Intelligent Proof-of-Trustworthiness-Based Secure Safety Message Dissemination Scheme for Vehicular Ad Hoc Networks Using Blockchain and Deep Learning Techniques

【Author】 Ghaleb, Fuad A.; Ali, Waleed; Al-Rimy, Bander Ali Saleh; Malebary, Sharaf J.

【Source】MATHEMATICS

【影响因子】2.592

【Abstract】Vehicular ad hoc networks have emerged as the main building block for the future cooperative intelligent transportation system (cITS) to improve road safety and traffic efficiency and to provide passenger comfort. However, vehicular networks are decentralized, characterized by high mobility and dynamicity, and vehicles move in a hostile environment; such characteristics make VANET applications suffer many security and communication issues. Recently, blockchain has been suggested to solve several VANET issues including the dissemination of trustworthy life-threatening information. However, existing dissemination schemes are inefficient for safety messages and are vulnerable to malicious nodes and rely on the majority of honest assumptions. In the VANET context, adversaries may collude to broadcast false information causing serious safety threats. This study proposes an intelligent proof-of-trustworthiness-based secure safety message dissemination scheme (PoTMDS) to efficiently share only trustworthy messages. The consistency and plausibility of the message were evaluated based on a predictive model developed using a convolutional neural network and signal properties such as the received signal strength and angle of arrival. A blockchain-based data dissemination scheme was developed to share critical messages. Each vehicle calculates the proof of trustworthiness of the disseminated messages by comparing the received message with the output of the prediction model. The results showed that the proposed scheme reduced the consensus delay by 58% and improved the detection accuracy by 7.8%. Therefore, the proposed scheme can have an important role in improving the applications of future cITS.

【Keywords】blockchain; consensus; convolutional neural network; Kalman filter; VANET

【发表时间】2023 APR

【收录时间】2023-05-17

【文献类型】理论模型

【主题类别】

区块链应用-实体经济-交通领域