5 research outputs found
Design and Implementation of a Heterogeneous Sensor-based Embedded System for Flood Management
Floods are one of the most common forms of natural disasters in the world, and cause huge loss of life, and property. There is a critical requirement for development and installation of enhanced flood forecasting sites in various commonly flooding regions of the world. In this paper, we describe the design and implementation of a novel heterogeneous sensor-based embedded system for flood management. This embedded system enables various types of electronic gages to be deployed at remote locations, wherever mobile network is available. Acquisition of hydrologic data occurs at user defined intervals of time, and is uploaded to a database, through the internet. Information acquired into the database can then be easily viewed from anywhere, used for analysis, and running flood forecasting simulation models. The overall system architecture, module description, and results are described here
Selecting a suitable Parallel Label-propagation based algorithm for Disjoint Community Detection
Community detection is an essential task in network analysis as it helps
identify groups and patterns within a network. High-speed community detection
algorithms are necessary to analyze large-scale networks in a reasonable amount
of time. Researchers have made significant contributions in the development of
high-speed community detection algorithms, particularly in the area of
label-propagation based disjoint community detection. These algorithms have
been proven to be highly effective in analyzing large-scale networks in a
reasonable amount of time. However, it is important to evaluate the performance
and accuracy of these existing methods to determine which algorithm is best
suited for a particular type of network and specific research problem. In this
report, we investigate the RAK, COPRA, and SLPA, three label-propagation-based
static community discovery techniques. We pay close attention to each
algorithm's minute details as we implement both its single-threaded and
multi-threaded OpenMP-based variants, making any necessary adjustments or
optimizations and obtaining the right parameter values. The RAK algorithm is
found to perform well with a tolerance of 0.05 and OpenMP-based strict RAK with
12 threads was 6.75x faster than the sequential non-strict RAK. The COPRA
algorithm works well with a single label for road networks and max labels of
4-16 for other classes of graphs. The SLPA algorithm performs well with
increasing memory size, but overall doesn't offer a favourable return on
investment. The RAK algorithm is recommended for label-propagation based
disjoint community detection.Comment: 11 pages, 1 tabl
Heuristics for Inequality minimization in PageRank values
This research study investigates the minimization of inequality in the ranks
of vertices obtained using the PageRank algorithm. PageRank is a widely used
algorithm for ranking webpages and plays a significant role in determining web
traffic. This study employs the Gini coefficient, a measure of income/wealth
inequality, to assess the inequality in PageRank distributions on various types
of graphs. The investigation involves two experiments: one that modifies
strategies for handling dead-end nodes and another that explores six
deterministic methods for reducing inequality. Our findings indicate that a
combination of two distinct heuristics may present an effective strategy for
minimizing inequality.Comment: 4 pages, 3 figure