Machine Learning Based Trust Computational Model for IoT Services
Open accessAbstract
The Internet of Things has facilitated access to a large volume of sensitive information on each participating object in an ecosystem. This imposes many threats ranging from the risks of data management to the potential discrimination enabled by data analytics over delicate information such as locations, interests, and activities. To address these issues, the concept of trust is introduced as an important role in supporting both humans and services to overcome the perception of uncertainty and risks before making any decisions. However, establishing trust in a cyber world is a challenging task due to the volume of diversified influential factors from cyber-physical-systems. Hence, it is essential to have an intelligent trust computation model that is capable of generating accurate and intuitive trust values for prospective actors. Therefore, in this paper, a quantifiable trust assessment model is proposed. Built on this model, individual trust attributes are then calculated numerically. Moreover, a novel algorithm based on machine learning principles is devised to classify the extracted trust features and combine them to produce a final trust value to be used for decision making. Finally, our model's effectiveness is verified through a simulation. The results show that our method has advantages over other aggregation methods.
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Gao et al. designed a multidimensional adaptive trust evaluation mechanism using edge computing for weak-computing-power nodes, improving robustness, but the model's flexibility is not high.
“In 2019, Gao Z and others [47] designed a multidimensional adaptive trust evaluation mechanism using edge computing for nodes with weak computing power that widely exist in the network (especially local edge computing networks), which improved node trust.The robustness of the evaluation, but the flexibility of the model, is not high and should be improved in future research.”
From A Survey on Zero Trust Architecture: Challenges and Future Trends (2022) · cites this paper as [47]
The Introduction (page unavailable) describes the paper's own trust algorithm as generalized, able to accept any number of features and apply across use cases without restrictions. That directly conflicts with the claim that this model has low flexibility. The passages do not describe Gao et al. designing an edge-computing mechanism for weak-computing-power nodes. (LLM, conf=0.83)
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Social Internet of Things: Ethical AI Principles in Trust Management
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- Authors
- Jayasinghe, U., Lee, G. M., Um, T.-W., & Shi, Q.
- Affiliations
- Liverpool John Moores University (GB)
- Chosun University (KR)
- Venue
- IEEE Transactions on Sustainable Computing
- Published
- 2019-01-01
- Volume
- 4
- Issue
- 1
- Pages
- 39-52
- ISSN
- 2377-3782 (ISSN-L)
- 2377-3790
- OpenAlex
- W2804907169
- Cited by
- 256
- References
- 49
- Citations by year
- 2026: 19 · 2025: 26 · 2024: 44 · 2023: 45 · 2022: 39 · 2021: 41 · 2020: 29 · 2019: 10 · 2018: 3
- FWCI
- 47.23
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- IEEE Transactions on Sustainable Computing (publishedVersion)
- Liverpool John Moores University (acceptedVersion)
- Funders
- National Research Foundation of Korea
- Ministry of Science and ICT (2018R1A2B2003774)
- Topics
- Social Sciences › Sociology and Political Science › Access Control and Trust
- Physical Sciences › Computer Science › Computer Networks and Communications › IoT and Edge/Fog Computing
- Physical Sciences › Computer Science › Information Systems › Internet of Things and AI
- Keywords
- trust computation, Internet of Things services, trust aggregation, decision-making, cyber-physical systems, privacy risks, data discrimination
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