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A Novel Theoretical Probabilistic Model for Opportunistic Routing with Applications in Energy Consumption for WSNs.
Galarza, Christian E; Palma, Jonathan M; Morais, Cecilia F; Utria, Jaime; Carvalho, Leonardo P; Bustos, Daniel; Oliveira, Ricardo C L F.
Afiliação
  • Galarza CE; Escuela Superior Politécnica del Litoral-ESPOL, Facultad de Ciencias Naturales y Matemáticas, Vía Perimetral 5, Guayaquil 090150, Ecuador.
  • Palma JM; Department of Electrical Engineering, Faculty of Engineering, University of Talca, Curicó 3344158, Chile.
  • Morais CF; Center of Exact, Environmental and Technological Sciences, Pontifical Catholic University of Campinas, Campinas 13086-900, SP, Brazil.
  • Utria J; Institute of Mathematics and Statistics, Fluminense Federal University-UFF, Niterói 24210-201, RJ, Brazil.
  • Carvalho LP; Discrete Technology and Production Automation (DTPA), Rijksuniversiteit Groningen, 9712 CP Groningen, The Netherlands.
  • Bustos D; Polytechnic School, University of São Paulo, São Paulo 05508-900, SP, Brazil.
  • Oliveira RCLF; Centro de Investigación de Estudios Avanzados del Maule (CIEAM), Vicerrectoría de Investigación y Postgrado, Universidad Católica del Maule, Talca 3460000, Chile.
Sensors (Basel) ; 21(23)2021 Dec 02.
Article em En | MEDLINE | ID: mdl-34884063
ABSTRACT
This paper proposes a new theoretical stochastic model based on an abstraction of the opportunistic model for opportunistic networks. The model is capable of systematically computing the network parameters, such as the number of possible routes, the probability of successful transmission, the expected number of broadcast transmissions, and the expected number of receptions. The usual theoretical stochastic model explored in the methodologies available in the literature is based on Markov chains, and the main novelty of this paper is the employment of a percolation stochastic model, whose main benefit is to obtain the network parameters directly. Additionally, the proposed approach is capable to deal with values of probability specified by bounded intervals or by a density function. The model is validated via Monte Carlo simulations, and a computational toolbox (R-packet) is provided to make the reproduction of the results presented in the paper easier. The technique is illustrated through a numerical example where the proposed model is applied to compute the energy consumption when transmitting a packet via an opportunistic network.
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Texto completo: 1 Base de dados: MEDLINE Idioma: En Revista: Sensors (Basel) Ano de publicação: 2021 Tipo de documento: Article País de afiliação: Equador

Texto completo: 1 Base de dados: MEDLINE Idioma: En Revista: Sensors (Basel) Ano de publicação: 2021 Tipo de documento: Article País de afiliação: Equador