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.
CITATION STYLE
Sagar, S., Mahmood, A., Sheng, Q. Z., & Siddiqui, S. A. (2020). SCaRT-SIoT: Towards a scalable and robust trust platform for social internet of things: Demo abstract. In SenSys 2020 - Proceedings of the 2020 18th ACM Conference on Embedded Networked Sensor Systems (pp. 635–636). Association for Computing Machinery, Inc. https://doi.org/10.1145/3384419.3430434
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