Towards Resilient IoV Networks — A Trust Management Perspective
The emerging and promising paradigm of the Internet of Vehicles (IoV), also referred to as Internet of Things-on-Wheels, has evolved from the notion of Vehicular Ad hoc Networks and is an indispensable constituent of the modern Intelligent Transportation Systems (ITS). Accordingly, over the past dec...
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| Format: | Thesis |
| Language: | en en |
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Universiti Malaysia Sarawak
2026
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| Online Access: | http://ir.unimas.my/id/eprint/51864/5/Wang%20Yingxun_PhD%20Thesis.IR.pdf http://ir.unimas.my/id/eprint/51864/6/Wang%20Yingxun_PhD%20Thesis_24pages.pdf http://ir.unimas.my/id/eprint/51864/ |
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| Summary: | The emerging and promising paradigm of the Internet of Vehicles (IoV), also referred to as Internet of Things-on-Wheels, has evolved from the notion of Vehicular Ad hoc Networks and is an indispensable constituent of the modern Intelligent Transportation Systems (ITS). Accordingly, over the past decade or so, researchers from academia and industry have investigated and carefully developed architectures, salient characteristics, and applications pertinent to IoV, however, its security, particularly internal security, remains a considerable concern. This PhD dissertation is, therefore, an effort to tackle the internal security of such a highly dynamic and distributed network from the eyes of trust. In light of same, a state-of-the-art machine learning-based trust management mechanism which aggregates direct trust, indirect trust, and context to ascertain the trustworthiness of vehicles has been envisaged for segregating between trustworthy and untrustworthy vehicles via an optimal decision boundary. Extensive evaluations demonstrate that the said mechanism outperforms the other state-of-the-art trust management mechanisms. Moreover, a time-aware IoV-based trust management mechanism has been proposed to investigate the behavior of vehicles for ascertaining various trust-based attacks, i.e., zig-zag attacks, self-promoting attacks, on-off attacks, and opportunistic attacks. Experimental results suggest that the proposed mechanism can ascertain the impact of multiple trust-based attacks instigated by the malicious vehicles across the entire time span of an IoV network in an intelligent manner. Furthermore, a first-of-its-kind dedicated IoV-based trust dataset has been introduced which encompasses 96,707 interactions from 79 vehicles across different time instances. The said IoV dataset comprises nine salient trust parameters which play an indispensable role for quantifying the trust of vehicles. Together, these contributions enhance the resilience and trustworthiness of IoV networks for next-generation ITS. |
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