders responsible for resources management.

On average, the egress evolution obtained in the simulations fit well with the evolution of cell phone counts captured during the event. The behavior of groups of agents evidenced real-life phenomena, such as the persistence of group cohesion and repulsion interactions (both with architectural obstacles and other agents).

Model performance degradation with the increasing number of agents may hamper the usage of this model/platform for participatory meetings, due to the incurred delay in obtaining results. To mitigate this problem, we plan to explore parallelization strategies for agent-based simulation, such as using GPUs.

Internet of Vehicles (IoV) relies heavily on its computing capability to facilitate various vehicular applications. Since the cloud computing or mobile edge computing (MEC) only cannot satisfy the latency requirement due to the limitation of the resource coverage, the cloud–edge-end cooperative computing has become an emerging paradigm. A comprehensive IoV architecture is considered and a joint optimization problem is formulated to minimize the system function value. To optimize the resource allocation and the task offloading strategy, the simulated spring system algorithm (SSSA) is designed where the initial problem is decoupled into two sub-problems with priority. The first one is to allocate computing resources based on KKT conditions, thus the individual optimal solution is achieved. The second one is solved based on the idea of simulated spring system such that the task offloading strategy is obtained. Two sub-problems iterate mutually to update each other until finishing the binary tree traversal. Thus, the proposed solution adapts to various conditions and the computational complexity is also reduced compared with traditional methods. Simulation verifies that the proposed algorithm reduces the maximum system function value by about 31% compared with the benchmark methods and performs efficiently in various road conditions.

Fifth generation communication networks (5G) has received a great deal of attention from academia and industry alike, which will enable a wide variety of vertical applications by connecting heterogeneous devices and machines. Assessing availability and reliability in many circumstances and environments is critical. Researchers have recently focused on investigating and analyzing new multimedia networks with artificial intelligence (AI) technologies to achieve higher data rates and secure communication traffic between parties. User information privacy and security are of vital importance and of growing concerns that present evolving challenges to overcome in preventing attacks. Man-in-the-middle (MITM) attack is considered one of the most common attacks, where an attacker can impersonate one of the parties in a communication system to steal user data or forge the malicious data. Due to the limitation of using conventional cryptographic techniques for mobile networks and similar systems, new methods have been introduced to validate and authenticate transmitted signals dynamically, depending on the physical layer. In this paper, we present the distance-time directional delay (DTDD) model to detect the MITM attack in a variety of contexts and scenario. Indoor hotspots (InH) and urban micro-cellular (UMi) propagation environments were investigated to verify the reliability of the proposed approaches using realistic 5G millimeter-wave configurations and system setups. Simulations have been constructed based on the mmWave 5G channel simulator tool NYUSIM, in conjunction with a collection of machine learning algorithms (ML) including the extreme gradient boosting (XGBoost) and light gradient boosting machine (LGBM) as the core of the presented models and methodologies. Numerical simulations results produced a detection accuracy approaching 100% in the InH environment scenario, whereas for UMi environment scenario, a detection accuracy approaching 99% was attained.

Energy harvesting plays a significant role in the Internet of Things (IoT). Indeed, although numerous approaches exist to limit the system’s power consumption, the energy provided by the battery remains constrained, thereby limiting the system’s lifetime. Energy harvesting represents an interesting technique that allows a set of devices in an IoT architecture to operate for a potentially infinite time without the need for battery replacement or recharge. This work presents a formal modeling framework for the performance evaluation of energy harvesting architectures and strategies in IoT systems. We present a model-based approach using UPPAAL to model and analyze IoT device lifetimes and capture the energy-related behavior of nodes and various energy harvesters. Furthermore, the model is calibrated using measurements acquired from real-life IoT applications to demonstrate the effectiveness of the proposed model and its ability to investigate various energy-related aspects.

