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Dilan Sahin

Researcher at Bahçeşehir University

Publications -  11
Citations -  3749

Dilan Sahin is an academic researcher from Bahçeşehir University. The author has contributed to research in topics: Smart grid & Wireless sensor network. The author has an hindex of 7, co-authored 11 publications receiving 3360 citations. Previous affiliations of Dilan Sahin include University of Michigan.

Papers
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Journal ArticleDOI

Smart Grid Technologies: Communication Technologies and Standards

TL;DR: The main objective of this paper is to provide a contemporary look at the current state of the art in smart grid communications as well as to discuss the still-open research issues in this field.
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A Survey on Smart Grid Potential Applications and Communication Requirements

TL;DR: This paper overviews the issues related to the smart grid architecture from the perspective of potential applications and the communications requirements needed for ensuring performance, flexible operation, reliability and economics.
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Smart Grid and Smart Homes: Key Players and Pilot Projects

TL;DR: The smart grid is envisioned as providing a communications network for the energy industry, similar to that which the Internet now provides for business and personal communications as mentioned in this paper, which offers new business opportunities for different kind of industries, such as smart-meter vendors, electric utilities, and telecom operators from all around the world.
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Cognitive Radio Networks for Smart Grid Applications: A Promising Technology to Overcome Spectrum Inefficiency

TL;DR: A comprehensive review about SG characteristics and CR-based SG applications and architectures to support CR networks in SG applications, major challenges, and open issues are presented.
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Code-Smell Detection as a Bilevel Problem

TL;DR: The statistical analysis of the experiments over 31 runs on nine open-source systems and one industrial project shows that seven types of code smells were detected with an average of more than 86% in terms of precision and recall, which confirms the outperformance of the bilevel proposal compared to state-of-art code-smell detection techniques.