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SCaRT-SIoT: towards a scalable and robust trust platform for social internet of things: demo abstract

Published:16 November 2020Publication History

ABSTRACT

Recently, the convergence of the paradigm of the Internet of Things (IoT) with social networking has been widely recognized as an emerging interdisciplinary area, lately, referred to as the Social Internet of Things (SIoT). SIoT enables the object-to-object interaction and provides the platform for these objects to autonomously socialize with the other objects in the network in a bid to overcome the key challenges of IoT, i.e., data discovery and composition, network navigability, trust management, etc. Trust plays a significant role while establishing these inter-object social relationships and it is also essential to observe the trustworthiness of an object before relying on information provided by them. A number of trust evaluation models for SIoT have been proposed in the literature, nevertheless, most of these models suffer in validating and testing their respective models due to lack of appropriate datasets. To address this issue, this paper proposes a scalable plug and play trust platform referred to as SCalable and Robust Trust platform for SIoT (SCaRT-SIoT) to provide the dataset to test and analyze various SIoT-based trust models of the research community.

References

  1. Quan Z. Sheng, Yongrui Qin, Lina Yao, and Boualem Benatallah. Managing the web of things: Linking the real world to the web. Elsevier, 02 2017.Google ScholarGoogle Scholar
  2. Roopa M.S., Santosh Pattar, Rajkumar Buyya, Venugopal K.R., S.S. Iyengar, and L.M. Patnaik. Social internet of things (siot): Foundations, thrust areas, systematic review and future directions. Computer Communications, 139:32 -- 57, 2019.Google ScholarGoogle Scholar
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    • Published in

      cover image ACM Conferences
      SenSys '20: Proceedings of the 18th Conference on Embedded Networked Sensor Systems
      November 2020
      852 pages
      ISBN:9781450375900
      DOI:10.1145/3384419

      Copyright © 2020 ACM

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      Association for Computing Machinery

      New York, NY, United States

      Publication History

      • Published: 16 November 2020

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      Overall Acceptance Rate174of867submissions,20%

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