NeTS: Small: MobiBench: Benchmarking Mobility Models for Simulation and Design of Future Networks

随着新的移动应用、协议和服务的激增，迫切需要新的工具来评估和测试移动网络。该项目旨在建立一个系统框架，以评估方案和测试套件的形式为用户和车辆移动性的移动网络协议和服务产生基准(称为MobiB边)。研究有两条主线。首先，引入了一个多维度量空间来表征用户(人)的移动性，并将其应用于移动性测量和模型。指标跨度为：一、个人、二。结对配对和III。集体移动性维度，并促进对移动性模型和网络协议的全面分析和评估。开发了新颖的跟踪驱动模型，以准确捕获用户移动性指标和移动网络性能。眼镜蛇模型旨在捕获移动社交网络中的集体社区行为。第二，收集和分析新的广泛的车辆痕迹，包括来自世界各地数千个网络摄像头的车辆图像数据。开发了车辆密度分布和模型，以帮助建立真实的车辆机动性模型和基准。车辆密度的估计使用自适应的背景减去算法进行图像处理。这项工作将导致建立一个关于行人和车辆流动性的基准、轨迹、模型和测试套件的库，用于对未来的移动协议、网络和服务进行现实评估。这项研究的结果将影响移动网络研究，并有助于交通控制和交通拥堵缓解。

With the explosion of new mobile apps, protocols and services, there is a compelling need for new tools to evaluate and test mobile networks. This project aims to establish a systematic framework to produce benchmarks (called MobiBench) in the form of evaluation scenarios and test-suites for mobile networking protocols and services for user and vehicular mobility. Two threads of research are pursued. First, a multi-dimensional metric space is introduced for characterization of user (human) mobility, to be applied to mobility measurements and models. The metrics span: i. individual, ii. pair-wise and iii. collective mobility dimensions, and facilitate comprehensive analysis and evaluation of mobility models and networking protocols. Novel, trace-driven models are developed to accurately capture user mobility metrics and mobile network performance. The COBRA model is designed to capture collective communal behavior in mobile social networks. Second, new extensive vehicular traces are collected and analyzed, including vehicular imagery data from thousands of webcams around the world. Vehicular density distributions and models are developed to aid in establishing realistic vehicular mobility models and benchmarks. Estimation of vehicular density uses adapted background subtraction algorithms for image processing. This work will result in the establishment of a library of benchmarks, traces, models and test-suites for pedestrian and vehicular mobility, to be used for the realistic evaluation of future mobile protocols, networks and services. Results of this research will impact mobile networking research and aid in traffic control and transportation congestion mitigation.