基于Copula模型实现的车辆碰撞协同预警

近十年来,智能交通系统(ITS)和车载网络开始融合紧密,从而彻底改变了事件侦查和交通信息传送的方式。车辆碰撞协同预警系统是其中一个重要方向。根据预设的一系列车辆碰撞协同预警系统(CCWS)的参数,如前后两辆车之间的距离、司机的反应时间、相对速度和加速性能等,采用Copula模型对这些参数进行分析并得到车辆发生碰撞的条件概率,从而为其建立相互的联系,从全局的角度多方面对碰撞作出预测。最后的仿真验证了此分析结果的正确性。

Recent advances in mobile edge computing and content caching

The demand for digital media services is increasing as the number of wireless subscriptions is growing exponentially.In order to meet this growing need,mobile wireless networks have been advanced at a tremendous pace over recent days.However,the centralized architecture of existing mobile networks,with limited capacity and range of bandwidth of the radio access network and low bandwidth back-haul network,can not handle the exponentially increasing mobile traffic.Recently,we have seen the growth of new mechanisms of data caching and delivery methods through intermediate caching servers.In this paper,we present a survey on recent advances in mobile edge computing and content caching,including caching insertion and expulsion policies,the behavior of the caching system,and caching optimization based on wireless networks.Some of the important open challenges in mobile edge computing with content caching are identified and discussed.We have also compared edge,fog and cloud computing in terms of delay.Readers of this paper will get a thorough understanding of recent advances in mobile edge computing and content caching in mobile wireless networks.

New Modified Controlled Bat Algorithm for Numerical Optimization Problem

Bat algorithm(BA)is an eminent meta-heuristic algorithm that has been widely used to solve diverse kinds of optimization problems.BA leverages the echolocation feature of bats produced by imitating the bats’searching behavior.BA faces premature convergence due to its local search capability.Instead of using the standard uniform walk,the Torus walk is viewed as a promising alternative to improve the local search capability.In this work,we proposed an improved variation of BA by applying torus walk to improve diversity and convergence.The proposed.Modern Computerized Bat Algorithm(MCBA)approach has been examined for fifteen well-known benchmark test problems.The finding of our technique shows promising performance as compared to the standard PSO and standard BA.The proposed MCBA,BPA,Standard PSO,and Standard BA have been examined for well-known benchmark test problems and training of the artificial neural network(ANN).We have performed experiments using eight benchmark datasets applied from the worldwide famous machine-learning(ML)repository of UCI.Simulation results have shown that the training of an ANN with MCBA-NN algorithm tops the list considering exactness,with more superiority compared to the traditional methodologies.The MCBA-NN algorithm may be used effectively for data classification and statistical problems in the future.

Unprecedented Smart Algorithm for Uninterrupted SDN Services During DDoS Attack

In the design and planning of next-generation Internet of Things(IoT),telecommunication,and satellite communication systems,controller placement is crucial in software-defined networking(SDN).The programmability of the SDN controller is sophisticated for the centralized control system of the entire network.Nevertheless,it creates a significant loophole for the manifestation of a distributed denial of service(DDoS)attack straightforwardly.Furthermore,recently a Distributed Reflected Denial of Service(DRDoS)attack,an unusual DDoS attack,has been detected.However,minimal deliberation has given to this forthcoming single point of SDN infrastructure failure problem.Moreover,recently the high frequencies of DDoS attacks have increased dramatically.In this paper,a smart algorithm for planning SDN smart backup controllers under DDoS attack scenarios has proposed.Our proposed smart algorithm can recommend single or multiple smart backup controllers in the event of DDoS occurrence.The obtained simulated results demonstrate that the validation of the proposed algorithm and the performance analysis achieved 99.99%accuracy in placing the smart backup controller under DDoS attacks within 0.125 to 46508.7 s in SDN.

Game theory-based attack and defense analysis in virtual wireless networks with jammers and eavesdroppers

Dynamic spectrum sharing and cognitive radio networks were proposed to enhance the Radio Frequency(RF)spectrum utilization.However,there are several challenges to realize them in real systems,such as sensing uncertainty causing issues to licensed users,business models for licensed service providers.Wireless virtualization is regarded as a technology that leverages service level agreements to sublease unused or underutilized RF spectrum that addresses aforementioned issues and helps to significantly enhance the utilization of the RF spectrum,offer improved coverage and capacity of networks,enhance network security and reduce energy consumption.With wireless virtualization,wireless networks'physical substrate is shared and reconfigured dynamically between virtual wireless networks through Mobile Virtual Network Operations(MVNOs).Wireless virtualization with dynamic configurable features of Wireless Infrastructure Providers(WIPs),virtualized wireless networks are vulnerable to a multitude of attacks,including jamming attacks and eavesdropping attacks.This paper investigates a means of defense through the employment of coalition game theory when jammers try to degrade the signal quality of legitimate users,and eavesdroppers aim to reduce secrecy rates.Specifically,we consider a virtual wireless network where MVNO users'job is to improve their Signal to Interference plus Noise Ratio(SINR)while the jammers target to collectively enhance their Jammer Received Signal Strength(JRSS),and an eavesdropper's goal is to reduce the overall secrecy rate.Numerical results have demonstrated that the proposed game strategies are effective(in terms of data rate,secrecy rate and latency)against such attackers compared to the traditional approaches.

Applying a bilingual model to mine e-commerce satisfaction sentiment

In recent years we have witnessed that e-commerce has become an internationalphenomenon in which sellers often need to do business with buyers who usedifferent languages. As customers, including international customers, often makepurchase decisions based on online reviews, comments and recommendationsmade by other customers, understanding the sentiment of online commentsbecomes necessary for sellers who want to provide customized recommendationsto users for a higher level of customer satisfaction. To this end, we have recentlydeveloped a bilingual model to handle both English and Chinese user commentsposted on e-commerce websites. This paper addresses several key issues includingChinese segmentation, data mining models and systematic design. An experimentin mining user satisfaction sentiment with English and Chinese online usercomments illustrates the value of our developed bilingual model.

On the Security of Information Dissemination in the Internet-of-Vehicles

Internet of Vehicles（IoV） is regarded as an emerging paradigm for connected vehicles to exchange their information with other vehicles using vehicle-to-vehicle（V2V） communications by forming a vehicular ad hoc networks（VANETs）, with roadside units using vehicle-to-roadside（V2R） communications. IoV offers several benefits such as road safety, traffic efficiency, and infotainment by forwarding up-to-date traffic information about upcoming traffic. For instance, IoV is regarded as a technology that could help reduce the number of deaths caused by road accidents, and reduce fuel costs and travel time on the road. Vehicles could rapidly learn about the road condition and promptly respond and notify drivers for making informed decisions. However, malicious users in IoV may mislead the whole communications and create chaos on the road. Data falsification attack is one of the main security issues in IoV where vehicles rely on information received from other peers/vehicles. In this paper,we present data falsification attack detection using hashes for enhancing network security and performance by adapting contention window size to forward accurate information to the neighboring vehicles in a timely manner（to improve throughput while reducing end-to-end delay）. We also present clustering approach to reduce travel time in case of traffic congestion. Performance of the proposed approach is evaluated using numerical results obtained from simulations. We found that the proposed adaptive approach prevents IoV from data falsification attacks and provides higher throughput with lower delay.