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.
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.
The Internet of Things (IoT) has been widely used in various application domains to provide advanced and intelligent services for human beings, such as environmental monitoring, intrusion detection. In IoT networks, senor nodes are normally low capability devices that are vulnerable for numerous security attacks. In order to address this issue, many security schemes and solutions have been proposed in recent years. The security and privacy requirements including privacy and trust management among users and things have played a fundamental role to detect malicious nodes in IoT, thus to better promote the applications of IoT. In this paper, we focus on the security problems in IoT networks, and provide a survey on trust evaluation towards trustworthy IoT with specified criteria. Moreover, we present research challenges of trust evaluation in IoT and provide future research direction in this area.
Software-Defined Networking (SDN) has gained special attention in both academia and industry. It is a new network architecture framework for networking, which decouples the network control plane from the data plane at physical topology. SDN promotes centralization of network control and introduces the ability to program the network. However, the development of the SDN is constrained by various security concerns, e.g., the central management of SDN is ideal for security attack. In this paper, we investigate potential security threats and specify security requirements accordingly. Existing security solutions in SDN are seriously reviewed and evaluated based on the specified security requirements in order to figure out open issues and motivate future research efforts.
Recommender systems have been widely used in e-commerce platforms, such as Amazon and Taobao. Among the available recommendation algorithms, Item Collaborative Filtering (ItemCF) Algorithm and Content Filtering Algorithm have gained wide adoption because of various strengths. For example, hidden interests can be digged so as to get fresh recommendations, and highly individual recommendations can be made. Despite their strengths and wide adoption, there are still some weaknesses associated with them. One representative weakness is the existence of duplicated, and outdated recommendations due to the lack of purchasing cycles, e.g., weekly or seasonal, of goods. We name such cycles Commodity Purchase Cycle (CPC), and propose a new recommendation algorithm based on CPC in this paper. We leverage CPC attributes to modify the collaborative filtering output rating matrix acquired by the ItemCF Algorithm, and take into consideration both user behaviors and commodity characteristics to make timely recommendations. We utilize a realistic dataset from Taobao to verify the performance of the proposed algorithm. Experimental results demonstrate good performance of CPC algorithm. Specifically, from the perspective of Root Mean Square Error (RMSE), the CPC Algorithm promotes the recommendation accuracy by 15%-20%, compared with the state-of-the-art ItemCF Algorithm.
Microblogs open opportunities for social spammer accounts. These widespread spammers, are threatening for microblog services and normal users. Therefore, detecting spammers should be conducted to fight and stop them. In this paper, we propose an approach to diagnose user accounts in China's most popular microblog site Sina Weibo. Unlike existing approaches, which can hardly discover sophisticated spammers and only give a simple conclusion as spammer or not lacking of detail information, but our work provides a more responsible way that reveals the clues to verify a spammer account by using classifier-level fusion and feature-level comparison. Distinct discriminative features are used to train basic classifiers. Then a fusion model is learned to combine the outputs of the basic classifiers and make the final prediction. Comparing basic classifiers outputs with the final prediction offers the insights of spammer identification. Experiments show that our approach significantly improves the classification performance and this approach can point out spammers' specific spam action in a detail way that helps us strike spammers accurately.
Multimedia service providers are widely using HTTP adaptive streaming technology over Content Delivery Networks (CDNs) in Internet. Content distribution in network congestion situations and request redirection are the common challenges facing CDNs performance in delivering multimedia content to end-users. In this paper a CDN testbed is designed that consist of two mechanisms in order to fast deliver the segmentation of adaptive video streaming and redirect clients request to appropriate surrogate servers that hold copy of replica video content. Evaluation results prove the efficiency of the designed testbed according to vary bandwidth, delay and packet loss. The candidate application layer protocol to push proactively media segments to surrogate servers is faster than others and the clients redirection to appropriate surrogate servers based on the performance of server load and network congestion provides better experience to end-users.
Grid computing has been aggressively expanded with the increasing demands for high performance computing, with the steady price decrease of the hardware, and the growth of related software support. Resource and security assurance are the core of the Grid computing systems. If the supporting resource management scheme and the security assurance cannot keep pace with the Grid's evolvement, a bottleneck of Grid computing will be easily formed. Schemes based on trust and reputation evaluation are treated as an efficient way to address these problems. In this paper, we provide a systematic review on existing work of trust and reputation evaluation mechanisms in grid computing, and compare them according to the criteria proposed by us. Through review and comparison, we found a number of open problems in this research field, which directs the future research and investigation.
