Towards a machine learning-driven trust evaluation model for social internet of things: a time-aware approach

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contributionpeer-review

Abstract

The emerging paradigm of the Social Internet of Things (SIoT) has transformed the traditional notion of the Internet of Things (IoT) into a social network of billions of interconnected smart objects by integrating social networking facets into the same. In SIoT, objects can establish social relationships in an autonomous manner and interact with the other objects in the network based on their social behaviour. A fundamental problem that needs attention is establishing of these relationships in a reliable and trusted way, i.e., establishing trustworthy relationships and building trust amongst objects. In addition, it is also indispensable to ascertain and predict an object's behaviour in the SIoT network over a period of time. Accordingly, in this paper, we have proposed an efficient time-aware machine learning-driven trust evaluation model to address this particular issue. The envisaged model deliberates social relationships in terms of friendship and community-interest, and further takes into consideration the working relationships and cooperativeness (object-object interactions) as trust parameters to quantify the trustworthiness of an object. Subsequently, in contrast to the traditional weighted sum heuristics, a machine learning-driven aggregation scheme is delineated to synthesize these trust parameters to ascertain a single trust score. The experimental results demonstrate that the proposed model can efficiently segregates the trustworthy and untrustworthy objects within a network, and further provides the insight on how the trust of an object varies with time along with depicting the effect of each trust parameter on a trust score.

Original languageEnglish
Title of host publicationProceedings of the 17th EAI International Conference on Mobile and Ubiquitous Systems
Subtitle of host publicationComputing, Networking and Services, MobiQuitous 2020
Place of PublicationNew York, NY
PublisherAssociation for Computing Machinery
Pages283-290
Number of pages8
ISBN (Electronic)9781450388405
DOIs
Publication statusPublished - 2021
Event17th EAI International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services, MobiQuitous 2020 - Virtual, Online, Darmstadt, Germany
Duration: 7 Dec 20209 Dec 2020

Publication series

NamePervasiveHealth: Pervasive Computing Technologies for Healthcare
ISSN (Print)2153-1633

Conference

Conference17th EAI International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services, MobiQuitous 2020
CountryGermany
CityDarmstadt
Period7/12/209/12/20

Keywords

  • Community-of-Interest
  • Cooperativeness
  • Friendship
  • Machine Learning
  • Social Internet of Things
  • Social Similarity
  • Trustworthiness Management

Fingerprint

Dive into the research topics of 'Towards a machine learning-driven trust evaluation model for social internet of things: a time-aware approach'. Together they form a unique fingerprint.

Cite this