원문정보
초록
영어
Differing from the physical connectivity of the topology structure, the logical connectivity of VANET considers both the interior network configuration and the external communication environment. Hence, the traditional mathematical analysis and modeling methods which are usually used in physical connectivity research are no longer suitable for the logical connectivity prediction. Taking the AODV protocol as an example, this paper simulates the effects of different road traffic parameters on logical connectivity probability and selects three main effect factors, roadway length, vehicle number and vehicle speed. Furthermore, the inner relation between the logical connectivity and the three road traffic parameters is studied based on data mining technique and then two logical connectivity prediction models are presented, the nonlinear regression-based model and the extreme learning machine-based model. Simulation results show that the two models are both with high accuracy in predicting the network logical connectivity under different road traffic environments.
목차
1. Introduction
2. Related Work
3. Simulation Data Acquisition and Effect Factors Analysis
3.1. Simulation Data Acquisition
3.2. Effects of Vehicle Density on Logical Connectivity Probability
3.3. Effects of Lane Number on Logical Connectivity Probability
3.4. Effects of Roadway Length, Vehicle Number and Vehicle Speed on Logical Connectivity Probability
3. Logical Connectivity Prediction Models based on Nonlinear Regression and ELM
3.1. Connectivity Prediction Model based on Nonlinear Regression
3.2. Connectivity Prediction Model based on ELM
4. Simulations and Results
5. Conclusions and Future Work
References