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| 1 | An Overview of Internet of Vehicles显示文摘The new era of the Internet of Things is driving the evolution of conventional Vehicle Ad-hoc Networks into the Internet of Vehicles(IoV).With the rapid development of computation and communication technologies,IoV promises huge commercial interest and research value,thereby attracting a large number of companies and researchers.This paper proposes an abstract network model of the IoV,discusses the technologies required to create the IoV,presents different applications based on certain currently existing technologies,provides several open research challenges and describes essential future research in the area of IoV. | YANGFangchun WANG Shangguang LI Jinglin LIU Zhihan SUN Qibo | 2014 | China Communications2014，11，10： | 46 |
| 2 | Enhancing Reliability via Checkpointing in Cloud Computing Systems显示文摘Cloud computing is becoming an important solution for providing scalable computing resources via Internet. Because there are tens of thousands of nodes in data center, the probability of server failures is nontrivial. Therefore, it is a critical challenge to guarantee the service reliability. Fault-tolerance strategies, such as checkpoint, are commonly employed. Because of the failure of the edge switches, the checkpoint image may become inaccessible. Therefore, current checkpoint-based fault tolerance method cannot achieve the best effect. In this paper, we propose an optimal checkpoint method with edge switch failure-aware. The edge switch failure-aware checkpoint method includes two algorithms. The first algorithm employs the data center topology and communication characteristic for checkpoint image storage server selection. The second algorithm employs the checkpoint image storage characteristic as well as the data center topology to select the recovery server. Simulation experiments are performed to demonstrate the effectiveness of the proposed method. | Ao Zhou Qibo Sun Jinglin Li | 2017 | China Communications2017，14，7： | 4 |
| 3 | Particle Swarm Optimization with Skyline Operator for Fast Cloud-based Web Service Composition显示文摘 | Shangguang Wang Qibo Sun Hua Zou Fangchun Yang | 2013 | Mobile Networks and Applications2013，，1： | 4 |
| 4 | Towards an accurate evaluation of quality of cloud service in service-oriented cloud computing显示文摘 | Shangguang Wang Zhipiao Liu Qibo Sun Hua Zou Fangchun Yang | 2014 | Journal of Intelligent Manufacturing2014，，2： | 3 |
| 5 | Bayesian approach with maximum entropy principle for trusted quality of Web service metric in e-commerce applications显示文摘 | Wang Shangguang Zou Hua Sun Qibo | | 0，，10： | 1 |
| 6 | The performance prediction of cloud pervice via JOGM(1,1) Model显示文摘 | Liu Zhipiao Sun Qibo Wang Shangguang | | 0，，05： | 1 |
| 7 | Cost-aware cloud service request scheduling for SaaS providers显示文摘 | Zhipiao Liu Shangguang Wang Qibo Sun | 2014 | The Computer Journal2014，57，2： | 1 |
| 8 | Cost-Aware cloud service request scheduling for SaaS providers显示文摘 | Zhipian LIU Shangguang WANG Qibo SUN Hua ZOU Fangchun YANG | 2013 | The Computer Journal2013，1，10： | 1 |
| 9 | Community detection via improved genetic algorithm in complex network 显示文摘 | WANG Shangguan ZOU Hua SUN Qibo | 2012 | Information Technology Journal2012，11，3： | 1 |
| 10 | An Early Stage Detecting Method against SYN Flooding Attacks显示文摘Existing detection methods against SYN flooding attacks are effective only at the later stages when attacking signatures are obvious.In this paper an early stage detecting method(ESDM) is proposed.The ESDM is a simple but effective method to detect SYN flooding attacks at the early stage.In the ESDM the SYN traffic is forecasted by autoregressive integrated moving average model, and non-parametric cumulative sum algorithm is used to find the SYN flooding attacks according to the forecasted traffic.Trace-driven simulations show that ESDM is accurate and efficient to detect the SYN flooding attacks. | Sun Qibo Wang Shangguang Yan Danfeng Yang Fangchun | 2009 | China Communications2009，6，4： | 1 |
| 11 | QoS Evaluation for Web Service Recommendation显示文摘Web service recommendation is one of the most important fi elds of research in the area of service computing. The two core problems of Web service recommendation are the prediction of unknown Qo S property values and the evaluation of overall Qo S according to user preferences. Aiming to address these two problems and their current challenges, we propose two efficient approaches to solve these problems. First, unknown Qo S property values were predicted by modeling the high-dimensional Qo S data as tensors, by utilizing an important tensor operation, i.e., tensor composition, to predict these Qo S values. Our method, which considers all Qo S dimensions integrally and uniformly, allows us to predict multi-dimensional Qo S values accurately and easily. Second, the overall Qo S was evaluated by proposing an efficient user preference learning method, which learns user preferences based on users' ratings history data, allowing us to obtain user preferences quantifiably and accurately. By solving these two core problems, it became possible to compute a realistic value for the overall Qo S. The experimental results showed our proposed methods to be more efficient than existing methods. | MA You XIN Xin WANG Shangguang LI Jinglin SUN Qibo YANG Fangchun | 2015 | China Communications2015，12，4： | 1 |
| 12 | β-decay study of neutron-rich nucleus ^(34)Al显示文摘The'island of inversion'has been known for over a quarter century,since Warburton et al.[1]proposed that nuclei with intruder ground states would constitute a 3×3 square with Z=10-12,N=20-22 in 1990.Uncovering the underlying inversion mechanism and exploring the scope of the island have attracted significant theoretical and experimental efforts in the following years.Now it is well known that the reduction of N=20 shell gap,which is likely caused by the | Rui Han XiangQing Li WeiGuang Jiang ZhiHuan Li Hui Hua ShuangQuan Zhang CenXi Yuan DongXing Jiang YanLin Ye Jing Li ZongHao Li FuRong Xu QiBo Chen Jie Meng JianSong Wang Chuan Xu YeLei Sun ChunGuang Wang HongYi Wu ChenYang Niu ChenGuang Li Chao He Wei Jiang PengJie Li HongLiang Zang Jun Feng SiDong Chen Qiang Liu XiaoChi Chen HuShan Xu ZhengGuo Hu YanYun Yang Peng Ma JunBing Ma ShiLun Jin Zhen Bai MeiRong Huang Yuan Jie Zhou WeiHu Ma Yong Li XiaoHong Zhou YuHu Zhang GuoQing Xiao WenLong Zhan | 2017 | Science China(Physics,Mechanics & Astronomy)2017，60，4： | 0 |
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