Deep Learning-Based Dynamic Stable Cluster Head Selection in VANET. (12th July 2021)

• Record Type: Journal Article
• Authors: Saleem, Muhammad Asim; Shijie, Zhou; Sarwar, Muhammad Umer; Ahmad, Tanveer; Maqbool, Amarah; Shivachi, Casper Shikali; Tariq, Maham
• Other Names: Zanin Massimiliano Academic Editor.
• Is Part Of: Journal of advanced transportation. Volume 2021(2021)
• Publication Date: 2021-07-12
• Subjects: Transportation -- Periodicals; 388.05
• DOI: 10.1155/2021/9936299
• Languages: English
• ISSNs: 0197-6729

Abstract

Abstract : VANET is the spontaneous evolving creation of a wireless network, and clustering in these networks is a challenging task due to rapidly changing topology and frequent disconnection in networks. The cluster head (CH) stability plays a prominent role in robustness and scalability in the network. The stable CH ensures minimum intra- and intercluster communication, thereby reducing the overhead. These challenges lead the authors to search for a CH selection method based on a weighted amalgamation of four metrics: befit factor, community neighborhood, eccentricity, and trust. The stability of CH depends on the vehicle's speed, distance, velocity, and change in acceleration. These all are included in the befit factor. Also, the accurate location of the vehicle in changing the model is very vital. Thus, the predicted location with the Kalman filter's help is used to evaluate CH stability. The results have shown better performance than the existing state of the art for the befit factor. The change in dynamics and frequent disconnection in communication links due to the vehicle's high speed are inevitable. To comprehend this problem, a graphing approach is used to evaluate the eccentricity and the community neighborhood. The link reliability is calculated using the eigengap heuristic. The last metric is trust; this is one of the concepts that has not been included in the weighted approach to date as per the literature. An adaptive spectrum sensing is designed for evaluating the trust values specifically for the primary users. A deep recurrent learning network, commonly known as long short-term memory (LSTM), is trained for the probability of detection with various signals and noise conditions. The false rate has drastically reduced with the usage of LSTM. The proposed scheme is tested on the real map of Chengdu, southwestern China's Sichuan province, with different vehicular mobilities. The comparative study with the individual and weighted metric has shown significant improvement in the cluster head stability during high vehicular density. Also, there is a considerable increase in network performance in energy, packet delay, packet delay ratio, and throughput.