3,816 research outputs found
Rethinking the Intercept Probability of Random Linear Network Coding
This letter considers a network comprising a transmitter, which employs
random linear network coding to encode a message, a legitimate receiver, which
can recover the message if it gathers a sufficient number of linearly
independent coded packets, and an eavesdropper. Closed-form expressions for the
probability of the eavesdropper intercepting enough coded packets to recover
the message are derived. Transmission with and without feedback is studied.
Furthermore, an optimization model that minimizes the intercept probability
under delay and reliability constraints is presented. Results validate the
proposed analysis and quantify the secrecy gain offered by a feedback link from
the legitimate receiver.Comment: IEEE Communications Letters, to appea
On Intercept Probability Minimization under Sparse Random Linear Network Coding
This paper considers a network where a node wishes to transmit a source
message to a legitimate receiver in the presence of an eavesdropper. The
transmitter secures its transmissions employing a sparse implementation of
Random Linear Network Coding (RLNC). A tight approximation to the probability
of the eavesdropper recovering the source message is provided. The proposed
approximation applies to both the cases where transmissions occur without
feedback or where the reliability of the feedback channel is impaired by an
eavesdropper jamming the feedback channel. An optimization framework for
minimizing the intercept probability by optimizing the sparsity of the RLNC is
also presented. Results validate the proposed approximation and quantify the
gain provided by our optimization over solutions where non-sparse RLNC is used.Comment: To appear on IEEE Transactions on Vehicular Technolog
Secure Data Offloading Strategy for Connected and Autonomous Vehicles
Connected and Automated Vehicles (CAVs) are expected to constantly interact
with a network of processing nodes installed in secure cabinets located at the
side of the road -- thus, forming Fog Computing-based infrastructure for
Intelligent Transportation Systems (ITSs). Future city-scale ITS services will
heavily rely upon the sensor data regularly off-loaded by each CAV on the Fog
Computing network. Due to the broadcast nature of the medium, CAVs'
communications can be vulnerable to eavesdropping. This paper proposes a novel
data offloading approach where the Random Linear Network Coding (RLNC)
principle is used to ensure the probability of an eavesdropper to recover
relevant portions of sensor data is minimized. Our preliminary results confirm
the effectiveness of our approach when operated in a large-scale ITS networks.Comment: To appear in IEEE VTC-Spring 201
Random linear network coding based physical layer security for relay-aided device-to-device communication
We investigate physical layer security design, which employs random linear network coding with opportunistic relaying and jamming to exploit the secrecy benefit of both source and relay transmissions. The proposed scheme requires the source to transmit artificial noise along with a confidential message. Moreover, in order to further improve the dynamical behaviour of the network against an eavesdropping attack, aggregated power controlled transmissions with optimal power allocation strategy is considered. The network security is accurately characterised by the probability that the eavesdropper will manage to intercept a sufficient number of coded packets to partially or fully recover the confidential message
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Improving the reliability of model-based decision-making estimates in the two-stage decision task with reaction-times and drift-diffusion modeling
A well-established notion in cognitive neuroscience proposes that multiple brain systems contribute to choice behaviour. These include: (1) a model-free system that uses values cached from the outcome history of alternative actions, and (2) a model-based system that considers action outcomes and the transition structure of the environment. The widespread use of this distinction, across a range of applications, renders it important to index their distinct influences with high reliability. Here we consider the two-stage task, widely considered as a gold standard measure for the contribution of model-based and model-free systems to human choice. We tested the internal/temporal stability of measures from this task, including those estimated via an established computational model, as well as an extended model using drift-diffusion. Drift-diffusion modeling suggested that both choice in the first stage, and RTs in the second stage, are directly affected by a model-based/free trade-off parameter. Both parameter recovery and the stability of model-based estimates were poor but improved substantially when both choice and RT were used (compared to choice only), and when more trials (than conventionally used in research practice) were included in our analysis. The findings have implications for interpretation of past and future studies based on the use of the two-stage task, as well as for characterising the contribution of model-based processes to choice behaviour
The impact of partial packet recovery on the inherent secrecy of random linear coding
This paper considers a source, which employs random linear coding (RLC) to encode a message, a legitimate destination, which can recover the message if it gathers a sufficient number of coded packets, and an eavesdropper. The probability of the eavesdropper accumulating enough coded packets to recover the message, known as the intercept probability, has been studied in the literature. In our work, the eavesdropper does not abandon its efforts to obtain the source message if RLC decoding has been unsuccessful; instead, it employs partial packet recovery (PPR) offline in an effort to repair erroneously received coded packets before it attempts RLC decoding again. Results show that PPR-assisted RLC decoding marginally increases the intercept probability, compared to RLC decoding, when the channel conditions are good. However, as the channel conditions deteriorate, PPR-assisted RLC decoding significantly improves the chances of the eavesdropper recovering the source message, even if the eavesdropper experiences similar or worse channel conditions than the destination
Early survival and growth plasticity of 33 species planted in 38 arboreta across the European Atlantic area
To anticipate European climate scenarios for the end of the century, we explored the climate
gradient within the REINFFORCE (RÉseau INFrastructure de recherche pour le suivi et l’adaptation
des FORêts au Changement climatiquE) arboreta network, established in 38 sites between latitudes
37 and 57 , where 33 tree species are represented. We aim to determine which climatic variables
best explain their survival and growth, and identify those species that are more tolerant of climate
variation and those of which the growth and survival future climate might constrain. We used
empirical models to determine the best climatic predictor variables that explain tree survival and
growth. Precipitation-transfer distance was most important for the survival of broadleaved species,
whereas growing-season-degree days best explained conifer-tree survival. Growth (annual height
increment) was mainly explained by a derived annual dryness index (ADI) for both conifers and
broadleaved trees. Species that showed the greatest variation in survival and growth in response
to climatic variation included Betula pendula Roth, Pinus elliottii Engelm., and Thuja plicata Donn
ex D.Don, and those that were least affected included Quercus shumardii Buckland and Pinus nigra
J.F.Arnold. We also demonstrated that provenance differences were significant for Pinus pinea L., Quercus robur L., and Ceratonia siliqua L. Here, we demonstrate the usefulness of infrastructures along
a climatic gradient like REINFFORCE to determine major tendencies of tree species responding to
climate changesinfo:eu-repo/semantics/publishedVersio
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