Mobility models performance analysis using random Dijkstra algorithm and doppler effect for IEEE 1609.4 standard

Taking into account some issue such as the high mobility and change trajectory, one of the most challenging issues in IEEE 1609.4 are the assurance of Quality of Service (QoS), i.e. to improve throughput and reduce delay for IEEE 1609.4 standard. Mobility models represent real world scenarios and evaluate shortest path performance using random Dijkstra algorithm for IEEE 1609.4 standard. We evaluate the performance mobility model for IEEE 1609.4 standard, in terms of throughput, queuing delay, and number of delivered packets. The mobility models observed this work are Manhattan Mobility Model, STRAW Mobility Model, Traffic Sign Model and Intelligent Driver Management Model (IDM_IM). The mobility models is also evaluated performance using random Dijkstra algorithm. We also evaluates the doppler effect performance in the mobility model for IEEE 1609.4 standard. We use VanetMobiSim and ns 2.34 simulator for the evaluation. From the simulation, it was found that IDM_IM mobility model has the worst delay. We can analyze IDM_IM using spatial map randomly to generate traffic. On the other hand, STRAW mobility model has the highest throughput among others. Subsequently, based on the doppler effect evaluates the traffic sign and IDM_IM mobility models have fluctuated performance compared with Manhattan and STRAW mobility models. We also evaluate performance service differentiation impact of different mobility models for IEEE 1609.4 standard. Data service (background traffic) has the worst performance compared with other services different mobility models using Dijkstra’s algorithm.