A Multiobjective Particle Swarm Optimization-Based Partial Classification for Accident Severity Analysis

Reducing accident severity is an effective way to improve road safety. In this article, a novel multiobjective particle swarm optimization (MOPSO)-based partial classification method is employed to identify the contributing factors that impact accident severity. The accident dataset contains only a few fatal accidents but the patterns of fatal accidents are of great interest to traffic agencies. Partial classification can deal with the unbalanced dataset by producing rules for each class. The rules can be evaluated by several conflicting criteria such as accuracy and comprehensibility. A MOPSO is applied to discover a set of Pareto optimal rules. The accident data of Beijing between 2008 and 2010 are used to build the model. The proposed approach is compared with several rule-learning algorithms. The results show that the proposed approach can generate a set of accurate and comprehensible rules, which can indicate the relationship between risk factors and accident severity.     

On the Design of Safety Communication Systems for Vehicles

Statistics show that the number of casualties due to traffic accidents exceeds one million each year. For the development of systems that prevent vehicle collisions, vehicular communication is considered a promising technology. This paper focuses on design aspects of communication systems that support the development of collaborative active safety systems such as collision warning and collision avoidance. We introduce a design method for safety communication systems that includes a set of analyses and a reasoning system for modeling and analyzing traffic scenarios. An overview of a specific solution for communication is presented in this paper. This solution proposes techniques for network organization and data dissemination that make use of contextual information. This allows the development of a communication system that is adaptable to the specifics of the traffic situation.     

Quality of service based resource allocation for scheduled lightpath demands

In this paper, we consider resource allocation under the scheduled traffic model, where the setup and teardown times of scheduled demands are known in advance. A number of integer linear program (ILP) solutions for this problem have been presented in the literature. Most of these ILPs minimize the number of wavelength links. A more appropriate metric, for wavelength routed, all-optical networks, is to minimize the congestion (the maximum number of lightpaths on a single fiber link) of the network. We present a new ILP formulation for routing and wavelength allocation, under the scheduled traffic model that minimizes the congestion of the network. We propose two levels of service, where idle backup resources can be used to carry low-priority traffic, under fault-free conditions. When a fault occurs, and resources for a backup path need to be reclaimed, any low-priority traffic on the affected channels is dropped. The results demonstrate that this can lead to significant improvements over single service level models. We also show that the complexity of our formulation (in terms of the number of integer variables) is lower, even compared to existing approaches, which only allow a single level of service. Therefore, we are able to generate optimal solutions for practical networks within a reasonable amount of time, whereas existing formulations often do not find the optimal solution even after 2 h. Finally, we present a simple and fast heuristic that can quickly generate good solutions for much larger networks.     

Diagnosing resource usage failures in multi-agent systems

In the not-so-far future, autonomous vehicles will be ubiquitous and, consequently, need to be coordinated to avoid traffic jams and car accidents. A failure in one or more autonomous vehicles may break this coordination, resulting in reduced efficiency (due to traffic load) or even bodily harm (due to accidents). The challenge we address in this paper is to identify the root cause of such failures. Identifying the faulty vehicles in such cases is crucial in order to know which vehicles to repair to avoid future failures as well as for determining accountability (e.g., for legal purposes). More generally, this paper discusses multi-agent systems (MAS) in which the agents use a shared pool of resources and they coordinate to avoid resource contention by agreeing on a temporal resource allocation. The problem we address, called the Temporal Multi-Agent Resource Allocation (TMARA) diagnosis problem (TMARA-Diag), is to find the root cause of failures in such MAS that are caused by malfunctioning agents that use resources not allocated to them. As in the autonomous vehicles example, such failures may cause the MAS to perform suboptimally or even fail, potentially causing a chain reaction of failures, and we aim to identify the root cause of such failures, i.e., which agents did not follow the planned resource allocation. We show how to formalize TMARA-Diag as a model-based diagnosis problem and how to compile it to a set of logical constraints that can be compiled to Boolean satisfiability (SAT) and solved efficiently with modern SAT solvers. Importantly, the proposed solution does not require the agents to share their actual plans, only the agreed upon temporal resource allocation and the resources used at the time of failure. Such solutions are key in the development and success of intelligent, large, and security-aware MAS.     

