1,089 research outputs found
RORS: Enhanced Rule-based OWL Reasoning on Spark
The rule-based OWL reasoning is to compute the deductive closure of an
ontology by applying RDF/RDFS and OWL entailment rules. The performance of the
rule-based OWL reasoning is often sensitive to the rule execution order. In
this paper, we present an approach to enhancing the performance of the
rule-based OWL reasoning on Spark based on a locally optimal executable
strategy. Firstly, we divide all rules (27 in total) into four main classes,
namely, SPO rules (5 rules), type rules (7 rules), sameAs rules (7 rules), and
schema rules (8 rules) since, as we investigated, those triples corresponding
to the first three classes of rules are overwhelming (e.g., over 99% in the
LUBM dataset) in our practical world. Secondly, based on the interdependence
among those entailment rules in each class, we pick out an optimal rule
executable order of each class and then combine them into a new rule execution
order of all rules. Finally, we implement the new rule execution order on Spark
in a prototype called RORS. The experimental results show that the running time
of RORS is improved by about 30% as compared to Kim & Park's algorithm (2015)
using the LUBM200 (27.6 million triples).Comment: 12 page
On the Construction of Radio Environment Maps for Cognitive Radio Networks
The Radio Environment Map (REM) provides an effective approach to Dynamic
Spectrum Access (DSA) in Cognitive Radio Networks (CRNs). Previous results on
REM construction show that there exists a tradeoff between the number of
measurements (sensors) and REM accuracy. In this paper, we analyze this
tradeoff and determine that the REM error is a decreasing and convex function
of the number of measurements (sensors). The concept of geographic entropy is
introduced to quantify this relationship. And the influence of sensor
deployment on REM accuracy is examined using information theory techniques. The
results obtained in this paper are applicable not only for the REM, but also
for wireless sensor network deployment.Comment: 6 pages, 7 figures, IEEE WCNC conferenc
Geo-Spatio-Temporal Information Based 3D Cooperative Positioning in LOS/NLOS Mixed Environments
We propose a geographic and spatio-temporal information based distributed
cooperative positioning (GSTICP) algorithm for wireless networks that require
three-dimensional (3D) coordinates and operate in the line-of-sight (LOS) and
nonline-of-sight (NLOS) mixed environments. First, a factor graph (FG) is
created by factorizing the a posteriori distribution of the position-vector
estimates and mapping the spatial-domain and temporal-domain operations of
nodes onto the FG. Then, we exploit a geographic information based NLOS
identification scheme to reduce the performance degradation caused by NLOS
measurements. Furthermore, we utilize a finite symmetric sampling based scaled
unscented transform (SUT) method to approximate the nonlinear terms of the
messages passing on the FG with high precision, despite using only a small
number of samples. Finally, we propose an enhanced anchor upgrading (EAU)
mechanism to avoid redundant iterations. Our GSTICP algorithm supports any type
of ranging measurement that can determine the distance between nodes.
Simulation results and analysis demonstrate that our GSTICP has a lower
computational complexity than the state-of-the-art belief propagation (BP)
based localizers, while achieving an even more competitive positioning
performance.Comment: 6 pages, 5 figures, accepted to appear on IEEE Globecom, Aug. 2022.
arXiv admin note: text overlap with arXiv:2208.1185
Advanced framework for microscopic and lane‐level macroscopic traffic parameters estimation from UAV video
Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/166282/1/itr2bf00873.pd
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