School of Computer Science and Communication Engineering, Jiangsu University, Zhenjiang 212013, China.
Faculty of Engineering Science, Technology, and Management, Ziauddin University, Karachi 74600, Pakistan.
Sensors (Basel). 2020 Jul 16;20(14):3945. doi: 10.3390/s20143945.
The Internet of Things (IoT) is a world of connected networks and modern technology devices, among them vehicular networks considered more challenging due to high speed and network dynamics. Future trends in IoT allow these inter networks to share information. Also, the previous security solutions to vehicular IoT (VIoT) much emphasize on privacy protection and security related issues using public keys infrastructure. However, the primary concern about efficient trust assessment, authorized users malfunctioning, and secure information dissemination in vehicular wireless networks have not been explored. To cope with these challenges, we propose a trust enhanced on-demand routing (TER) scheme, which adopts TrustWalker (TW) algorithm for efficient trust assessment and route search technique in VIoT. TER comprised of novel three-valued subjective logic (3VSL), TW algorithm, and ad hoc on-demand distance vector (AODV) routing protocol. The simulated results validate the accuracy of the proposed scheme in term of throughput, packet drop ratio (PDR), and end to end (E2E) delay. In the simulation, the execution time of the TW algorithm is analyzed and compared with another route search algorithm, i.e., Assess-Trust (AT), by considering real-world online datasets such as Pretty Good Privacy and Advogato. The accuracy and efficiency of the TW algorithm, even with a large number of vehicle users, are also demonstrated through simulations.
物联网(IoT)是一个由联网网络和现代技术设备组成的世界,其中车联网由于高速和网络动态性而被认为更具挑战性。物联网的未来趋势允许这些网络之间共享信息。此外,以前针对车联网(VIoT)的安全解决方案主要侧重于使用公钥基础设施保护隐私和解决安全相关问题。然而,在车联网中,关于有效信任评估、授权用户故障和安全信息传播的主要关注点尚未得到探索。为了应对这些挑战,我们提出了一种基于信任增强的按需路由(TER)方案,该方案在 VIoT 中采用了 TrustWalker(TW)算法进行有效的信任评估和路由搜索技术。TER 由新颖的三值主观逻辑(3VSL)、TW 算法和按需距离矢量路由协议(AODV)组成。仿真结果验证了所提出方案在吞吐量、分组丢失率(PDR)和端到端(E2E)延迟方面的准确性。在仿真中,分析了 TW 算法的执行时间,并通过考虑 Pretty Good Privacy 和 Advogato 等真实在线数据集,将其与另一种路由搜索算法即 Assess-Trust(AT)进行了比较。通过仿真还证明了 TW 算法的准确性和效率,即使在大量车辆用户的情况下也是如此。