Biblio

In this paper, an optimization model of automobile supply chain network with risks under fuzzy price is put forward. The supply chain network is composed of component suppliers, plants, and distribution centers. The total costs of automobile supply chain consist of variable costs, fixed costs, and transportation costs. The objective of this study is to minimize the risks of total profits. In order to deal with this model, this paper puts forward an approximation method to transform a continuous fuzzy problem into discrete fuzzy problem. The model is solved using Cplex 12.6. The results show that Cplex 12.6 can perfectly solve this model, the expected value and lower semi-variance of total profits converge with the increasing number of discretization points, the structure of automobile supply chain network keeps unchanged with the increasing number of discretization points.

Controller Area Network (CAN) is the main bus network that connects electronic control units in automobiles. Although CAN protocols have been revised to improve the vehicle safety, the security weaknesses of CAN have not been fully addressed. Security threats on automobiles might be from external wireless communication or from internal malicious CAN nodes mounted on the CAN bus. Despite of various threat sources, the security weakness of CAN is the root of security problems. Due to the limited computation power and storage capacity on each CAN node, there is a lack of hardware-efficient protection methods for the CAN system without losing the compatibility to CAN protocols. To save the cost and maintain the compatibility, we propose to exploit the built-in CAN fault confinement mechanism to detect the masquerade attacks originated from the malicious CAN devices on the CAN bus. Simulation results show that our method achieves the attack misdetection rate at the order of 10-5 and reduces the encryption latency by up to 68% over the complete frame encryption method.

Communicating vehicles will change road traffic as we know it. With current versions of European and US standards in mind, the authors discuss privacy and traffic surveillance issues in vehicular network technology and outline research directions that could address these issues.