• DocumentCode
    621678
  • Title

    A genetic approach to plan shopping in the AmI-based Blended Commerce

  • Author

    Gaeta, Matteo ; Loia, Vincenzo ; Orciuoli, Francesco ; Parmentola, Maurizio

  • Author_Institution
    Dipartimento di Ingegneria dell´Informazione, Ingegneria Elettrica e Matematica Applicata, University of Salerno, Fisciano, SA, 84084 Italy
  • fYear
    2013
  • fDate
    28-31 May 2013
  • Firstpage
    1
  • Lastpage
    6
  • Abstract
    This work describes an approach to sinergistically exploit Ambient Intelligence, Smartphones of last generation and Genetic Computation in order to support innovative Blended Commerce scenarios. The paper proposes both a framework for AmI-based Blended Commerce and an instantiation of this framework to implement a scenario where a Shopping Mall is presented as an intelligent environment in which customers use the NFC capabilities of their smartphones in order to manage e-coupons that are produced, suggested (also in a context-aware way) and consumed by the same environment. In this scenario, the main function of the intelligent environment is supporting customers to define shopping plans that minimize their total costs by looking for best prices and most convenient discounts for the needed products. The paper proposes a genetic approach to find sub-optimal solutions for the shopping plan problem that is not trivial given that the final cost for a single product of a plan is dependent by the previous purchases because, in a coupon world, every purchase could generate a discount for next purchases.
  • Keywords
    Ambient intelligence; Biological cells; Business; Context; Genetic algorithms; Service-oriented architecture; Smart phones; Ambient Intelligence; Blended Commerce; Genetic Algorithm; Shopping Plan; e-Couponing;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Industrial Electronics (ISIE), 2013 IEEE International Symposium on
  • Conference_Location
    Taipei, Taiwan
  • ISSN
    2163-5137
  • Print_ISBN
    978-1-4673-5194-2
  • Type

    conf

  • DOI
    10.1109/ISIE.2013.6563733
  • Filename
    6563733