Improved Hybrid Model in Vehicular Clouds based on Data Types (IHVCDT)

In Vehicular Cloud (VC), vehicles collect data from the surrounding environment and exchange this data among the vehicles and the cloud centers. To do that in an efficient way first we need to organize the vehicles into clusters, each one works as a VC, and every cluster is managed by the cluster head (broker). The vehicles are grouped in clusters with adaptive size based on their mobility and capabilities. This model is an approach that forms the clusters based on the vehicles capabilities and handles with different types of data according to its importance to select the best route. A hybrid model is proposed to deal with these differences; Long-Term Evolution (LTE) is used with IEEE 802.11P which forms the traditional wireless access for Vehicular Ad hoc Networks (VANETs). This merge gives the high data delivery, wide-range transmission, and low latency. However, using only LTE based VANET is not practical due to its high cost and the large number of occurrences in the base stations. In this paper, a new Vehicular Cloud (VC) model is proposed which provides data as a service based on Vehicular Cloud Computing (VCC). A new method is proposed for high data dissemination based on the data types. The model is classified into three modes: the urgent mode, the bulk mode, and the normal mode. In the urgent mode, Long-Term Evolution (LTE) is used to achieve a high delivery with minimum delay. In the bulk mode, the vehicle uses IEEE 802.11p and chooses two clusters to divide this huge data. In the normal mode, the model works as D-hops cluster based algorithm.

@article{Khawatreh2017,
  title     = {Improved Hybrid Model in Vehicular Clouds based on Data Types (IHVCDT)},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {8},
  number    = {8},
  year      = {2017},
  publisher = {The Science and Information Organization},
  author    = {Saleh A. Khawatreh and Enas N. Al-Zubi},
  doi       = {10.14569/IJACSA.2017.080815},
  url       = {https://doi.org/10.14569/IJACSA.2017.080815}
}