Vehicles often communicate among different networks in Internet of Vehicles (IoVs). However, existing unstable network statuses and different user preferences result in vehicle frequent vertical handoffs (VHOs). In this paper, we propose a novel VHO method based on a self-selection decision tree for IoVs. We first establish the respective handoff probability distribution of vehicles according to network attributes and movement trend. Then, based on handoff probability distributions and defined user preferences, we propose a novel handoff method by the selfselection decision tree for IoVs. Finally, we also present a feedback decision method according to the feedback of vehicle handoff, to improve next handoff quality when vehicle movement trend and vehicle service status change. Simulation results show that the proposed method not only supports the VHO among Wireless Access in Vehicular Environments, Worldwide Interoperability for Microwave Access, and third-generation cellular but also reduces switching times and ensures the network update rate and the vehicles' service quality. Index Terms-Decision tree, feedback decision, Internet of Vehicles (IoVs), self-selection, vertical handoff (VHO). 1932-8184

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Vehicular ad hoc networks (VANETs) have a high degree of openness. Therefore, if a new vehicle node wants to access the network, we need to validate the vehicle node carefully to ensure the security of the entire networking. There are a large number of vehicle nodes, and the strange degree among the nodes is very high in VANETs. In addition, VANETs are human-oriented networks. All vehicle nodes in the VANET have the right to decide whether to accept a new node. To attack this challenge, this paper proposes a security authentication method based on trust evaluation. The security authentication method consists of two parts: secure authentication based on direct trust evaluation and secure authentication based on indirect trust evaluation. In the direct trust evaluation, the security vector model is established based on the security behaviors of the new vehicle node. The historical security evaluation from the authority units (AU) is collected to calculate the final direct trust. In the indirect trust evaluation, the trust degree is calculated based on the recommendation trust vectors from the vehicle nodes in the network. The method employs correlation coefficient for distinguishing the malicious vehicle. Then, the recommendations from the malicious vehicle nodes are removed. The final recommended trust is gained by calculating the average recommendation trusts of remained vehicle nodes. Simulation results show the advantage of our proposed method.

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This work investigates shear fracture behavior occurring in a titanium alloy cylinder internally filled with high explosives through the use of photonic Doppler velocimetry(PDV) array, high-speed framing camera and soft capture tank. The real-time velocity profiles diagnosed by PDV array display from overlapping to scattering, corresponding to the cylinder from uniform expansion to onset of fracture. In addition to the general findings obtained from individual diagnostics, combined analysis from the experimental measurements determines sliding velocity between sheared cracks and the overall adiabatic shear failure process of the metal cylinder is discussed.

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