The consistency test on the cosmic evolution

We propose a new and robust method to test the consistency of the cosmic evolution given by a cosmological model. It is realized by comparing the combined quantity r_d^CMB/D_V^SN, which is derived from the comoving sound horizon r_d from cosmic microwave background (CMB) measurements and the effective distance D_V derived from low-redshift Type-Ia supernovae (SNe Ia) data, with direct and independent r_d/D_V obtained by baryon acoustic oscillation (BAO) measurements at median redshifts. We apply this test method for the Lambda-CDM and wCDM models, and investigate the consistency of the derived value of r_d/D_V from Planck 2015 and the SN Ia data sets of Union2.1 and JLA (z<1.5), and the r_d/D_V directly given by BAO data from six-degree-field galaxy survey (6dFGS), Sloan Digital Sky Survey Data Release 7 Main Galaxy Survey (SDSS-DR7 MGS), DR11 of SDSS-III, WiggleZ and Ly-alpha forecast surveys from Baryon Oscillation Spectroscopic Data (BOSS) DR-11 over 0.1<z<2.36. We find that r_d^CMB/D_V^SN for both non-flat Lambda-CDM and flat wCDM models with Union2.1 and JLA data are well consistent with the BAO and CMB measurements within 1-sigma CL. Future surveys will further tight up the constraints significantly, and provide stronger test on the consistency.Comment: 11 pages, 5 figures, 4 tables. Version accepted by PR

Federated Learning-based Vehicle Trajectory Prediction against Cyberattacks

With the development of the Internet of Vehicles (IoV), vehicle wireless communication poses serious cybersecurity challenges. Faulty information, such as fake vehicle positions and speeds sent by surrounding vehicles, could cause vehicle collisions, traffic jams, and even casualties. Additionally, private vehicle data leakages, such as vehicle trajectory and user account information, may damage user property and security. Therefore, achieving a cyberattack-defense scheme in the IoV system with faulty data saturation is necessary. This paper proposes a Federated Learning-based Vehicle Trajectory Prediction Algorithm against Cyberattacks (FL-TP) to address the above problems. The FL-TP is intensively trained and tested using a publicly available Vehicular Reference Misbehavior (VeReMi) dataset with five types of cyberattacks: constant, constant offset, random, random offset, and eventual stop. The results show that the proposed FL-TP algorithm can improve cyberattack detection and trajectory prediction by up to 6.99% and 54.86%, respectively, under the maximum cyberattack permeability scenarios compared with benchmark methods