179,187 research outputs found
Near Optimal Channel Assignment for Interference Mitigation in Wireless Mesh Networks
In multi-radio multi-channel (MRMC) WMNs, interference alleviation is
affected through several network design techniques e.g., channel assignment
(CA), link scheduling, routing etc., intelligent CA schemes being the most
effective tool for interference mitigation. CA in WMNs is an NP-Hard problem,
and makes optimality a desired yet elusive goal in real-time deployments which
are characterized by fast transmission and switching times and minimal
end-to-end latency. The trade-off between optimal performance and minimal
response times is often achieved through CA schemes that employ heuristics to
propose efficient solutions. WMN configuration and physical layout are also
crucial factors which decide network performance, and it has been demonstrated
in numerous research works that rectangular/square grid WMNs outperform random
or unplanned WMN deployments in terms of network capacity, latency, and network
resilience. In this work, we propose a smart heuristic approach to devise a
near-optimal CA algorithm for grid WMNs (NOCAG). We demonstrate the efficacy of
NOCAG by evaluating its performance against the minimal-interference CA
generated through a rudimentary brute-force technique (BFCA), for the same WMN
configuration. We assess its ability to mitigate interference both,
theoretically (through interference estimation metrics) and experimentally (by
running rigorous simulations in NS-3). We demonstrate that the performance of
NOCAG is almost as good as the BFCA, at a minimal computational overhead of
O(n) compared to the exponential of BFCA
MScMS-II: an innovative IR-based indoor coordinate measuring system for large-scale metrology applications
According to the current great interest concerning large-scale metrology applications in many different fields of manufacturing industry, technologies and techniques for dimensional measurement have recently shown a substantial improvement. Ease-of-use, logistic and economic issues, as well as metrological performance are assuming a more and more important role among system requirements. This paper describes the architecture and the working principles of a novel infrared (IR) optical-based system, designed to perform low-cost and easy indoor coordinate measurements of large-size objects. The system consists of a distributed network-based layout, whose modularity allows fitting differently sized and shaped working volumes by adequately increasing the number of sensing units. Differently from existing spatially distributed metrological instruments, the remote sensor devices are intended to provide embedded data elaboration capabilities, in order to share the overall computational load. The overall system functionalities, including distributed layout configuration, network self-calibration, 3D point localization, and measurement data elaboration, are discussed. A preliminary metrological characterization of system performance, based on experimental testing, is also presente
Computing Vertex Centrality Measures in Massive Real Networks with a Neural Learning Model
Vertex centrality measures are a multi-purpose analysis tool, commonly used
in many application environments to retrieve information and unveil knowledge
from the graphs and network structural properties. However, the algorithms of
such metrics are expensive in terms of computational resources when running
real-time applications or massive real world networks. Thus, approximation
techniques have been developed and used to compute the measures in such
scenarios. In this paper, we demonstrate and analyze the use of neural network
learning algorithms to tackle such task and compare their performance in terms
of solution quality and computation time with other techniques from the
literature. Our work offers several contributions. We highlight both the pros
and cons of approximating centralities though neural learning. By empirical
means and statistics, we then show that the regression model generated with a
feedforward neural networks trained by the Levenberg-Marquardt algorithm is not
only the best option considering computational resources, but also achieves the
best solution quality for relevant applications and large-scale networks.
Keywords: Vertex Centrality Measures, Neural Networks, Complex Network Models,
Machine Learning, Regression ModelComment: 8 pages, 5 tables, 2 figures, version accepted at IJCNN 2018. arXiv
admin note: text overlap with arXiv:1810.1176
One-step Estimation of Networked Population Size: Respondent-Driven Capture-Recapture with Anonymity
Population size estimates for hidden and hard-to-reach populations are
particularly important when members are known to suffer from disproportion
health issues or to pose health risks to the larger ambient population in which
they are embedded. Efforts to derive size estimates are often frustrated by a
range of factors that preclude conventional survey strategies, including social
stigma associated with group membership or members' involvement in illegal
activities.
This paper extends prior research on the problem of network population size
estimation, building on established survey/sampling methodologies commonly used
with hard-to-reach groups. Three novel one-step, network-based population size
estimators are presented, to be used in the context of uniform random sampling,
respondent-driven sampling, and when networks exhibit significant clustering
effects. Provably sufficient conditions for the consistency of these estimators
(in large configuration networks) are given. Simulation experiments across a
wide range of synthetic network topologies validate the performance of the
estimators, which are seen to perform well on a real-world location-based
social networking data set with significant clustering. Finally, the proposed
schemes are extended to allow them to be used in settings where participant
anonymity is required. Systematic experiments show favorable tradeoffs between
anonymity guarantees and estimator performance.
Taken together, we demonstrate that reasonable population estimates can be
derived from anonymous respondent driven samples of 250-750 individuals, within
ambient populations of 5,000-40,000. The method thus represents a novel and
cost-effective means for health planners and those agencies concerned with
health and disease surveillance to estimate the size of hidden populations.
Limitations and future work are discussed in the concluding section
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