Intelligent monitoring methodology for large-scale logistics transport vehicles based on parallel Internet of Vehicles

Abstract

AbstractThe essence of the Internet of Vehicles is a social and physical information system, including the psychological and organizational factors of human beings. The complexity of the Internet will lead to certain deviations when monitoring vehicles. Therefore, the Parallel Internet of Vehicles is employed to monitor the information on large-scale logistics transport vehicles. This platform is built based on the ACP intelligent approach, which consists of three parts: An artificial system (A), a computational experiment (C), and parallel execution (P). The Adaboost algorithm is used to extract information on large-scale logistics transport vehicles from the ACP parallel Internet of Vehicles, and the Tabu search strategy is applied to optimize the Monte Carlo positioning algorithm. The approximate optimal estimation is obtained by optimizing the filtering to eliminate vehicle positions with fewer possibilities. The weight of important sampling values of the independent vehicle node positions is integrated to complete the posterior probability distribution estimation of the possible positions of vehicles, in order to realize vehicle position monitoring. It is verified that the root-mean square error of the algorithm when positioning a vehicle is less than 0.18, and the monitoring deviation is quite small.