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A Mesh Router Placement Scheme for Minimizing Interference in Indoor Wireless Mesh Networks

ํ•œ๊ตญํ•™์ˆ ์งฟ’์—์„œ ์ œ๊ณตํ•˜๋Š” ๊ตญ๋‚ด ์ตœ๊ณ  ์ˆ˜์ค€์˜ ํ•™์ˆ  ๋ฐ์ดํ„ฐ๋ฒ ์ด์Šค๋ฅผ ํ†ตํ•ด ๋‹ค์–‘ํ•œ ๋…ผ๋ๅฉ๊ณผ ํ•™์ˆ ์ง€ ์ •๋ณด๋ฅผ ๋งŒ๋‚˜๋ณด์„ธ์š”.
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๊ธฐํƒ€ํŒŒ์ผ
์ตœ์ดˆ๋“ฑ๋ก์ผ 2025.05.29 ์ตœ์ข…์ ฟ’์ž‘์ผ 2010.04
6P ๋ฏธ๋้ฉ๋ณด๊ธฐ
A Mesh Router Placement Scheme for Minimizing Interference in Indoor Wireless Mesh Networks
  • ๋ฏธ๋้ฉ๋ณด๊ธฐ

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    Due to the ease of deployment and the extended coverage, wireless mesh networks (WMNs) are gaining popularity and research focus. For example, the routing protocols that enhance the throughput on the WMNs and the link quality measurement schemes are among the popular research topics. However, most of these works assume that the locations of the mesh routers are predetermined. Since the operators in an Indoor mesh network can determine the locations of the mesh routers by themselves, it is essential to the WMN performance for the mesh routers to be initially placed by considering the performance issues. In this paper, we propose a mesh router placement scheme based on genetic algorithms by considering the characteristics of WMNs such as interference and topology. There have been many related works that solve similar problems such as base station placement in cellular networks and gateway node selection in WMNs. However, none of them actually considers the interference to the mesh clients from non-associated mesh routers in determining the locations of the mesh routers. By simulations, we show that the proposed scheme improves the performance by 30-40% compared to the random selection scheme.

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      ํŒŒ์ผ์˜ค๋ฅ˜ ์ค‘๋ณต์ž๋ฃŒ ์ €์ž‘๊ถŒ ์—†์Œ ์„ค๋ช…๊ณผ ์‹ค์ œ ๋‚ด์šฉ ๋ถˆ์ผ์น˜
      ํŒŒ์ผ์˜ ๋‹ค์šด๋กœ๋“œ๊ฐ€ ์ œ๋Œ€๋กœ ๋˜์ง€ ์•Š๊ฑฐ๋‚˜ ํŒŒ์ผํ˜•์‹์— ๋งž๋Š” ํ”„๋กœ๊ทธ๋žจ์œผ๋กœ ์ •์ƒ ์ž‘๋™ํ•˜์ง€ ์•Š๋Š” ๊ฒฝ์šฐ ๋‹ค๋ฅธ ์ž๋ฃŒ์™ฟ’ 70% ์ด์ƒ ๋‚ด์šฉ์ด ์ผ์น˜ํ•˜๋Š” ๊ฒฝ์šฐ (์ค‘๋ณต์ž„์„ ํ™•์ธํ•  ์ˆ˜ ์žˆ๋Š” ๊ทผ๊ฑฐ ํ•„์š”ํ•จ) ์ธํ„ฐ๋„ท์˜ ๋‹ค๋ฅธ ์‚ฌ์ดํŠธ, ์—ฐ๊ตฌ๊ธฐ๊ด€, ํ•™๊ป“, ์„œ์  ๋“ฑ์˜ ์ž๋ฃŒ๋ฅผ ๋„์šฉํ•œ ๊ฒฝ์šฐ ์ž๋ฃŒ์˜ ์„ค๋ช…๊ณผ ์‹ค์ œ ์ž๋ฃŒ์˜ ๋‚ด์šฉ์ด ์ผ์น˜ํ•˜์ง€ ์•Š๋Š” ๊ฒฝ์šฐ

โ€œ์ •๋ณด๊ณผํ•™ํšŒ ์ปดํ“จํŒ…์˜ ์‹ค์ œ ๋…ผ๋ๅฉ์ง€โ€์˜ ๋‹ค๋ฅธ ๋…ผ๋ๅฉ๋„ ํ™•์ธํ•ด ๋ณด์„ธ์š”!

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์•ˆ๋…•ํ•˜์„ธ์š”. ํ•ดํ”ผ์บ ํผ์Šค์˜ ๋ฐฉ๋Œ€ํ•œ ์ž๋ฃŒ ์ค‘์—์„œ ์„ ๋ณ„ํ•˜์—ฌ ๋‹น์‹ ๋งŒ์˜ ์ดˆ์•ˆ์„ ๋งŒ๋“ค์–ด์ฃผ๋Š” EasyAI ์ž…๋‹ˆ๋‹ค.
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์ด๋Ÿฐ ์ฃผ์ œ๋“ค์„ ์ž…๋ ฅํ•ด ๋ณด์„ธ์š”.
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