1,722,726 research outputs found
LENS: LEveraging anti-social Network against Spam
No full textSpam is still an open problem from the network operator's perspective. The common state-of-the-art strategy to place filters against spam is at the recipient's edge. Although this strategy largely solves the spam problem from the user's perspective false positives/negatives may still existit cannot prevent spam from traversing the Internet. Consequently, spam continues to consume large amounts of Internet bandwidththere are around 200 billion spam/dayand provokes non-negligible financial loss to network operators. Therefore it becomes imperative to mitigate spam much earlier than at the recipient's edge. This goal has been recently accomplished only partially by placing filters at the edge of a social circle within a social network. In this paper we introduce LENS, a novel spam protection system based on the anti-social networking paradigm, which further mitigates spam beyond social circles. The key idea of this paradigm in LENS is to let users select trusted users, called Gatekeepers (GKs), from outside their social circle and within predefined social distances. Unless a GK vouches for the emails of potential senders from outside the social circle of a particular recipient, those e-mails are prevented from transmission. In this way LENS drastically reduces the consumption of Internet bandwidth by spam to control messages only. To evaluate the scalability of LENS we use publicly available online social networks datasets and demonstrate that reliable email delivery from millions of potential users is possible using GKs in the order of hundreds
Towards a 3D Evaluation Dataset for User Acceptance of Automated Shuttles
Full text linkThe popularity of automatic driving technology has gradually freed users from driving tasks and increased interaction with vehicles and machines. Understanding user acceptance and making them more receptive to new technologies can help businesses and researchers find better ways to design Human-Machine Interactions. The simulation experiment in an immersion environment can evaluate the user's acceptance of the design with low cost and high efficiency. Fur-ther, the evaluation methods of some existing studies are different, which creates obstacles to the reuse and reference of research results between different scholars. However, there are limited simulation data that can be used for such interactive evaluation, such as typical 3D environment data based on Virtual Reality devices. We design dataset, an ongoing 3D test dataset produced by Unity software, to be employed by different studies to evaluate interaction design for autonomous driving. The physical medium, composition, test participants, and procedure of the 3D environment data are described in this paper.</p
LENS: LEveraging anti-social Network against Spam
No full textSpam is still an open problem from the network operator's perspective. The common state-of-the-art strategy to place filters against spam is at the recipient's edge. Although this strategy largely solves the spam problem from the user's perspective false positives/negatives may still existit cannot prevent spam from traversing the Internet. Consequently, spam continues to consume large amounts of Internet bandwidththere are around 200 billion spam/dayand provokes non-negligible financial loss to network operators. Therefore it becomes imperative to mitigate spam much earlier than at the recipient's edge. This goal has been recently accomplished only partially by placing filters at the edge of a social circle within a social network. In this paper we introduce LENS, a novel spam protection system based on the anti-social networking paradigm, which further mitigates spam beyond social circles. The key idea of this paradigm in LENS is to let users select trusted users, called Gatekeepers (GKs), from outside their social circle and within predefined social distances. Unless a GK vouches for the emails of potential senders from outside the social circle of a particular recipient, those e-mails are prevented from transmission. In this way LENS drastically reduces the consumption of Internet bandwidth by spam to control messages only. To evaluate the scalability of LENS we use publicly available online social networks datasets and demonstrate that reliable email delivery from millions of potential users is possible using GKs in the order of hundreds
Delay-tolerant network protocol testing and evaluation
No full textDelay-tolerant networks, DTNs, are characterized by lacking end-to-end paths between communication sources and destinations. A variety of routing schemes have been proposed to provide communication services in DTNs, and credible and flexible protocol evaluation tools are in demand in order to test these DTN routing schemes. By examining the evolution of DTN protocol testing and evaluation, this article discusses the trend toward large-scale mobility trace supported emulation, and we propose TUNIE, a large-scale emulation testbed for DTN protocol evaluation based on network virtualization. Unlike the existing simulation tools and real-life testbeds, which either cannot provide a realistic DTN environment setup or are too costly and time-consuming, our proposed TUNIE architecture is capable of simulating reliable DTN environments and obtaining an accurate system performance evaluation. By system proptotype and implementation, we demonstrate TUNIE as a flexible platform for evaluating DTN protocol performance
