25,176 research outputs found
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
Distributed data association for multi-target tracking in sensor networks
Associating sensor measurements with target tracks is a fundamental and challenging problem in multi-target tracking. The problem is even more
challenging in the context of sensor networks, since association is coupled
across the network, yet centralized data processing is in general
infeasible due to power and bandwidth limitations. Hence efficient, distributed solutions are needed. We propose techniques based on graphical models to efficiently solve such data association problems in sensor networks. Our approach scales well with the number of sensor nodes in the network, and it is well--suited for distributed implementation. Distributed inference is realized by a message--passing algorithm which requires iterative, parallel exchange of information among neighboring nodes on the graph. So as to address trade--offs between inference performance and communication costs, we also propose a communication--sensitive form of message--passing that is capable of achieving near--optimal performance using far less communication. We demonstrate the effectiveness of our approach with experiments on simulated data
Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks
Future wireless networks have a substantial potential in terms of supporting
a broad range of complex compelling applications both in military and civilian
fields, where the users are able to enjoy high-rate, low-latency, low-cost and
reliable information services. Achieving this ambitious goal requires new radio
techniques for adaptive learning and intelligent decision making because of the
complex heterogeneous nature of the network structures and wireless services.
Machine learning (ML) algorithms have great success in supporting big data
analytics, efficient parameter estimation and interactive decision making.
Hence, in this article, we review the thirty-year history of ML by elaborating
on supervised learning, unsupervised learning, reinforcement learning and deep
learning. Furthermore, we investigate their employment in the compelling
applications of wireless networks, including heterogeneous networks (HetNets),
cognitive radios (CR), Internet of things (IoT), machine to machine networks
(M2M), and so on. This article aims for assisting the readers in clarifying the
motivation and methodology of the various ML algorithms, so as to invoke them
for hitherto unexplored services as well as scenarios of future wireless
networks.Comment: 46 pages, 22 fig
Near-Surface Interface Detection for Coal Mining Applications Using Bispectral Features and GPR
The use of ground penetrating radar (GPR) for detecting the presence of near-surface interfaces is a scenario of special interest to the underground coal mining industry. The problem is difficult to solve in practice because the radar echo from the near-surface interface is often dominated by unwanted components such as antenna crosstalk and ringing, ground-bounce effects, clutter, and severe attenuation. These nuisance components are also highly sensitive to subtle variations in ground conditions, rendering the application of standard signal pre-processing techniques such as background subtraction largely ineffective in the unsupervised case. As a solution to this detection problem, we develop a novel pattern recognition-based algorithm which utilizes a neural network to classify features derived from the bispectrum of 1D early time radar data. The binary classifier is used to decide between two key cases, namely whether an interface is within, for example, 5 cm of the surface or not. This go/no-go detection capability is highly valuable for underground coal mining operations, such as longwall mining, where the need to leave a remnant coal section is essential for geological stability. The classifier was trained and tested using real GPR data with ground truth measurements. The real data was acquired from a testbed with coal-clay, coal-shale and shale-clay interfaces, which represents a test mine site. We show that, unlike traditional second order correlation based methods such as matched filtering which can fail even in known conditions, the new method reliably allows the detection of interfaces using GPR to be applied in the near-surface region. In this work, we are not addressing the problem of depth estimation, rather confining ourselves to detecting an interface within a particular depth range
A Non-Cooperative Game Theoretical Approach For Power Control In Virtual MIMO Wireless Sensor Network
Power management is one of the vital issue in wireless sensor networks, where
the lifetime of the network relies on battery powered nodes. Transmitting at
high power reduces the lifetime of both the nodes and the network. One
efficient way of power management is to control the power at which the nodes
transmit. In this paper, a virtual multiple input multiple output wireless
sensor network (VMIMO-WSN)communication architecture is considered and the
power control of sensor nodes based on the approach of game theory is
formulated. The use of game theory has proliferated, with a broad range of
applications in wireless sensor networking. Approaches from game theory can be
used to optimize node level as well as network wide performance. The game here
is categorized as an incomplete information game, in which the nodes do not
have complete information about the strategies taken by other nodes. For
virtual multiple input multiple output wireless sensor network architecture
considered, the Nash equilibrium is used to decide the optimal power level at
which a node needs to transmit, to maximize its utility. Outcome shows that the
game theoretic approach considered for VMIMO-WSN architecture achieves the best
utility, by consuming less power.Comment: 12 pages, 8 figure
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