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An Effective Algorithm to Find a Cost Minimizing Gateway Deployment for Node-Replaceable Wireless Sensor Networks.
Choi, Sun-Ho; Jang, Yoonkyung; Seo, Hyowon; Hong, Bum Il; Ryoo, Intae.
Afiliação
  • Choi SH; Department of Applied Mathematics and the Institute of Natural Sciences, Kyung Hee University, Yongin 17104, Korea.
  • Jang Y; Department of Computer Science and Engineering, Kyung Hee University, Yongin 17104, Korea.
  • Seo H; Department of Applied Mathematics and the Institute of Natural Sciences, Kyung Hee University, Yongin 17104, Korea.
  • Hong BI; Department of Applied Mathematics and the Institute of Natural Sciences, Kyung Hee University, Yongin 17104, Korea.
  • Ryoo I; Department of Computer Science and Engineering, Kyung Hee University, Yongin 17104, Korea.
Sensors (Basel) ; 21(5)2021 Mar 03.
Article em En | MEDLINE | ID: mdl-33802352
In this paper, we present an efficient way to find a gateway deployment for a given sensor network topology. We assume that the expired sensors and gateways can be replaced and the locations of the gateways are chosen among the given sensor nodes. The objective is to find a gateway deployment that minimizes the cost per unit time, which consists of the maintenance and installation costs. The proposed algorithm creates a cost reference and uses it to find the optimal deployment via a divide and conquer algorithm. Comparing all cases is the most reliable way to find the optimal gateway deployment, but this is practically impossible to calculate, since its computation time increases exponentially as the number of nodes increases. The method we propose increases linearly, and so is suitable for large scale networks. Additionally, compared to stochastic algorithms such as the genetic algorithm, this methodology has advantages in computational speed and accuracy for a large number of nodes. We also verify our methodology through several numerical experiments.
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Texto completo: 1 Base de dados: MEDLINE Idioma: En Ano de publicação: 2021 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Idioma: En Ano de publicação: 2021 Tipo de documento: Article