A Cognitive-based scheme for user reliability and expertise assessment in Q&A social networks

Q&A social media has gained a great deal of attention during recent years. People rely on these sites to obtain information due to the number of advantages they offer as compared to conventional sources of knowledge (e.g., asynchronous and convenient access). However, for the same question one may find highly contradictory answers, causing ambiguity with respect to the correct information. This can be attributed to the presence of unreliable and/or non-expert users. In this work, we propose a novel approach for estimating the reliability and expertise of a user based on human cognitive traits. Every user can individually estimate these values based on local pairwise interactions. We examine the convergence performance of our algorithm and we find that it can accurately assess the reliability and the expertise of a user and can successfully react to the latter's behavior change. © 2011 IEEE

Collaborative assessment of information provider's reliability and expertise using subjective logic

Q&A social media have gained a lot of attention during the recent years. People rely on these sites to obtain information due to a number of advantages they offer as compared to conventional sources of knowledge (e.g., asynchronous and convenient access). However, for the same question one may find highly contradicting answers, causing an ambiguity with respect to the correct information. This can be attributed to the presence of unreliable and/or non-expert users. These two attributes (reliability and expertise) significantly affect the quality of the answer/information provided. We present a novel approach for estimating these user's characteristics relying on human cognitive traits. In brief, we propose each user to monitor the activity of her peers (on the basis of responses to questions asked by her) and observe their compliance with predefined cognitive models. These observations lead to local assessments that can be further fused to obtain a reliability and expertise consensus for every other user in the social network (SN). For the aggregation part we use subjective logic. To the best of our knowledge this is the first study of this kind in the context of Q&A SN. Our proposed approach is highly distributed; each user can individually estimate the expertise and the reliability of her peers using her direct interactions with them and our framework. The online SN (OSN), which can be considered as a distributed database, performs continuous data aggregation for users expertise and reliability assessment in order to reach a consensus. We emulate a Q&A SN to examine various performance aspects of our algorithm (e.g., convergence time, responsiveness etc.). Our evaluations indicate that it can accurately assess the reliability and the expertise of a user with a small number of samples and can successfully react to the latter's behavior change, provided that the cognitive traits hold in practice. © 2011 ICST

When is electromagnetic spectrum fungible?

Fungibility is a common assumption for market-based spectrum management. In this paper, we explore the dimensions of practical fungibility of frequency bands from the point of view of the spectrum buyer who intends to use it. The exploration shows that fungibility is a complex, multidimensional concept that cannot casually be assumed. We develop two ideas for quantifying fungibility-(i) of a fungibility space in which the 'distance' between two slices of spectrum provides score of fungibility and (ii) a probabilistic score of fungibility. © 2012 IEEE

VA-index: Quantifying assortativity patterns in networks with multidimensional nodal attributes

Network connections have been shown to be correlated with structural or external attributes of the network vertices in a variety of cases. Given the prevalence of this phenomenon network scientists have developed metrics to quantify its extent. In particular, the assortativity coefficient is used to capture the level of correlation between a single-dimensional attribute (categorical or scalar) of the network nodes and the observed connections, i.e., the edges. Nevertheless, in many cases a multi-dimensional, i.e., vector feature of the nodes is of interest. Similar attributes can describe complex behavioral patterns (e.g., mobility) of the network entities. To date little attention has been given to this setting and there has not been a general and formal treatment of this problem. In this study we develop a metric, the vector assortativity index (VA-index for short), based on network randomization and (empirical) statistical hypothesis testing that is able to quantify the assortativity patterns of a network with respect to a vector attribute. Our extensive experimental results on synthetic network data show that the VA-index outperforms a baseline extension of the assortativity coefficient, which has been used in the literature to cope with similar cases. Furthermore, the VAindex can be calibrated (in terms of parameters) fairly easy, while its benefits increase with the (co-)variance of the vector elements, where the baseline systematically over(under)estimate the true mixing patterns of the network

Detection of selfish manipulation of carrier sensing in 802.11 networks

Recently, tuning the clear channel assessment (CCA) threshold in conjunction with power control has been considered for improving the performance of WLANs. However, we show that, CCA tuning can be exploited by selfish nodes to obtain an unfair share of the available bandwidth. Specifically, a selfish entity can manipulate the CCA threshold to ignore ongoing transmissions; this increases the probability of accessing the medium and provides the entity a higher, unfair share of the bandwidth. We experiment on our 802.11 testbed to characterize the effects of CCA tuning on both isolated links and in 802.11 WLAN configurations. We focus on AP-client(s) configurations, proposing a novel approach to detect this misbehavior. A misbehaving client is unlikely to recognize low power receptions as legitimate packets; by intelligently sending low power probe messages, an AP can efficiently detect a misbehaving node. Our key contributions are: 1) We are the first to quantify the impact of selfish CCA tuning via extensive experimentation on various 802.11 configurations. 2) We propose a lightweight scheme for detecting selfish nodes that inappropriately increase their CCAs. 3) We extensively evaluate our system on our testbed; its accuracy is 95 percent while the false positive rate is less than 5 percent. © 2012 IEEE

