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Cloud based Content Delivery Networks have emerged over the last years as a viable cost-effective alternative to traditional CDNs. In this work we demonstrate a novel surrogate server placement strategy for fostering content delivery within the evolving wireless Cloud environment by combining Social Network Analysis inspired metrics and virtual network embedding strategies. The proposed paradigm,...
Mobile social networks (MSNs) are a kind of delay tolerant network that consists of lots of mobile nodes with social characteristics. Recently, many social-aware algorithms have been proposed to address routing problems in MSNs. However, it also brings more security and privacy concerns. In this paper, we discuss a specific type of Sybil attack in MSNs, which few researches has been focused on. We...
Daily, vehicles in transit in a city and during their trajectories encounter other vehicles. The regularity of these encounters is influenced by several elements, such as: vehicle's speed, destinations, traffic conditions, and the period of the day. It is possible to justify these elements by road conditions and the driver's behavior. People have routines and similar behaviors, which strongly impact...
We consider a mobile content delivery network (mCDN) in which a special form of mobile devices designated as caching servers (caching-server device: CSD) can provide the near-by devices with some popular contents on demand via device-to-device (D2D) communication links. On the assumption that mobile CSDs are randomly distributed by a Poisson point process (PPP), an optimization problem is formulated...
Location privacy is an important issue in location-based services. A large number of location cloaking algorithms have been proposed for protecting location privacy of users. However, these algorithms cannot be used in vehicular networks due to constrained vehicular mobility. In this paper, we propose a new method named Protecting Location Privacy with Clustering Anonymization (PLPCA) for location-based...
Detecting the collaborative cheating in an online shopping system is an important but challenging issue. In this paper, we propose a novel approach to detect the collusive manipulation on ratings in Amazon, an online shopping system. Rather than focusing on rating values, we believe the online shopping and rating activities have nontrivial attributes in terms of social network connections. Our major...
Future dynamic wireless and online social networks will require more agile and efficient infrastructures to support the anticipated user traffic and service requirements. In response, in this work, we propose a novel spectrum-agile resource allocation approach that combines Markov Random Field (MRF) cross-layer decisions for the allocation of resources at the lower protocol layers with Back-pressure...
We study the important problem of source localization in the context of information spreads in large social networks. Specifically, we design a Maximum-Likelihood source localization algorithm that is especially suited to large social networks. Our proposed algorithm requires about 3% fewer sensor nodes than other single stage algorithms for the same level of accuracy in detection. For practical social...
The digitization of social networks has enabled the passive collection of large scale data, which in turn have fostered social studies that have been traditionally dependent on small scale, interview-based data. During the last years, a new class of digital social networks has emerged, namely, location-based social networks (LBSNs). The main interaction between users of an LBSN is location sharing,...
Efficient marketing or awareness-raising campaigns seek to recruit a small number, w, of influential individuals — where w is the campaign budget — that are able to cover the largest possible target audience through their social connections. In this paper we assume that the topology is gradually discovered thanks to recruited individuals disclosing their social connections. We analyze the performance...
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