NS

Research

My research interests include Distributed Computing and Computer Networks. I've analyzed gigabytes of Hadoop log traces to identify caching potential on network nodes, explored underwater wireless sensor networks, and leaded a team to design and develop the software infrastructure for the largest telescope being built in the world (LSST).

A Survey on Data Aggregation and Clustering Schemes in Underwater Sensor Networks

Int. J. Systems, Control and Communications, Vol. 7, No. 2, 2016

Energy consumption is one of the most challenging constraintsof the design and implementation of the sensor network. Underwater sensor networking is the technology that enables the applications like environment monitoring, underwater exploration, seismic monitoring and other surveillance applications. In underwater sensor network, a sensor node senses the data and transmits it to the sink. Many routing algorithms have been proposed in order to make the network phase of UWSNs more efficient.In this report, we present a review and comparison of various data collection algorithms and clustering schemes, proposed recently in order to execute the demands of the ongoing researches. The main goal of data aggregation technique is to accumulate data in an energy efficient manner for a long-term network monitoring. The main purpose of this study is to present algorithms addressing issues like deployment and localization in UWSNs under different conditions

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A fuzzy logic-based clustering algorithm for network optimisation

International Journal of Grid Distribution Computing Vol.7, No.6 (2014), pp.29-52

Rate of occurrence of high-dimensional data is much higher and sad to relate, classical clustering techniques do not hold good for such high-dimensional networks of arbitrary shapes in the underwater wireless sensor network. This is mainly due to the fact that clustering techniques are highly parameterised. Data aggregation using traditional clustering techniques is a problem for high-dimensional network because of its arbitrary shapes. Moreover, existing clustering algorithms cannot be used for solving the problem and enhancing clustering performance. Numerous top-notch researchers have employed fuzzy logic-based clustering schemes in the past. We analyse these techniques to find scientific tendency. In this paper, a fuzzy-based algorithm for clustering ensemble is proposed where sensor nodes are characterised based upon major and minor criteria. This algorithm is developed to group the sensor nodes in multiple clusters. The clustering precision is designed to assess the efficacy of the algorithm. This algorithm also competes in its ability to distinguish the difference between all the sensor nodes present in the network. Other clustering algorithms in existence for data aggregation are also reviewed and compared with the proposed technique.

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Machine-learning-based Brokers for Real-time Classification of the LSST Alert Stream

The Astrophysical Journal Supplement Series

The unprecedented volume and rate of transient events that will be discovered by the Large Synoptic Survey Telescope (LSST) demand that the astronomical community update its follow-up paradigm. Alert-brokers—automated software system to sift through, characterize, annotate, and prioritize events for follow-up—will be critical tools for managing alert streams in the LSST era. The Arizona-NOAO Temporal Analysis and Response to Events System (ANTARES) is one such broker. In this work, we develop a machine learning pipeline to characterize and classify variable and transient sources only using the available multiband optical photometry. We describe three illustrative stages of the pipeline, serving the three goals of early, intermediate, and retrospective classification of alerts. The first takes the form of variable versus transient categorization, the second a multiclass typing of the combined variable and transient data set, and the third a purity-driven subtyping of a transient class. Although several similar algorithms have proven themselves in simulations, we validate their performance on real observations for the first time. We quantitatively evaluate our pipeline on sparse, unevenly sampled, heteroskedastic data from various existing observational campaigns, and demonstrate very competitive classification performance. We describe our progress toward adapting the pipeline developed in this work into a real-time broker working on live alert streams from time-domain surveys.

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The ANTARES Astronomical Time-domain Event Broker

The Astronomical Journal

We describe the Arizona-NOIRLab Temporal Analysis and Response to Events System (ANTARES), a software instrument designed to process large-scale streams of astronomical time-domain alerts. With the advent of large-format CCDs on wide-field imaging telescopes, time-domain surveys now routinely discover tens of thousands of new events each night, more than can be evaluated by astronomers alone. The ANTARES event broker will process alerts, annotating them with catalog associations and filtering them to distinguish customizable subsets of events. We describe the data model of the system, the overall architecture, annotation, implementation of filters, system outputs, provenance tracking, system performance, and the user interface.

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