Social-aware D2D communications: qualitative insights and quantitative analysis
No full textWith emerging demands for local area services, device-to-device communication is conceived as a vital component for the next-generation cellular networks to improve spectral reuse, bring hop gains, and enhance system capacity. Ripening these benefits depends on efficiently solving several main technical problems, including mode selection, resource allocation, and interference management. Aiming to establish a new paradigm for solving these challenging problems in D2D communication, in this article we propose a social-aware enhanced D2D communication architecture that exploits social networking characteristics for system design. By developing a profound understanding of the interplay between social networks’ properties and mobile communication problems, we qualitatively analyze how D2D communications can benefit from social features, and quantitatively assess the achievable gains in a social-aware D2D communication system
Contact-aware data replication in roadside unit aided vehicular opportunistic networks
No full textRoadside units (RSUs), which enable vehicles to-infrastructure communications, are deployed along roadsides to handle the ever-growing communication demands caused by explosive increase of vehicular traffics. How to efficiently utilize them to enhance the vehicular delay tolerant network (VDTN) performance are the important problems in designing RSU-aided VDTNs. In this work, we implement an extensive experiment involving tens of thousands of operational vehicles in Beijing city. Based on this newly collected Beijing trace and the existing Shanghai trace, we obtain some invariant properties for communication contacts of large scale RSU-aided VDTNs. Specifically, we find that the contact time between RSUs and vehicles obeys an exponential distribution, while the contact rate between them follows a Poisson distribution. According to these observations, we investigate the problem of communication contact-aware mobile data replication for RSU-aided VDTNs by considering the mobile data dissemination system that transmits data from the Internet to vehicles via RSUs through opportunistic communications. In particular, we formulate the communication contact-aware RSU-aided vehicular mobile data dissemination problem as an optimization problem with realistic VDTN settings, and we provide an efficient heuristic solution for this NP-hard problem. By carrying out extensive simulation using realistic vehicular traces, we demonstrate the effectiveness of our proposed heuristic contact-aware data replication scheme, in comparison with the optimal solution and other existing schemes
Fighting Spam Using Social GateKeepers
No full textWe introduce LENS (LEveraging social Networking and trust to prevent Spam transmission), a novel spam protection system which leverages the recipient’s social network to allow correspondence within the social network to directly pass to the mailbox of the recipient. To enable new senders to send emails, legitimate and authentic users, called GateKeepers (GKs), are selected from outside the recipient’s social circle and within predefined social distances. Our evaluations show that LENS provides each recipient reliable email delivery from a large fraction (up to 55% of entire userbase) of the social network; it is also effective and lightweight in accepting all the legitimate inbound emails in the real email traces. LENS imposes zero overhead for the common case of frequent and familiar senders, and remains lightweight for the general case. Our prototype implementation of LENS in Postfix/MailAvenger shows that LENS consumes up to 75% less CPU and 9% less memory as traditional solutions like SpamAssassin
Limits of predictability for large-scale urban vehicular mobility
No full textKey challenges in vehicular transportation and communication systems are understanding vehicular mobility and utilizing mobility prediction, which are vital for both solving the congestion problem and helping to build efficient vehicular communication networking. Most of the existing works mainly focus on designing algorithms for mobility prediction and exploring utilization of these algorithms. However, the crucial questions of how much the mobility is predictable and how the mobility predictability can be used to enhance the system performance are still the open and unsolved problems. In this paper, we consider the fundamental problem of the predictability limits of vehicular mobility. By using two large-scale urban city vehicular traces, we propose an intuitive but effective model of areas transition to describe the vehicular mobility among the areas divided by the city intersections. Based on this model, we examine the predictability limits of large-scale urban vehicular networks and obtain the maximal predictability based on the methodology of entropy theory. Our study finds that about 78%–99% of the location and above 70% of the staying time, respectively, are predicable. Our findings thus reveal that there is strong regularity in the daily vehicular mobility, which can be exploited in practical prediction algorithm design
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