Routing protocols are the focus in mobile opportunistic networks, and the simulation test is one of the effective ways to evaluate their performance. This paper presents a mobile opportunistic network simulator MONICA. It provides a programmable interface to users, which is easy to learn and easy to use. It also integrates eight classic routing algorithms, six caching strategies and two types of mobility models, so that users can make a more comprehensive evaluation of the routing protocol. In addition, MONICA can trace abnormal behaviors with the network event function and observe the real simulation process by the visual module. All of these guarantee the efficiency of the simulator in evaluating the routing performance.
With the development of Web 2.0, ubiquitous computing, and corresponding technologies, social media has the ability to provide the concepts of information contribution, diffusion, and exchange. Different from the permitting the general public to issue the user-generated information, social media has enabled them to avoid the need to use centralized, authoritative agencies. One of the important functions of Weibo is to monitor real time urban emergency events, such as fire, explosion, traffic jam, etc. Weibo user can be seen as social sensors and Weibo can be seen as the sensor platform. In this paper, the proposed method focuses on the step for storytelling of urban emergency events: given the Weibo posts related to a detected urban emergency event, the proposed method targets at mining the multi-modal information (e.g., images, videos, and texts), as well as storytelling the event precisely and concisely. To sum up, we propose a novel urban emergency event storytelling method to generate multi-modal summary from Weibo. Specifically, the proposed method consists of three stages: irrelevant Weibo post filtering, mining multimodal information and storytelling generation. We conduct extensive case studies on real-world microblog datasets to demonstrate the superiority of the proposed framework.
Micro-blog has become an important crowdsensing place for a lot of real-time information dissemination and discussion. This paper explores building user micro-blog semantic view based on topic feature extraction to provide theoretical support for future application such as user clustering, micro-blog topic recommendation. First, user network is built to save the user relationship on micro-blog. In the process of building user network, user authority and micro-blog topic heat are respectively considered to pick up influential users and important topic. Second, the algorithm of generating user micro-blog semantic view is proposed to represent the content of a user historical micro-blog. User topic feature vector and user topic feature matrix are used to help compute topic similarity between differential users and build topic feature graph.
E-commerce is becoming a popular and growing industry in which buyers and sellers are trading with each other on the Internet. A large number of E-commerce companies have created very profitable businesses since E-commerce Web sites (such as JD.com, eBay.com) or pioneering E-commerce traders (such as Taobao.com, Amazon.com) showed up more than 10 years ago. One of the most important factors that impact the success of Ecommerce is ensuring security and trust. Undoubtedly, in lack of trust as a key element of online business communications, Ecommerce will face a lot of challenges. Trust evaluation plays an important role in E-commerce for evaluating trust relationship between enterprises and customers, which assists on providing qualified services and enhancing user privacy and security. However, current literature still lacks a comprehensive study on trust evaluation in E-commerce. In this paper, we propose review criteria of trust evaluation in E-commerce and survey the current literature by analyzing its advantages and shortcomings according to the proposed criteria. At last, we discuss unsolved issues and current challenges and propose future research trends in the area of trust evaluation in E-commerce.
In IaaS (such as Amazon EC2 and Microsoft Azure), several VM (virtual-machine) instances usually run in one physical machine so as to improve resource utilization. However this also caused more attack opportunities. A typical example is a cross-VM timing channel. Recent studies show that this kind of covert channel can successfully steal private information (e.g. private key) from the co-resident VM instances. It brought great challenges to the security of the cloud and has absorbed more and more attention in recent years. But to our knowledge, there is still little work on detecting and investigating such covert channel. Therefore, we propose a behavior-based method to automatically detect and investigate the timing channel. First, in order to record the behavior of this covert channel, a page-level memory monitoring method is designed. Second, an automatic identification algorithm is proposed based on some memory activity signatures. Finally, in order to confirm the result, the memory dump will be obtained and the binary code of the suspicious process will be analyzed. We have implemented a prototype on Xen, and the experimental results show that all of these kinds of attacks can be detected even under the disturbance from normal processes.
