A 3D multi-objective optimization planning algorithm for wireless sensor networks

He, Danping; Portilla Berrueco, Jorge y Riesgo Alcaide, Teresa (2013). A 3D multi-objective optimization planning algorithm for wireless sensor networks. En: "39th Annual Conference of the IEEE Industrial Electronics Society (IECON 2013)", 10/11/2013 - 13/11/2013, Viena, Austria. pp. 5426-5431. https://doi.org/10.1109/IECON.2013.6700019.

Descripción

Título: A 3D multi-objective optimization planning algorithm for wireless sensor networks
Autor/es:
  • He, Danping
  • Portilla Berrueco, Jorge
  • Riesgo Alcaide, Teresa
Tipo de Documento: Ponencia en Congreso o Jornada (Artículo)
Título del Evento: 39th Annual Conference of the IEEE Industrial Electronics Society (IECON 2013)
Fechas del Evento: 10/11/2013 - 13/11/2013
Lugar del Evento: Viena, Austria
Título del Libro: A 3D multi-objective optimization planning algorithm for wireless sensor networks
Fecha: 2013
Materias:
Escuela: Centro de Electrónica Industrial (CEI) (UPM)
Departamento: Otro
Licencias Creative Commons: Reconocimiento - Sin obra derivada - No comercial

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Resumen

The complexity of planning a wireless sensor network is dependent on the aspects of optimization and on the application requirements. Even though Murphy's Law is applied everywhere in reality, a good planning algorithm will assist the designers to be aware of the short plates of their design and to improve them before the problems being exposed at the real deployment. A 3D multi-objective planning algorithm is proposed in this paper to provide solutions on the locations of nodes and their properties. It employs a developed ray-tracing scheme for sensing signal and radio propagation modelling. Therefore it is sensitive to the obstacles and makes the models of sensing coverage and link quality more practical compared with other heuristics that use ideal unit-disk models. The proposed algorithm aims at reaching an overall optimization on hardware cost, coverage, link quality and lifetime. Thus each of those metrics are modelled and normalized to compose a desirability function. Evolutionary algorithm is designed to efficiently tackle this NP-hard multi-objective optimization problem. The proposed algorithm is applicable for both indoor and outdoor 3D scenarios. Different parameters that affect the performance are analyzed through extensive experiments; two state-of-the-art algorithms are rebuilt and tested with the same configuration as that of the proposed algorithm. The results indicate that the proposed algorithm converges efficiently within 600 iterations and performs better than the compared heuristics.

Más información

ID de Registro: 31138
Identificador DC: http://oa.upm.es/31138/
Identificador OAI: oai:oa.upm.es:31138
Identificador DOI: 10.1109/IECON.2013.6700019
Depositado por: Memoria Investigacion
Depositado el: 27 Abr 2015 19:07
Ultima Modificación: 27 Abr 2015 19:07
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