Juárez Rubén, Bordel Borja
Department of Informatics Systems, Universidad Politécnica de Madrid, 28031 Madrid, Spain.
Sensors (Basel). 2023 Aug 29;23(17):7500. doi: 10.3390/s23177500.
The Vehicular Self-Organizing Network (VANET) is a burgeoning research topic within Intelligent Transportation Systems, holding promise in enhancing safety and convenience for drivers. In general, VANETs require large amounts of data to be shared among vehicles within the network. But then two challenges arise. First, data security, privacy, and reliability need to be ensured. Second, data management and security solutions must be very scalable, because current and future transportation systems are very dense. However, existing Vehicle-to-Vehicle solutions fall short of guaranteeing the veracity of crucial traffic and vehicle safety data and identifying and excluding malicious vehicles. The introduction of blockchain technology in VANETs seeks to address these issues. But blockchain-enabled solutions, such as the Starling system, are too computationally heavy to be scalable enough. Our proposed NeoStarling system focuses on proving a scalable and efficient secure and reliable obstacle mapping using blockchain. An opportunistic mutual authentication protocol, based on hash functions, is only triggered when vehicles travel a certain distance. Lightweight cryptography and an optimized message exchange enable an improved scalability. The evaluation results show that our collaborative approach reduces the frequency of authentications and increases system efficiency by 35%. In addition, scalability is improved by 50% compared to previous mechanisms.
车载自组织网络(VANET)是智能交通系统中一个新兴的研究课题,有望提高驾驶员的安全性和便利性。一般来说,VANET需要在网络内的车辆之间共享大量数据。但随之而来出现了两个挑战。第一,需要确保数据安全、隐私和可靠性。第二,数据管理和安全解决方案必须具有很强的可扩展性,因为当前和未来的交通系统非常密集。然而,现有的车对车解决方案无法保证关键交通和车辆安全数据的准确性,也无法识别和排除恶意车辆。在VANET中引入区块链技术旨在解决这些问题。但是,诸如Starling系统之类的基于区块链的解决方案计算量太大,无法实现足够的可扩展性。我们提出的NeoStarling系统专注于使用区块链证明一种可扩展且高效的安全可靠障碍物映射。一种基于哈希函数的机会性相互认证协议仅在车辆行驶一定距离时触发。轻量级加密和优化的消息交换提高了可扩展性。评估结果表明,我们的协作方法减少了认证频率,系统效率提高了35%。此外,与以前的机制相比,可扩展性提高了50%。