无线异构网络的资源分配策略

无线异构网络要求实现不同工作模式通信系统间的资源合理分配。为了获得不同通信系统对无线资源的有效利用,参考XG架构定义了可承载不同通信系统业务需求的多维度资源容器,提出二级资源分配策略及相应的资源分配算法来实现多维度资源容器与各个通信系统之间各种业务承载需求的匹配。仿真与性能分析验证了该资源分配策略在实现不同通信系统共存的条件下,提高了无线网络频谱资源利用率。     

基于多智能体Q学习的异构车载网络选择方法

异构车载网络环境下如何选择接入网络对于车载终端用户的服务体验而言至关重要,目前基于Q学习的网络选择方法利用智能体与环境的交互来迭代学习网络选择策略,从而实现较优的网络资源分配.然而该类方法通常存在状态空间过大引起迭代效率低下和收敛速度较慢的问题,同时由于Q值表更新产生的过高估计现象容易导致网络资源利用不均衡.针对上述问...     

异构云系统中基于智能优化算法的多维资源公平分配

资源分配策略的研究一直是云计算领域研究的热点和难点,针对异构云计算环境下多维资源的公平分配问题,结合基因算法（GA）和差分进化算法（DE）,分别给出了两种兼顾分配公平性和效率的资源分配策略,改进了解矩阵表达式使异构云系统中的主资源公平分配（DRFH）模型转化成为整数线性规划（ILP）模型,并提出了基于最大任务数匹配值（MTM）的初始解产生机制和使不可行解转化为可行解的修正操作,以此提高算法的收敛速度,使其能够快速有效地得到最优分配方案。实验结果表明,基于GA和DE算法的多维资源公平分配策略可以得到近似最优解,在最大化最小主资源份额目标值和资源利用率方面明显优于Best-Fit DRFH和Distributed-DRFH,而且针对不同任务类型的资源需求,具有较强的自适应能力。     

基于QMix的车辆云计算资源动态分配方法

城市交通智能化和通信技术的进步会产生大量基于车辆的应用,但目前车辆有限的计算资源无法满足车辆应用的计算需求与延迟性约束。车辆云（VC）可以高效地调度资源,从而显著降低任务请求的延迟与传输成本。针对VC环境下任务卸载与计算资源分配问题,提出一个考虑异质车辆和异质任务的计计资源分配算法。对到达的任务构建M/M/1队列模型与计算模型,并定义一个效用函数以最大化系统整体效用。针对环境中车辆地理分布的高度动态系统变化,提出基于双时间尺度的二次资源分配机制（SRA）,使用两个不同时间尺度的资源分配决策动作,对其分别构建部分可观测马尔可夫决策过程。两个决策动作通过执行各自的策略获得的奖励进行连接,将问题建模为两层计算资源分配问题。在此基础上提出基于二次资源分配机制的多智能体算法SRA-QMix求解最优策略。仿真结果表明,与深度确定性策略梯度算法对比,该算法的整体效用值和任务完成率分别提高了70%、6%,对于QMix和MADDPG算法分别应用SRA后的任务完成率分别提高了13%与15%,可适用于动态的计算资源分配环境。     

Swarm intelligence for traffic light scheduling: Application to real urban areas

Congestion, pollution, security, parking, noise, and many other problems derived from vehicular traffic are present every day in most cities around the world. The growing number of traffic lights that control the vehicular flow requires a complex scheduling, and hence, automatic systems are indispensable nowadays for optimally tackling this task. In this work, we propose a Swarm Intelligence approach to find successful cycle programs of traffic lights. Using a microscopic traffic simulator, the solutions obtained by our algorithm are evaluated in the context of two large and heterogeneous metropolitan areas located in the cities of Málaga and Sevilla (in Spain). In comparison with cycle programs predefined by experts (close to real ones), our proposal obtains significant profits in terms of two main indicators: the number of vehicles that reach their destinations on time and the global trip time.     

