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Towards Risk Based Effort Estimation: A Framework to Identify, Analyze, and Classify Risk for Early Identification at Requirement Engineering Phase

Towards Risk Based Effort Estimation: A Framework to Identify, Analyze, and Classify Risk for Early Identification at Requirement Engineering Phase

Priyanka Chandani, Chetna Gupta
Copyright: © 2018 |Volume: 9 |Issue: 4 |Pages: 18
ISSN: 1947-8186|EISSN: 1947-8194|EISBN13: 9781522545118|DOI: 10.4018/IJISMD.2018100104
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MLA

Chandani, Priyanka, and Chetna Gupta. "Towards Risk Based Effort Estimation: A Framework to Identify, Analyze, and Classify Risk for Early Identification at Requirement Engineering Phase." IJISMD vol.9, no.4 2018: pp.54-71. http://doi.org/10.4018/IJISMD.2018100104

APA

Chandani, P. & Gupta, C. (2018). Towards Risk Based Effort Estimation: A Framework to Identify, Analyze, and Classify Risk for Early Identification at Requirement Engineering Phase. International Journal of Information System Modeling and Design (IJISMD), 9(4), 54-71. http://doi.org/10.4018/IJISMD.2018100104

Chicago

Chandani, Priyanka, and Chetna Gupta. "Towards Risk Based Effort Estimation: A Framework to Identify, Analyze, and Classify Risk for Early Identification at Requirement Engineering Phase," International Journal of Information System Modeling and Design (IJISMD) 9, no.4: 54-71. http://doi.org/10.4018/IJISMD.2018100104

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Abstract

Power consumption mainly takes place in three stages: processing the data, receiving the data, and transmitting the data. Power consumption in data transmitting is one of the most important phenomena in wireless sensor networks (WSNs). In this article, authors analyze the power transmission in three scenarios with 100 and 500 nodes for 100 and 1000 sq. meters of area respectively and design a network which should be more efficient in power saving. Results analysis section presents different data aggregation techniques and their impact on the power transmission in WSNs. Three different scenarios have been used during simulation of network in Matlab. After that, the authors find that the proposed approach has outperformed in the first two scenarios. However, in the third scenario, results are partially better as compared to the existing approaches (tree-based, cluster-based, chain-based, and grid-based). The proposed approach, PLBDA, is 10.30%, 18.55%, 37.11%, and 55.67% better for transmission power save in comparison to tree-based, cluster-based, grid-based, and chain-based approaches respectively.

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