Soft Computing

, Volume 21, Issue 21, pp 6297–6315 | Cite as

Developing a trust model for pervasive computing based on Apriori association rules learning and Bayesian classification

  • Gianni D’Angelo
  • Salvatore Rampone
  • Francesco Palmieri
Methodologies and Application

Abstract

Pervasive computing is one of the latest and more advanced paradigms currently available in the computers arena. Its ability to provide the distribution of computational services within environments where people live, work or socialize leads to make issues such as privacy, trust and identity more challenging compared to traditional computing environments. In this work, we review these general issues and propose a pervasive computing architecture based on a simple but effective trust model that is better able to cope with them. The proposed architecture combines some artificial intelligence techniques to achieve close resemblance with human-like decision making. Accordingly, Apriori algorithm is first used in order to extract the behavioral patterns adopted from the users during their network interactions. Naïve Bayes classifier is then used for final decision making expressed in term of probability of user trustworthiness. To validate our approach, we applied it to some typical ubiquitous computing scenarios. The obtained results demonstrated the usefulness of such approach and the competitiveness against other existing ones.

Keywords

Pervasive computing Trust model Artificial intelligence Apriori algorithm Naïve Bayes classifier 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  • Gianni D’Angelo
    • 1
  • Salvatore Rampone
    • 1
  • Francesco Palmieri
    • 2
  1. 1.Department of Science and TechnologyUniversity of SannioBeneventoItaly
  2. 2.Department of Computer ScienceUniversity of SalernoFiscianoItaly

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