Name
Abstract | ||
|---|---|---|
The growth of technical revolution towards 5G Next generation networks is expected to meet various communication requirements of future Intelligent Transportation Systems (ITS). Motivated by the consumer needs for variety of ITS applications, bandwidth, high speed and ubiquity, researches are currently exploring different network architectures and techniques, which could be employed in Next generation ITS. To provide flexible network management, control and high resource utilization in Vehicular Ad-hoc Networks (VANETs) on large scale, a new hierarchical 5G Next generation VANET architecture is proposed. The key idea of this holistic architecture is to integrate the centralization and flexibility of Software Defined Networking (SDN) and Cloud-RAN (CRAN), with 5G communication technologies, to effectively allocate resources with a global view. Moreover, a fog computing framework (comprising of zones and clusters) has been proposed at the edge, to avoid frequent handovers between vehicles and RSUs. The transmission delay, throughput and control overhead on controller are analyzed and compared with other architectures. Simulation results indicate reduced transmission delay and minimized control overhead on controllers. Moreover, the throughput of proposed system is also improved. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/CCNC.2018.8319192 | 2018 15th IEEE Annual Consumer Communications & Networking Conference (CCNC) |
Keywords | Field | DocType |
Next generation VANETs,Software Defined VANETs,Fog computing,Edge Computing,5G VANET architecture | Edge computing,Computer science,Transmission delay,Computer network,Network architecture,Throughput,Intelligent transportation system,Network management,Software-defined networking,Vehicular ad hoc network,Distributed computing | Conference |
ISSN | ISBN | Citations |
2331-9852 | 978-1-5386-4791-2 | 5 |
PageRank | References | Authors |
0.43 | 20 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ammara Anjum Khan | 1 | 8 | 1.24 |
| Mehran Abolhasan | 2 | 451 | 48.95 |
| Wei Ni | 3 | 474 | 70.16 |