Research Article
Performance Analysis of Vehicular Adhoc Network Using Different Highway Traffic Scenarios in Cloud Computing
@INPROCEEDINGS{10.1007/978-3-319-51207-5_15, author={Nigar Fida and Fazlullah Khan and Mian Jan and Zahid Khan}, title={Performance Analysis of Vehicular Adhoc Network Using Different Highway Traffic Scenarios in Cloud Computing}, proceedings={Future Intelligent Vehicular Technologies. First International Conference, Future 5V 2016, Porto, Portugal, September 15, 2016, Revised Selected Papers}, proceedings_a={FUTURE 5V}, year={2017}, month={1}, keywords={Cloud computing Network simulator Vehicle density VANET Performance analysis Throughput Packet loss End-to-end delay}, doi={10.1007/978-3-319-51207-5_15} }- Nigar Fida
Fazlullah Khan
Mian Jan
Zahid Khan
Year: 2017
Performance Analysis of Vehicular Adhoc Network Using Different Highway Traffic Scenarios in Cloud Computing
FUTURE 5V
Springer
DOI: 10.1007/978-3-319-51207-5_15
Abstract
Vehicular Ad-hoc Networks (VANETs) combine intelligent vehicles on highways aim to solve many transportation problems. The performance of VANETs is affected by many parameters due to highly dynamic structure. We assessed the performance of VANETs over different highway’s scenarios and investigated that under which circumstances the performance will be better and vice versa. We adopted our experiments in infrastructure environment, where the road side units (RSUs) are connected with cloud server. The RSU periodically gathers spatial-temporal information and upload it to cloud, which could help the drivers to predict the status of road before journey. The experiments carried on two types of highway’s scenarios: varying vehicles densities and simulation time. The simulation result shows that selected performance metrics (throughput, E2E delay and packet loss) greatly affect in both scenarios. The simulation time within the interval 200 to 500 is an optimal choice during simulation experiments. The throughput and packet loss increases with increase in vehicle density. The end-to-end delay has an inverse relation with vehicle density. The highway scenarios are generated by SUMO and the actual simulation is done by NS2.