2,483 research outputs found
Localizability of Wireless Sensor Networks: Beyond Wheel Extension
A network is called localizable if the positions of all the nodes of the
network can be computed uniquely. If a network is localizable and embedded in
plane with generic configuration, the positions of the nodes may be computed
uniquely in finite time. Therefore, identifying localizable networks is an
important function. If the complete information about the network is available
at a single place, localizability can be tested in polynomial time. In a
distributed environment, networks with trilateration orderings (popular in real
applications) and wheel extensions (a specific class of localizable networks)
embedded in plane can be identified by existing techniques. We propose a
distributed technique which efficiently identifies a larger class of
localizable networks. This class covers both trilateration and wheel
extensions. In reality, exact distance is almost impossible or costly. The
proposed algorithm based only on connectivity information. It requires no
distance information
Distributed Detection of Cycles
Distributed property testing in networks has been introduced by Brakerski and
Patt-Shamir (2011), with the objective of detecting the presence of large dense
sub-networks in a distributed manner. Recently, Censor-Hillel et al. (2016)
have shown how to detect 3-cycles in a constant number of rounds by a
distributed algorithm. In a follow up work, Fraigniaud et al. (2016) have shown
how to detect 4-cycles in a constant number of rounds as well. However, the
techniques in these latter works were shown not to generalize to larger cycles
with . In this paper, we completely settle the problem of cycle
detection, by establishing the following result. For every , there
exists a distributed property testing algorithm for -freeness, performing
in a constant number of rounds. All these results hold in the classical CONGEST
model for distributed network computing. Our algorithm is 1-sided error. Its
round-complexity is where is the property
testing parameter measuring the gap between legal and illegal instances
An Adaptive Design Methodology for Reduction of Product Development Risk
Embedded systems interaction with environment inherently complicates
understanding of requirements and their correct implementation. However,
product uncertainty is highest during early stages of development. Design
verification is an essential step in the development of any system, especially
for Embedded System. This paper introduces a novel adaptive design methodology,
which incorporates step-wise prototyping and verification. With each adaptive
step product-realization level is enhanced while decreasing the level of
product uncertainty, thereby reducing the overall costs. The back-bone of this
frame-work is the development of Domain Specific Operational (DOP) Model and
the associated Verification Instrumentation for Test and Evaluation, developed
based on the DOP model. Together they generate functionally valid test-sequence
for carrying out prototype evaluation. With the help of a case study 'Multimode
Detection Subsystem' the application of this method is sketched. The design
methodologies can be compared by defining and computing a generic performance
criterion like Average design-cycle Risk. For the case study, by computing
Average design-cycle Risk, it is shown that the adaptive method reduces the
product development risk for a small increase in the total design cycle time.Comment: 21 pages, 9 figure
Predicting and Recovering Link Failure Localization Using Competitive Swarm Optimization for DSR Protocol in MANET
Portable impromptu organization is a self-putting together, major construction-less, independent remote versatile hub that exists without even a trace of a determined base station or government association. MANET requires no extraordinary foundation as the organization is unique. Multicasting is an urgent issue in correspondence organizations. Multicast is one of the effective methods in MANET. In multicasting, information parcels from one hub are communicated to a bunch of recipient hubs all at once, at a similar time. In this research work, Failure Node Detection and Efficient Node Localization in a MANET situation are proposed. Localization in MANET is a main area that attracts significant research interest. Localization is a method to determine the nodes’ location in the communication network. A novel routing algorithm, which is used for Predicting and Recovering Link Failure Localization using a Genetic Algorithm with Competitive Swarm Optimization (PRLFL-GACSO) Algorithm is proposed in this study to calculate and recover link failure in MANET. The process of link failure detection is accomplished using mathematical modelling of the genetic algorithm and the routing is attained using the Competitive Swarm optimization technique. The result proposed MANET method makes use of the CSO algorithm, which facilitates a well-organized packet transfer from the source node to the destination node and enhances DSR routing performance. Based on node movement, link value, and endwise delay, the optimal route is found. The main benefit of the PRLFL-GACSO Algorithm is it achieves multiple optimal solutions over global information. Further, premature convergence is avoided using Competitive Swarm Optimization (CSO). The suggested work is measured based on the Ns simulator. The presentation metrix are PDR, endwise delay, power consumption, hit ratio, etc. The presentation of the proposed method is almost 4% and 5% greater than the present TEA-MDRP, RSTA-AOMDV, and RMQS-ua methods. After, the suggested method attains greater performance for detecting and recovering link failure. In future work, the hybrid multiway routing protocols are presented to provide link failure and route breakages and liability tolerance at the time of node failure, and it also increases the worth of service aspects, respectively
Approximate Inference for Constructing Astronomical Catalogs from Images
We present a new, fully generative model for constructing astronomical
catalogs from optical telescope image sets. Each pixel intensity is treated as
a random variable with parameters that depend on the latent properties of stars
and galaxies. These latent properties are themselves modeled as random. We
compare two procedures for posterior inference. One procedure is based on
Markov chain Monte Carlo (MCMC) while the other is based on variational
inference (VI). The MCMC procedure excels at quantifying uncertainty, while the
VI procedure is 1000 times faster. On a supercomputer, the VI procedure
efficiently uses 665,000 CPU cores to construct an astronomical catalog from 50
terabytes of images in 14.6 minutes, demonstrating the scaling characteristics
necessary to construct catalogs for upcoming astronomical surveys.Comment: accepted to the Annals of Applied Statistic
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