Using Similarity to Achieve Trust to Enhance Decision Making In Vehicular Safety Applications
Vehicles exchange different types of messages either periodically or as needed for different types of applications. The data in the network of vehicles can be used to extract valuable knowledge to support various applications in vehicular ad hoc networks (VANETs). Knowledge gained from the gathered data can be used to create local views of the network for individual vehicles; for instance, a vehicle can form a view of a subset of the network using neighboring vehicles’ directions of travel, speeds, and the types of applications they run. In the network, vehicles that have common attributes and requirements facilitate the establishment of trust between them as these shared features make up the foundation for trust. Trust relations between vehicles can be utilized for enhancing the performance and reliability of some applications. This dissertation is concerned with trust establishment in VANETs, and how it can be utilized to enhance efficiency and decision making in the network. We provide solutions to this question: How to utilize trust relationship between vehicles to improve decision-making and efficiency in VANET safety applications. In our research, we aim to establish trust relationships through similarity to assist vehicles in identifying false safety messages in the network. We start by designing and implementing a trust management system to generate and process trust values and to establish a set of trusted relationships for vehicles running vehicular safety applications. Next, we explore the possibility of enhancing the decision-making process using trust. First, we develop an analytical model that associates trust with the performance of the decision-making process and the accuracy, and then we study the effectiveness of similarity-based trust in identifying false safety event messages in VANETs. Finally, we show that similarity-based trust has a positive impact on the time needed to make a decision and on the accuracy of that decision.