The modeling of driving behavior is pivotal for the accurate simulation of traffic scenarios and for providing human-like decision-making of autonomous driving systems. Car-following (CF) and lane-changing (LC) behaviors are continuous maneuvers within traffic flow, generally modeled separately in the literature. The coherence between these two behaviors may be ignored, leading to unrealistic behavioral simulations. Therefore, this paper establishes a risk field-based driving behavior model for two-dimensional motion, ensuring coherent modeling of CF and LC behaviors under a unified framework. First, a risk quantification method is developed to calculate the risk in two-dimensional scenarios, accounting for risk over the preview time. A cubic polynomial is applied to generate path curves that align with vehicle dynamics. Second, the enhanced behavior model primarily comprises two integral components: path and trajectory planning. These two components aim to identify the path or trajectory that maximizes the benefit while meeting the desired risk. Third, the maximum acceptable risk, representing a higher risk than the desired risk, is defined to facilitate path adjustment and avoid frequent path adjustment. Finally, the proposed model is proved through comparisons with existing models using driving data. Several cases are employed for further analysis to show the model's rationality and potential in various aspects. This study develops the previous risk field-based behavior model from one-dimensional to two-dimensional scenarios, furnishes a unified framework for elucidating driving behavior in various scenarios, and contributes to the progress of behavior modeling.

As a device characterized by multiple degrees of freedom in one driving unit, analytical electromagnetic torque modeling is needed for the rotor position tracking control of a Permanent Magnet Spherical Motor (PMSpM). In this paper, Extreme Gradient Boosting (XGBoost) was proposed to be employed for establishing the output relationship between the rotor position and the electromagnetic torque of PMSpM. The Finite Element Method (FEM) was applied to obtain train data and test data concerning the rotor position and electromagnetic torque of PMSpM. Particle Swarm Optimization (PSO) was applied to optimize partial parameters of XGBoost, which serves to enhance the modeling accuracy of electromagnetic torque via XGBoost. The predictive results of algorithms, including Random Forest (RF), Gradient Boosting Regression Tree (GBRT), Multi-task Gaussian Process (MTGP), and XGBoost, were compared with FEM results and experimental results over multiple indicators. The capability of XGBoost has been validated not only to perform modeling tasks within an abbreviated time span but also to generate models that display amplified accuracy and efficiency.

We present a novel blockchain-based Federated Learning (FL) system that introduces a Byzantine-resilient consensus protocol that performs well in the presence of adversarial participants. Unlike existing state-of-the-art, this system can be deployed in a fully decentralized manner, meaning it does not rely on any single actor to function correctly. Using a Smart Contract-driven workflow coupled with a commitment scheme and a differential privacy-based solution, we ensure training integrity, prevent plagiarism, and protect against leakage of sensitive data while performing effective federated training. We demonstrate the system’s effectiveness by performing simulation and implementation of an end-to-end proof of concept. Our practical implementation showcases the system’s efficiency on a single computer with multiple trainers, revealing low memory demands and manageable network and block I/O, which suggest scalability to larger, more complex networks. The paper concludes by exploring future enhancements, including advanced cryptographic methods for enhanced privacy and potential applications extending the system’s utility to broader domains within FL. Our work lays the groundwork for a new generation of decentralized learning systems, promising increased adoption in real-world scenarios where data privacy and security are of paramount concern.

Cycling as a mode of transport is on an upward trend as a low-emission alternative to driving in urbanized areas nowadays. With the increasing number of cyclists, it is of great importance to assess the capacity of cycling infrastructure in practice. Simulation models are useful tools to investigate bicycle flow performance considering cyclists’ distinct moving behaviors. However, existing bicycle simulation models are restricted by either space discretization, lane-based setup, adaptation from models for car traffic, or complicated calibration requirement in a force-based environment. In addition, cyclists’ decision-making ability in the operational-level cycling behavior are not well-captured in these models. This paper proposes a comprehensible microscopic bicycle simulation model which includes a detailed decision-making process and the ability to simulate continuous-space lateral movement. The model consists of three levels, maneuver decision, movement planning, and physical acceleration. It is able to simulate bicycle flow dynamics in undersaturated traffic conditions on an exclusive bike path. As we do not intend to show the empirical validity of the proposed model, the simulation experiment aims at verifying the model and exploring bicycle flow performance in various scenarios by estimating the fundamental diagrams (FDs). The effect of different path widths on bicycle flow capacity is first explored. Other behavioral factors, including desired speed heterogeneity, overtaking incentive, and safety region size perceived by cyclists, which can potentially influence the shape of the FD are also tested. The model can be further extended to simulate relatively complex cycling behavior with cooperative and anticipative strategies and investigate bicycle flow characteristics in congested traffic conditions.