In order to further enhance the stability of traffic flow, based on the classical Optimal Velocity Changes with Memory (OVCM) model, a novel car-following model for intelligent connected vehicles based on Multiple Headway Optimal Velocity and Acceleration (MHOVA) was proposed. Firstly, the optimal velocity change of k leading cars was introduced with the weight γ, as well as the acceleration of the nearest leading car was considered with the weight ω. Then, the critical stability conditions of traffic flow were obtained based on the proposed model and by the linear stability analysis. Finally, the numerical simulations and analyses were carried out on the parameters such as velocity and headway of the fleet with disturbance by Matlab. Simulation results show that, in the simulation of the starting and stopping processes of the fleet, the proposed model reduces the time to obtain the stable state of the fleet compared to OVCM does, in the simulation of a disturbance to the fleet on the annular road, if both ω and k are of rationality, the proposed model can perform the less fluctuations in terms of velocity and headway, compared with the Full Velocity Difference (FVD) model, OVCM and the Multiple Headway Optimal Velocity (MHOV) model. Especially when ω is 0.3 and k is 5, the minimum upward and downward fluctuations of vehicle velocity can be 0.67% and 0.47% respectively. Consequently, the proposed model can better absorb traffic disturbance and enhance the driving stability of fleet.

Concerning large fluctuation in velocity and trajectory of typical vehicle trajectory planning methods in the Internet of Vehicles (IoV) environment, a new vehicle trajectory planning method based on improved artificial fish swarm algorithm was proposed. Using Delicated Short Range Communications (DSRC) application scenario as a design platform, taking the optimal speed as the core calculation basis, the optimal trajectory of the vehicle was analyzed and achieved. Firstly, the advantages and disadvantages of artificial fish swarm algorithm in the application scene of IoV were analyzd, and an improved artificial fish swarm algorithm was proposed by introducing universal gravitational model and obstacle avoidance mode control. Secondly, the force constraints of the vehicle in the IoV application scenario were analyzd, and the self-organizing behavior control strategy of networked vehicle was used to derive the optimal speed. Finally, real-time trajectory guidance and trajectory obstacle avoidance control planning for the vehicles was realized based on the optimal speed. The simulation results show that after using the trajectory planning model, the driving speed of the vehicle is more stable, the trajectory is less fluctuating, and zero collision can be achieved. In the case of multi-vehicle encounters, when the number of test vehicles is between 2 and 40, compared to the original artificial fish swarm algorithm and firefly algorithm, the iteration number of vehicular trajectory planning method using the improved artificial fish swarm algorithm was reduced,and iteration efficiency increased by 3 to 7 times and 4 to 8 times. The more vehicles, the more obvious the improvement of iteration efficiency.

Improving sync accuracy in Wireless Sensor Network (WSN) usually causes additional sync overhead. To optimize the compatibility of higher sync accuracy and lower sync overhead, a time sync algorithm based on the component decoupling fusion was proposed. With the two-way broadcast sync mechanism and the clock correlations, the clock deviations between synchronized nodes and reference nodes were estimated by the component decoupling fusion. Besides, the computation for the weights of different components was discussed according to the linear unbiased minimum variance estimation. The simulation results show that, without the additional energy consumption, the proposed algorithm can improve its sync accuracy after 20 sync rounds by 4.52μs, 13.8μs and 25.48μs, compared to PBS (Pairwise Broadcast Synchronization), TPSN (Timing-sync Protocol for Sensor Network) and RBS (Reference Broadcast Sync) respectively.