In this paper, we investigate a joint performance optimization on network lifetime and video distortion for wireless multimedia sensor network (WMSN). Considering the tradeoff between minimum video distortion and maximum network lifetime, a multi-objective cross-layer optimization framework is proposed, which not only optimizes network lifetime but also achieves optimal video quality, where the source encoding rate and link rate are jointly optimized. In addition, a secret scheme that couples secret sharing and multipath routing is developed to provide reliable security. Finally, a video distortion model, including source rate and link rate is specially studied. Decentralized algorithms are realized using a subgradient method to solve the multi-objective optimization problem. Experimental results demonstrate the optimal tradeoff performance. We also illustrated that the proposed scheme can achieve greater network lifetime and much less video distortion compared to existing distributed algorithms.
In the domain of emergency event analysis, it is still a difficult issue to acquire the event information from the Web efficiently. To solve the problem, this paper proposes a crowdsensing-based Web crawler for emergency event analysis. When an emergency event occurs, web users post event information on the Web with geographical position, which can be regarded as crowd sensors. In the proposed method, the crawler takes advantage of the information from these crowd sensors, such as semantic information, geographical information, sentiment information, etc. to get the information of event efficiently. Experimental results show that the proposed method can improve the efficiency of crawler when compared with common crawlers.
Despite extensive measurement based study of IEEE 802.11 wireless channels, rare work has performed to investigate the use of IEEE 802.11 protocol variants in practice and its traffics in various environments. This paper presents a traffic analysis on two different IEEE 802.11 networks under operation: one is inside a university campus and the other is out of the campus. The investigation focuses are on protocol efficiency, frame delivery, application types and IEEE 802.11 variants (i.e. a/b/g/n/ac/ad). With more than 10 million frames collected, the results show: (1) the IEEE 802.11 protocol could be more efficiency with a smaller retry limit, (2) various environments vary largely in traffic application types in that the users have various needs, and (3) users upgrades their devices quickly because the traffic dominates with latest IEEE 802.11 protocol variant. These observations will be valuable to future protocol design of IEEE 802.11 networking.
The nodes in intermittently connected wireless network have social attributes, and their movement has special rules. According to the analysis on the transition regularity of each node in its movement epochs, the nodes in the network are classified into two categories, central nodes and common nodes respectively. With the constrained label propagating method, the network topology is decomposed into several communities. Further, the network structure aware routing mechanism is proposed in our paper. To improve the network performance of packet delivery ratio with minimized overhead induced, the central nodes and common nodes of the destination community are selected as the relay nodes, and the activity of central node can be taken full advantage by the source node. Results and numerical analysis show that the proposed routing mechanism improves the network performance dramatically; especially almost 90% improvement can be achieved in terms of delivery ratio.
Social networking has become very popular in recent years by serving as a medium for disseminating information and connecting like-minded people. It influences today's social culture and changes the way of modern life. The success of social networking relies on the level of trust that social group members have with each other as well as with social networking service providers. Therefore, trust evaluation in the social networking becomes an important topic that has attracted special concerns. Many trust models or schemes have been proposed to improve the security or performance in social networking. However, existing work mostly only focused on certain aspects. There still lacks a comprehensive study on trust evaluation and management in social networking. In this paper, we propose comprehensive criteria with nine aspects for trust evaluation. Related work in this area published in recent five years have been seriously surveyed and evaluated based on the criteria. We compare existing work and analyze the advantages and disadvantages of the current methods in order to figure out open research issues and motivate future research efforts.
With the rapid development of social network, users are used to discuss and plan activities with their online friends by the form of interactive communication streams in mobile social networks. The information of these activities can be applied to track the prospective behavior and following demand of users for smart service supporting systems. In this paper, we describe a prospective activity as an event that happens at scheduled location and time, and propose a method to detect the event. The data of interactive communication streams are divided into five types, including request, question, confirmation, denying and uncertainty. We employ a combined multi-classifier based on D-S evidence theory to classify interactive streams into the five types, and extract the information of event, location and time in each text. Then a reasoning model is proposed to deduce the user's final intention of prospective activity through the series of different types of interactive streams. Based on the real data collected from social network, the experimental results show that our method could detect the information of events effectively.
Homomorphic Encryption (HE) enables meaningful computations on encrypted data without decrypting it. Thus, privacy concerns can be addressed in a satisfactory manner by encrypting data using the homomorphic cryptosystem before uploading the data to a cloud service provider. Recent years have witnessed rapid progress of (HE) development in theory and practice. However, current literature still lacks a thorough review on these new developments and their applications. This paper guides the reader through a journey of these developments by reviewing the state of the art of homomorphic cryptosystems and discussing their advantages, disadvantages as well as applicability. In addition, this work presents the privacy preserving applications of homomorphic cryptosystems in the field of private information retrieval, genomic data, cloud computing and participatory sensing. Moreover, this review further discusses open issues of such applications and future research trends.
