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Exploring Visual Analytics to Measure Reliability for IoT Oriented Pollution Detection Software Perspectives

Exploring Visual Analytics to Measure Reliability for IoT Oriented Pollution Detection Software Perspectives

Nishi Kant Kumar, Soumya Banerjee
Copyright: © 2019 |Volume: 10 |Issue: 2 |Pages: 19
ISSN: 1947-3532|EISSN: 1947-3540|EISBN13: 9781522566854|DOI: 10.4018/IJDST.2019040101
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MLA

Kumar, Nishi Kant, and Soumya Banerjee. "Exploring Visual Analytics to Measure Reliability for IoT Oriented Pollution Detection Software Perspectives." IJDST vol.10, no.2 2019: pp.1-19. http://doi.org/10.4018/IJDST.2019040101

APA

Kumar, N. K. & Banerjee, S. (2019). Exploring Visual Analytics to Measure Reliability for IoT Oriented Pollution Detection Software Perspectives. International Journal of Distributed Systems and Technologies (IJDST), 10(2), 1-19. http://doi.org/10.4018/IJDST.2019040101

Chicago

Kumar, Nishi Kant, and Soumya Banerjee. "Exploring Visual Analytics to Measure Reliability for IoT Oriented Pollution Detection Software Perspectives," International Journal of Distributed Systems and Technologies (IJDST) 10, no.2: 1-19. http://doi.org/10.4018/IJDST.2019040101

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Abstract

The measurement of the reliability of such IoT based application requires an embedded analysis. The parameters are the number of imprecise or faulty measures as well as the identification of core modules. This article investigates that how far visual introspection can assist in troubleshooting of IoT-based software bugs. This specific requirement improvises a new idea, where the shape of the plots with actual data can indicate the cause of the error and further they can be patched if the software repairing strategies are implemented adjudging the visual analytics. It is quite indifferent to analyze faults for existing applications as a variation of topological and practicing parameters which takes substantial numbers of iterations and observations. Categorically, the present use-case establishes the fact to analyze and infer concerning the shape of the visual plots derived from embedded modules.

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