22,904 research outputs found
Belief Consensus Algorithms for Fast Distributed Target Tracking in Wireless Sensor Networks
In distributed target tracking for wireless sensor networks, agreement on the
target state can be achieved by the construction and maintenance of a
communication path, in order to exchange information regarding local likelihood
functions. Such an approach lacks robustness to failures and is not easily
applicable to ad-hoc networks. To address this, several methods have been
proposed that allow agreement on the global likelihood through fully
distributed belief consensus (BC) algorithms, operating on local likelihoods in
distributed particle filtering (DPF). However, a unified comparison of the
convergence speed and communication cost has not been performed. In this paper,
we provide such a comparison and propose a novel BC algorithm based on belief
propagation (BP). According to our study, DPF based on metropolis belief
consensus (MBC) is the fastest in loopy graphs, while DPF based on BP consensus
is the fastest in tree graphs. Moreover, we found that BC-based DPF methods
have lower communication overhead than data flooding when the network is
sufficiently sparse
Byzantine Attack and Defense in Cognitive Radio Networks: A Survey
The Byzantine attack in cooperative spectrum sensing (CSS), also known as the
spectrum sensing data falsification (SSDF) attack in the literature, is one of
the key adversaries to the success of cognitive radio networks (CRNs). In the
past couple of years, the research on the Byzantine attack and defense
strategies has gained worldwide increasing attention. In this paper, we provide
a comprehensive survey and tutorial on the recent advances in the Byzantine
attack and defense for CSS in CRNs. Specifically, we first briefly present the
preliminaries of CSS for general readers, including signal detection
techniques, hypothesis testing, and data fusion. Second, we analyze the spear
and shield relation between Byzantine attack and defense from three aspects:
the vulnerability of CSS to attack, the obstacles in CSS to defense, and the
games between attack and defense. Then, we propose a taxonomy of the existing
Byzantine attack behaviors and elaborate on the corresponding attack
parameters, which determine where, who, how, and when to launch attacks. Next,
from the perspectives of homogeneous or heterogeneous scenarios, we classify
the existing defense algorithms, and provide an in-depth tutorial on the
state-of-the-art Byzantine defense schemes, commonly known as robust or secure
CSS in the literature. Furthermore, we highlight the unsolved research
challenges and depict the future research directions.Comment: Accepted by IEEE Communications Surveys and Tutoiral
Target Tracking in Confined Environments with Uncertain Sensor Positions
To ensure safety in confined environments such as mines or subway tunnels, a
(wireless) sensor network can be deployed to monitor various environmental
conditions. One of its most important applications is to track personnel,
mobile equipment and vehicles. However, the state-of-the-art algorithms assume
that the positions of the sensors are perfectly known, which is not necessarily
true due to imprecise placement and/or dropping of sensors. Therefore, we
propose an automatic approach for simultaneous refinement of sensors' positions
and target tracking. We divide the considered area in a finite number of cells,
define dynamic and measurement models, and apply a discrete variant of belief
propagation which can efficiently solve this high-dimensional problem, and
handle all non-Gaussian uncertainties expected in this kind of environments.
Finally, we use ray-tracing simulation to generate an artificial mine-like
environment and generate synthetic measurement data. According to our extensive
simulation study, the proposed approach performs significantly better than
standard Bayesian target tracking and localization algorithms, and provides
robustness against outliers.Comment: IEEE Transactions on Vehicular Technology, 201
Evidential Label Propagation Algorithm for Graphs
Community detection has attracted considerable attention crossing many areas
as it can be used for discovering the structure and features of complex
networks. With the increasing size of social networks in real world, community
detection approaches should be fast and accurate. The Label Propagation
Algorithm (LPA) is known to be one of the near-linear solutions and benefits of
easy implementation, thus it forms a good basis for efficient community
detection methods. In this paper, we extend the update rule and propagation
criterion of LPA in the framework of belief functions. A new community
detection approach, called Evidential Label Propagation (ELP), is proposed as
an enhanced version of conventional LPA. The node influence is first defined to
guide the propagation process. The plausibility is used to determine the domain
label of each node. The update order of nodes is discussed to improve the
robustness of the method. ELP algorithm will converge after the domain labels
of all the nodes become unchanged. The mass assignments are calculated finally
as memberships of nodes. The overlapping nodes and outliers can be detected
simultaneously through the proposed method. The experimental results
demonstrate the effectiveness of ELP.Comment: 19th International Conference on Information Fusion, Jul 2016,
Heidelber, Franc
A reliability-based approach for influence maximization using the evidence theory
The influence maximization is the problem of finding a set of social network
users, called influencers, that can trigger a large cascade of propagation.
Influencers are very beneficial to make a marketing campaign goes viral through
social networks for example. In this paper, we propose an influence measure
that combines many influence indicators. Besides, we consider the reliability
of each influence indicator and we present a distance-based process that allows
to estimate the reliability of each indicator. The proposed measure is defined
under the framework of the theory of belief functions. Furthermore, the
reliability-based influence measure is used with an influence maximization
model to select a set of users that are able to maximize the influence in the
network. Finally, we present a set of experiments on a dataset collected from
Twitter. These experiments show the performance of the proposed solution in
detecting social influencers with good quality.Comment: 14 pages, 8 figures, DaWak 2017 conferenc
Classification of Message Spreading in a Heterogeneous Social Network
Nowadays, social networks such as Twitter, Facebook and LinkedIn become
increasingly popular. In fact, they introduced new habits, new ways of
communication and they collect every day several information that have
different sources. Most existing research works fo-cus on the analysis of
homogeneous social networks, i.e. we have a single type of node and link in the
network. However, in the real world, social networks offer several types of
nodes and links. Hence, with a view to preserve as much information as
possible, it is important to consider so-cial networks as heterogeneous and
uncertain. The goal of our paper is to classify the social message based on its
spreading in the network and the theory of belief functions. The proposed
classifier interprets the spread of messages on the network, crossed paths and
types of links. We tested our classifier on a real word network that we
collected from Twitter, and our experiments show the performance of our belief
classifier
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