In vehicular networks, vehicles communicate with each other and transfer messages to enhance the safety of both commuters and vehicles. Trust management in these networks has become a key issue because of the selfish behavior that is frequently exhibited by some vehicles. Most of the existing trust management schemes for vehicular networks schemes have adopted cryptographic techniques, which require high computation time and high network resource utilization. We propose a Hierarchical Trust Management System (HTMS) that relies on the trust metric computation for every individual vehicle. We also propose a systematic approach based on trust evaluation, trust propagation and trust aggregation to compute the trustworthiness of vehicles. Initially, the trust evaluation scheme computes a local trust score based on a behavioral analysis. Next, the proposed trust propagation model shares the computed local trust score to neighboring vehicles using a platoon-based dissemination technique. Based on the local trust score and propagated trust opinions, our aggregation technique computes a Global Trust score (GTS) using Dempster-Shafer theory. Finally, based on the computed GTS score, the proposed system invokes an action module to reward or punish vehicles. The simulation results obtained show that the proposed system is highly resilient to trust-based attacks and enables trustworthy peers to generate a higher number of data transfers within the network.
|State||Published - Aug 2020|
Bibliographical noteFunding Information:
This research was supported by the Roadway, Transportation, and Traffic Safety Research Center (RTTSRC) of the United Arab Emirates University (grant number 31R116 ). We thank the anonymous reviewers for their valuable comments and suggestions which helped us improve the quality, content, and presentation of this paper.
© 2020 Elsevier Inc.
- Intelligent transportation systems
- Trust management
- Vehicular networks
ASJC Scopus subject areas
- Automotive Engineering
- Electrical and Electronic Engineering