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A computational Model for Accessibility in Smart Cities

Published: 17 May 2016 Publication History

Abstract

This article presents the MASC, which is a computational model for accessibility in smart cities. The use of ubiquitous computing in the area of accessibility provides solutions to support persons with disabilities (PwD). Unlike the proposed approaches, the MASC uses the interactions of PwD to compose trails which will be offered as a service. Moreover supports various disabilities, is intended for mass applications. A prototype was developed to evaluate performance and functionality. This evaluation was conducted with data generated by a context simulator in Sao Leopoldo - RS. The results presented in the tests indicate that the services offered by the model can be applied in smart cities to collaborate with accessibility, helping PwD, health professionals and public administration.

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Published In

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SBSI '16: Proceedings of the XII Brazilian Symposium on Information Systems on Brazilian Symposium on Information Systems: Information Systems in the Cloud Computing Era - Volume 1
May 2016
615 pages
ISBN:9788576693178
  • General Chairs:
  • Frank Siqueira,
  • Patricia Vilain,
  • Program Chairs:
  • Claudia Cappelli,
  • Raul Sidnei Wazlawick

Sponsors

  • FAPESC: Santa Catarina State Research and Innovation Support Foundation
  • FAPEU: Foundation for the Support of University Research and Outreach
  • CAPES: Brazilian Higher Education Funding Council
  • CNPq: National Council for Technological and Scientific Development

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Brazilian Computer Society

Porto Alegre, Brazil

Publication History

Published: 17 May 2016

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Author Tags

  1. Ubiquitous accessibility
  2. city accessible
  3. model to support
  4. persons with disabilities

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  • Research-article

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SBSI '16
Sponsor:
  • FAPESC
  • FAPEU
  • CAPES
  • CNPq
SBSI '16: Brazilian Symposium on Information Systems
May 17 - 20, 2016
Santa Catarina, Florianopolis, Brazil

Acceptance Rates

SBSI '16 Paper Acceptance Rate 80 of 244 submissions, 33%;
Overall Acceptance Rate 181 of 557 submissions, 32%

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