Distributed resource allocation of second‐order multiagent systems with exogenous disturbances

In this paper, we study the resource allocation problem of second‐order multiagent systems with exogenous disturbances, and the communication networks are weight‐balanced digraphs. Different from the well‐studied resource allocation problems, our problem involves the disturbed second‐order dynamics of agents. In order to achieve the optimal allocation, we propose a distributed algorithm based on gradient descent and internal model approach. Furthermore, we analyze the convergence of the algorithm by constructing a suitable Lyapunov function. Moreover, we prove that the agents in the network can achieve the exact optimal allocation even in the presence of external disturbances. Finally, we provide two examples to illustrate our result.     

Resource allocation for multimedia traffic flows using rate variance envelopes

In order for networks to support the delay and loss requirements of interactive multimedia applications, resource management algorithms are needed that efficiently allocate network resources. In this paper, we introduce a new resource allocation scheme based on rate variance envelopes. Such envelopes capture a flow's burstiness properties and autocorrelation structure by characterizing the variance of its rate distribution over intervals of different length. From this traffic characterization, we develop a simple and efficient resource allocation algorithm for static priority schedulers by employing a Gaussian approximation over intervals and considering a maximal busy period. Our approach supports heterogeneous quality-of-service requirements via our consideration of prioritized service disciplines, and supports heterogeneous and bursty traffic flows via our general framework of traffic envelopes. To evaluate the scheme, we perform trace-driven simulation experiments with long traces of compressed video and show that our approach is accurate enough to capture most of the available statistical multiplexing gain, achieving average network utilizations of up to 90% for these traces and substantially outperforming alternate schemes.     

Driver information system: a combination of augmented reality,deep learning and vehicular Ad-hoc networks

Improving traffic safety is one of the important goals of Intelligent Transportation Systems (ITS). In vehicle-based safety systems, it is more desirable to prevent an accident than to reduce severity of injuries. Critical traffic problems such as accidents and traffic congestion require the development of new transportation systems. Research in perceptual and human factors assessment is needed for relevant and correct display of this information for maximal road traffic safety as well as optimal driver comfort. One of the solutions to prevent accidents is to provide information on the surrounding environment of the driver. Augmented Reality Head-Up Display (AR-HUD) can facilitate a new form of dialogue between the vehicle and the driver; and enhance ITS by superimposing surrounding traffic information on the users view and keep drivers view on roads. In this paper, we propose a fast deep-learning-based object detection approaches for identifying and recognizing road obstacles types, as well as interpreting and predicting complex traffic situations. A single convolutional neural network predicts region of interest and class probabilities directly from full images in one evaluation. We also investigated potential costs and benefits of using dynamic conformal AR cues in improving driving safety. A new AR-HUD approach to create real-time interactive traffic animations was introduced in terms of types of obstacle, rules for placement and visibility, and projection of these on an in-vehicle display. The novelty of our approach is that both global and local context information are integrated into a unified framework to distinguish the ambiguous detection outcomes, enhance ITS by superimposing surrounding traffic information on the users view and keep drivers view on roads.     

Improving performance by network-aware virtual machine clustering and consolidation

Modern data center consists of thousands of servers, racks and switches. Complicated structure means it requires well-designed algorithms to utilize resources of data centers efficiently. Current virtual machine scheduling algorithms mainly focus on the initial allocation of virtual machines based on the CPU, memory and network bandwidth requirements. However, when tasks finished or lease expired, related virtual machines would be deleted from the system which would generate resource fragments. Such fragments lead to unbalanced resource utilization and decline of communication performance. This paper investigates the network influence on typical applications in data centers and proposed a self-adaptive network-aware virtual machine clustering and consolidation algorithm to maintain an optimal system-wide status. Our consolidation algorithm periodically checks whether consolidation is necessary and then clusters and consolidates virtual machines to lower communication cost with an online heuristic. We used two benchmarks in a real environment to examine network influence on different tasks. To evaluate the advantages of the proposed algorithm, we also built a cloud computing testbed. Real workload trace-driven simulations and testbed-based experiments showed that, our algorithm greatly shortened the average finish time of map-reduce tasks and reduced time delay of web applications. Simulation results showed that our algorithm considerably reduced the amount of high-delay jobs, lowered the average traffic passed through aggregate switches and improved the communication ability among virtual machines.     

