As an emerging application scenario of wireless technologies, vehicular communications have been initiated not only for enhancing the transportation safety and driving experiences, but also for a new commercial market of on-board Internet services. Due to extraordinarily high mobility of vehicles in a vehicular network, frequent handover requests will be a norm, which initiates the demand for an effective and fast authentication scheme that can maintain the service continuity in presence of the frequent handover events. However, previously reported authentication schemes, although with minimized handover latency and packet loss rate, may disclose the location information of the mobile user to the third party, which will seriously violate the location privacy of the user. In this paper, we propose a location privacy preserving authentication scheme based on blind signature in the elliptic curve domain. The scheme cannot only provide fast authentication, but also guarantee the security and location anonymity to the public. To analyze the proposed scheme, a theoretical traceability analysis is conducted, which shows that the probability of tracing an vehicle's route is negligibly small. We will also examine the authentication speed of the scheme, and show that the scheme can satisfy seamless handover for fast moving vehicles.

Reliability prediction of spinning machines can result in a time-saving and cost-saving development process with high reliability. Based on an analysis of failure times among systems and subsystems, a simulation method for reliability prediction of spinning machines is proposed by using the Monte Carlo simulation model. Firstly, factor weights are determined according to the fuzzy scoring and analytic hierarchy process. According to the status of reliability growth, growth coefficients are proposed based on reliability influencing factor weights and fuzzy scoring. To achieve the prediction of reliability distribution law, reliability index, and fault frequency, the reliability prediction model is constituted by combining the reliability growth coefficient and the Monte Carlo simulation model. Simulation results for spinning machines are obtained via the model thus built, which are confirmed with a practical example.