A case for adaptive sub-carrier level power allocation in OFDMA networks

In today's OFDMA networks, the transmission power is typically fixed and the same for all the sub-carriers that compose a channel. The sub-carriers though, experience different degrees of fading and thus, the received power is different for different sub-carriers; while some frequencies experience deep fades, others are relatively unaffected. In this paper, we make a case of redistributing the power across the sub-carriers (subject to a fixed power budget constraint) to better cope with this frequency selectivity. Specifically, we design a joint power and rate adaptation scheme (called JPRA for short) wherein power redistribution is combined with sub-carrier level rate adaptation to yield significant throughput benefits. We further consider two variants of JPRA: (a) JPRA-CR where, the power is redistributed across sub-carriers so as to support a maximum common rate (CR) across sub-carriers and (b) JPRA-MT where, the goal is to redistribute power such that the transmission time of a packet is minimized. While the first variant decreases transceiver complexity and is simpler, the second is geared towards achieving the maximum throughput possible. We implement both variants of JPRA on our WARP radio testbed. Our extensive experiments demonstrate that our scheme provides a 35% improvement in total network throughput in testbed experiments compared to FARA, a scheme where only sub-carrier level rate adaptation is used. We also perform simulations to demonstrate the efficacy of JPRA in larger scale networks. © 2012 ACM

Modeling and simulation of wireless link quality (ETT) through principal component analysis of trace data

Principal Component Analysis (PCA) is a powerful method in data analysis. In this paper, we employ the capabilities of PCA combined with statistical fits to trace data to develop tractable models that can be used to simulate the quality of links in wireless mesh networks using the expected transmission time (ETT) metric. We apply principal component analysis to ETT traces from a wireless mesh network to determine what features in the ETT traces are important and to extract any meaningful relationships therein. We demonstrate that PCA can be used to efficiently approximate large volumes of ETT values. In particular, the ETT trace for each link can be expressed as a combination of two basis vectors - one fairly stable and the other containing the variations in time. We also show how the extracted features can be employed to simulate ETT for a given network topology with and without known ETT trace data. Copyright 2011 ACM

Power-hop: A pervasive observation for real complex networks

Complex networks have been shown to exhibit universal properties, with one of the most consistent patterns being the scale-free degree distribution, but are there regularities obeyed by the r-hop neighborhood in real networks? We answer this question by identifying another power-law pattern that describes the relationship between the fractions of node pairs C(r) within r hops and the hop count r. This scale-free distribution is pervasive and describes a large variety of networks, ranging from social and urban to technological and biological networks. In particular, inspired by the definition of the fractal correlation dimension D2 on a point-set, we consider the hop-count rto be the underlying distance metric between two vertices of the network, and we examine the scaling of C(r) with r. We find that this relationship follows a power-law in real networks within the range 2<r<d, where d is the effective diameter of the network, that is, the 90-th percentile distance. We term this relationship as power-hop and the corresponding power-law exponent as power-hop exponent h. We provide theoretical justification for this pattern under successful existing network models, while we analyze a large set of real and synthetic network datasets and we show the pervasiveness of the power-hop

Decoupling trust and wireless channel induced effects on collaborative sensing attacks

One of the most crucial functionalities of cognitive radio networks is spectrum sensing. Completing this task in an accurate manner requires opportunistic spectrum access. Traditionally, sensing has been performed through energy detection by each individual secondary user. In order to increase accuracy, individual measurements are aggregated using different fusion functions. However, even though collaborative spectrum sensing can increase accuracy under benign settings, it is prone to falsification attacks, where malicious secondary users report fake sensings. Previous studies have designed trust (reputation) based systems to contain the effect of the adversaries, ignoring to a large extent the wireless channel irregularities when performing the computation. In this paper, we decouple the reasons behind an incorrect sensing report and propose the Decoupling Trust and Capability Spectrum Sensing System (DTCS3). DTCS3 is a collaborative spectrum sensing system that takes into account both a secondary sensor node's trust and its capability to sense the channel. Through thorough evaluations that consider a large variety of attack strategies, we show that by accounting for wireless induced effects while calculating the reporting trust of a secondary user, we can significantly improve the performance of a collaborative spectrum sensing system as compared to existing schemes in the literature. In particular, the true positive/negative rates can be improved by as much as 38%, while DTCS 3 is able to track and respond to dynamic changes in the adversaries' behavior. © 2014 IEEE

Postopek pridobitve vstopnega vizuma za državljane Bosne in Hercegovine

With the rise of online social networks and smartphones that record the user's location, a new type of online social network has gained popularity during the last few years, the so called Location-based Social Networks (LBSNs). In such networks, users voluntarily share their location with their friends via a check-in. In exchange they get recommendations tailored to their particular location as well as special deals that businesses offer when users check-in frequently. LBSNs started as specialized platforms such as Gowalla and Foursquare, however their immense popularity has led online social networking giants like Facebook to adopt this functionality. The spatial aspect of LBSNs directly ties the physical with the online world, creating a very rich ecosystem where users interact with their friends both online as well as declare their physical (co-)presence in various locations. Such a rich environment calls for novel analytic tools that can model the aforementioned types of interactions. In this work, we propose to model and analyze LBSNs using Tensors and Tensor Decompositions, powerful analytical tools that have enjoyed great growth and success in fields like Machine Learning, Data Mining, and Signal Processing alike. By doing so, we identify tightly knit, hidden communities of users and locations which they frequent. In addition to Tensor Decompositions, we use Signal Processing tools that have been previously used in Direction of Arrival (DOA) estimations, in order to study the temporal dynamics of hidden communities in LBSNs