دانلود رایگان مقاله انگلیسی یکپارچه سازی توزیع محتوا مبتنی بر خوشه بندی LTE و IEEE 802.11p با منطق فازی و Q-learning - نشریه IEEE 2018

Abstract

There is an increasing demand for distributing a large amount of content to vehicles on the road. However, the cellular network is not sufficient due to its limited bandwidth in a dense vehicle environment. In recent years, vehicular ad hoc networks (VANETs) have been attracting great interests for improving communications between vehicles using infrastructure-less wireless technologies. In this paper, we discuss integrating LTE (Long Term Evolution) with IEEE 802.11p for the content distribution in VANETs. We propose a twolevel clustering approach where cluster head nodes in the first level try to reduce the MAC layer contentions for vehicle-tovehicle (V2V) communications, and cluster head nodes in the second level are responsible for providing a gateway functionality between V2V and LTE. A fuzzy logic-based algorithm is employed in the first-level clustering, and a Q-learning algorithm is used in the second-level clustering to tune the number of gateway nodes. We conduct extensive simulations to evaluate the performance of the proposed protocol under various network conditions. Simulation results show that the proposed protocol can achieve 23% throughput improvement in highdensity scenarios compared to the existing approaches.

V. Conclusions

We have proposed a novel protocol for content distribution in hybrid LTE and IEEE 802.11p vehicular networks. The protocol employs a two-level clustering approach where the first-level clustering is used to solve the MAC layer contention problem of IEEE 802.11p-based V2V communications in a high-density vehicular environment, and the second-level clustering is responsible for selecting gateway nodes which bridge V2V and LTE. We used a fuzzy logic algorithm in the first-level clustering to generate a stable cluster head nodes by taking into account vehicle velocity, vehicle distribution and link quality between vehicles. We further employed a Q-learning algorithm in the second-level clustering to tune the number of gateway nodes in order to achieve high overall network performance under various network conditions. Through computer simulations, we have confirmed that the proposed protocol can provide a better performance than the existing baselines in various scenarios, achieving 23% throughput improvement in high-density scenarios.