Toward end-to-end fairness: a framework for the allocation of multiple prioritized resources in switches and routers

As flows of traffic traverse a network, they share with other flows a variety of resources such as links, buffers and router CPUs in their path. Fairness is an intuitively desirable property in the allocation of resources in a network shared among flows of traffic from different users. While fairness in bandwidth allocation over a shared link has been extensively studied, overall end-to-end fairness in the use of all the resources in the network is ultimately the desired goal. End-to-end fairness becomes especially critical when fair allocation algorithms are used as a component of the mechanisms used to provide end-to-end quality-of-service guarantees. This paper seeks to answer the question of what is fair when a set of traffic flows share multiple resources in the network with a shared order of preference for the opportunity to use these resources. We present the Principle of Fair Prioritized Resource Allocation or the FPRA principle, a powerful extension of any of the classic notions of fairness such as max–min fairness, proportional fairness and utility max–min fairness defined over a single resource. We illustrate this principle by applying it to a system model with a buffer and an output link shared among competing flows of traffic. To complete our illustration of the applicability of the FPRA principle, we propose a measure of fairness and evaluate representative buffer allocation algorithms based on this measure. Besides buffer allocation, the FPRA principle may also be used in other contexts in data communication networks and operating system design.     

Toward intelligent driver-assistance and safety warning system

A major problem associated with the rapid growth in automotive production is an increase in traffic congestion and accidents, especially in big cities of China. To solve the problem, the government has been increasing funds for improving the traffic infrastructure, enforcing traffic laws, and educating drivers about traffic regulations. In addition, research institutes have launched R&D projects in driver assistance and safety warning systems. In particular, in 1999, the Chinese Academy of Sciences' Intelligent Control and Systems Engineering Center started the Intelligent Vehicle Platforms project. The project aims to promote the use of intelligent technology for safe, efficient, and smart vehicles and to prototype vehicular electronic and sensory products and systems for the Chinese automotive industry. One of the project's key objectives is to develop a vehicular application-specific operating system (vASOS). The National Science Foundation of China and the CAS Knowledge Innovation Program support the project. Supported by the Vehicular Embedded Computing Platform project, the Xi'an Jiaotong University (XJTU-"Jiao Tong" means transportation in Chinese) Institute of Artificial Intelligence and Robotics and the CAS have collaborated to develop intelligent driver-assistance and safety warning systems for passenger vehicles, particularly GPS-and vision-based systems.     

A deadlock avoidance approach for nonsequential resource allocation systems

The paper concentrates on the deadlock-avoidance problem for a class of resource allocation systems modeling manufacturing systems. In these systems, a set of production orders have to be executed in a concurrent way. To be executed, each step of each production order needs a set of reusable system resources. The competition for the use of these resources can lead to deadlock problems. Many solutions, from different perspectives, can be found in the literature for deadlock-related problems when the production orders have a sequential nature [sequential resource allocation systems (S-RAS)]. However, in the case in which the involved processes have a nonsequential nature [nonsequential resource allocation systems (NS-RAS)], the problem becomes more complex. In this paper, we propose a deadlock avoidance algorithm for this last class of systems. We also show the usefulness of the proposed solution by means of its application to a real system.     

