Trust-SIoT: Toward Trustworthy Object Classification in the Social Internet of Things

被引:6
|
作者
Sagar, Subhash [1 ,2 ]
Mahmood, Adnan [1 ]
Wang, Kai [3 ]
Sheng, Quan Z. [1 ]
Pabani, Jitander Kumar [4 ]
Zhang, Wei Emma [5 ]
机构
[1] Macquarie Univ, Sch Comp, Sydney, NSW 2109, Australia
[2] Deakin Univ, Sch Informat Technol, Melbourne, Vic 3125, Australia
[3] Nanyang Technol Univ, Sch Comp Sci & Engn, Singapore, Singapore
[4] Univ Malaga, Sch Telecommun Engn, Malaga 29016, Spain
[5] Univ Adelaide, Sch Comp Sci, Adelaide, SA 5005, Australia
基金
澳大利亚研究理事会;
关键词
Trust management; social Internet of Things; knowledge graph embedding; social relationships; reliability; benevolence; MODEL; MANAGEMENT;
D O I
10.1109/TNSM.2023.3247831
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
The recent emergence of the promising paradigm of the Social Internet of Things (SIoT) is a result of an intelligent amalgamation of the social networking concepts with the Internet of Things (IoT) objects (also referred to as "things") in an attempt to unravel the challenges of network discovery, navigability, and service composition. This is realized by facilitating the IoT objects to socialize with one another, i.e., similar to the social interactions amongst human beings. A fundamental issue that mandates careful attention is to thus establish, and over time, maintain trustworthy relationships amongst these IoT objects. Therefore, a trust framework for SIoT must include object-object interactions, the aspects of social relationships, credible recommendations, etc., however, the existing literature has only focused on some aspects of trust by primarily relying on the conventional approaches that govern linear relationships between input and output. In this paper, an artificial neural network-based trust framework, Trust-SIoT, has been envisaged for identifying the complex non-linear relationships between input and output in a bid to classify trustworthy objects. Moreover, Trust-SIoT has been designed for capturing a number of key trust metrics as input, i.e., direct trust by integrating both current and past interactions, reliability and benevolence of an object, credible recommendations, and the degree of relationship by employing knowledge graph embedding. Finally, we have performed extensive experiments to evaluate the performance of Trust-SIoT vis-a-vis state-of-the-art heuristics on two real-world datasets. The results demonstrate that Trust-SIoT achieves a higher F1-score and lower MAE and MSE scores.
引用
收藏
页码:1210 / 1223
页数:14
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