Deep Learning-Based Dynamic Stable Cluster Head Selection in VANET
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 s...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2021-01-01
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| Series: | Journal of Advanced Transportation |
| Online Access: | http://dx.doi.org/10.1155/2021/9936299 |
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| author | Muhammad Asim Saleem Zhou Shijie Muhammad Umer Sarwar Tanveer Ahmad Amarah Maqbool Casper Shikali Shivachi Maham Tariq |
| author_facet | Muhammad Asim Saleem Zhou Shijie Muhammad Umer Sarwar Tanveer Ahmad Amarah Maqbool Casper Shikali Shivachi Maham Tariq |
| author_sort | Muhammad Asim Saleem |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-c719d234b87f48cb9256e3cdbcfadc70 |
| institution | Kabale University |
| issn | 0197-6729 2042-3195 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Advanced Transportation |
| spelling | doaj-art-c719d234b87f48cb9256e3cdbcfadc702025-02-03T06:05:33ZengWileyJournal of Advanced Transportation0197-67292042-31952021-01-01202110.1155/2021/99362999936299Deep Learning-Based Dynamic Stable Cluster Head Selection in VANETMuhammad Asim Saleem0Zhou Shijie1Muhammad Umer Sarwar2Tanveer Ahmad3Amarah Maqbool4Casper Shikali Shivachi5Maham Tariq6School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu, ChinaSchool of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu, ChinaDepartment of Computer Science, Government College University, Faisalabad, PakistanDepartment of Electrical and Electronic Engineering, Auckland University of Technology, Auckland 1010, New ZealandDepartment of Computer Science, Government College Women University, Faisalabad, PakistanSouth Eastern Kenya University, Kitui, KenyaDepartment of Computer Science, Government College Women University, Faisalabad, PakistanVANET 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.http://dx.doi.org/10.1155/2021/9936299 |
| spellingShingle | Muhammad Asim Saleem Zhou Shijie Muhammad Umer Sarwar Tanveer Ahmad Amarah Maqbool Casper Shikali Shivachi Maham Tariq Deep Learning-Based Dynamic Stable Cluster Head Selection in VANET Journal of Advanced Transportation |
| title | Deep Learning-Based Dynamic Stable Cluster Head Selection in VANET |
| title_full | Deep Learning-Based Dynamic Stable Cluster Head Selection in VANET |
| title_fullStr | Deep Learning-Based Dynamic Stable Cluster Head Selection in VANET |
| title_full_unstemmed | Deep Learning-Based Dynamic Stable Cluster Head Selection in VANET |
| title_short | Deep Learning-Based Dynamic Stable Cluster Head Selection in VANET |
| title_sort | deep learning based dynamic stable cluster head selection in vanet |
| url | http://dx.doi.org/10.1155/2021/9936299 |
| work_keys_str_mv | AT muhammadasimsaleem deeplearningbaseddynamicstableclusterheadselectioninvanet AT zhoushijie deeplearningbaseddynamicstableclusterheadselectioninvanet AT muhammadumersarwar deeplearningbaseddynamicstableclusterheadselectioninvanet AT tanveerahmad deeplearningbaseddynamicstableclusterheadselectioninvanet AT amarahmaqbool deeplearningbaseddynamicstableclusterheadselectioninvanet AT caspershikalishivachi deeplearningbaseddynamicstableclusterheadselectioninvanet AT mahamtariq deeplearningbaseddynamicstableclusterheadselectioninvanet |