NCTUns tool for wireless vehicular communication network researches

Several goals such as improving road safety and increasing transport efficiency are being pursued in intelligent transportation systems (ITS). Wireless vehicular communication is one technology to achieve these goals. Conducting vehicular experiments on the roads is an approach to studying the effectiveness of wireless vehicular communication. However, such an approach is costly, hard-to-control (repeat), dangerous, and infeasible when many vehicles and people are involved in the field trial. In contrast, the simulation approach does not have these problems. It is a very useful approach and complements the field trial approach. This paper presents NCTUns, an open source integrated simulation platform, for wireless vehicular communication network researches. This tool tightly integrates network and traffic simulations and provides a fast feedback loop between them. Therefore, a simulated vehicle can quickly change its driving behavior such as moving speed and direction when it receives a message from the wireless vehicular communication network. This capability is required by several novel ITS applications such as active collision avoidance systems. In this paper, we present the design, implementation, validation, and performance of this tool.     

电力线通信系统中跨层的用户调度和资源分配

针对多用户多业务基于正交频分多址的电力线通信系统,提出一种在数据链路控制层进行用户调度和在物理层进行资源分配的多层多目标最优的跨层资源分配算法,其用户调度根据所有用户的服务质量(QoS)满意程度、QoS要求、业务包模型、信道状态信息和队列状态信息,从所有用户中选出要服务的用户和确定这些用户的最优跨层参数;其资源分配则根据所有调度用户的QoS要求、最优跨层参数和信道状态信息,先把功率按地窖注水原理分给每个子载波,再把每个子载波最优地分给调度用户并采用逐比特加载查表算法调整其上分配的功率和比特。最后在典型的电力线信道环境下对算法进行仿真,结果表明新算法在系统资源大范围变化时也能保障用户的服务质量,同时有效地提高系统资源的利用。     

The performance impact of traffic patterns on routing protocols in mobile ad hoc networks

As mobile ad hoc network (MANET) systems research has matured and several testbeds have been built to study MANETs, research has focused on developing new MANET applications such as collaborative games, collaborative computing, messaging systems, distributed security schemes, MANET middleware, peer-to-peer file sharing systems, voting systems, resource management and discovery, vehicular computing and collaborative education systems. The growing set of diverse applications developed for MANETs pose far more complex traffic patterns than the simple one-to-one traffic pattern, and hence the one-to-one traffic pattern widely used in previous protocol studies has become inadequate in reflecting the relative performance of these protocols when deployed to support these emerging applications.As a first step towards effectively supporting newly developed and future diverse MANET applications, this paper studies the performance impact of diverse traffic patterns on routing protocols in MANETs. Specifically, we propose a new communication model that extends the previous communication model to include a more general traffic pattern that varies the number of connections per source node. We study the performance impact of traffic patterns on various routing protocols via detailed simulations of an ad hoc network of 112 mobile nodes. Our simulation results show that many of the conclusions drawn in previous protocol comparison studies no longer hold under the new traffic patterns. These results motivate the need for performance evaluation of ad hoc networks to not only include rich and diverse mobility models as has been done in the past but also include diverse traffic patterns that stress a wide set of protocol design issues.     

Multi-task emotion communication system with dynamic resource allocations

The research of long-distance emotion communication and interaction without time and space constraints is an important area in human-robot interaction (HRI) systems. Although many methods of emotion recognition have been studied for analyzing various emotion signals, the resource allocation of transmission for emotion communication signals of many pairs of users has not been fully considered nor solved at the same time. This paper proposes a new multi-task emotion communication system (MEmSys), where the transmission resources allocation issue is considered. Specifically, we firstly establish the architecture of MEmSys, and the entire emotion interaction process of the proposed system is introduced. By analyzing fairness and urgency of different tasks, the mathematical expressions of the minimum task transmission rates for all user pairs are derived. Then, a dynamic optimal resource allocation scheme is presented to maximize the sum of the task transmission rates in the proposed system. Moreover, simulation experiment results and performance analyses show that the resource utilization ratio of the proposed allocation scheme for multiple user pairs is significantly improved compared to the single user pair system. Finally, future works are discussed to provide insights for our next research.