EtHgSC: Eigen Trick-Based Hypergraph Stable Clustering Algorithm in VANET
A smart city’s vehicular communication strategy is important. A significant problem with vehicular communication is scalability. Clustering can help with vehicular ad hoc network (VANET) problems; however, clustering in VANET faces stability problems because of the rapid mobility of the vehicles. To...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Hindawi Limited
2023-01-01
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| Series: | Journal of Electrical and Computer Engineering |
| Online Access: | http://dx.doi.org/10.1155/2023/6327247 |
| _version_ | 1797862243708174336 |
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| author | Mays Kareem Jabbar Hafedh Trabelsi |
| author_facet | Mays Kareem Jabbar Hafedh Trabelsi |
| author_sort | Mays Kareem Jabbar |
| collection | DOAJ |
| description | A smart city’s vehicular communication strategy is important. A significant problem with vehicular communication is scalability. Clustering can help with vehicular ad hoc network (VANET) problems; however, clustering in VANET faces stability problems because of the rapid mobility of the vehicles. To achieve high stability for the VANET, this paper presents a new efficient Eigen-trick-based hypergraph stable clustering algorithm (EtHgSC). This algorithm has a twofold scheme for stable CH selection. In the first part of the proposed scheme, the cluster generation is handled using an improved hypergraph-based spectral clustering algorithm using the Eigen-trick method. The “Eigen-trick” method is used to partition both vertices and hyperedges, which provides an approach for reducing the computational complexity of the clustering. The cluster head (CH) is chosen in the second part, taking into account the requirements for keeping a stable connection with most neighbors. In addition to relative speed, neighboring degree, and eccentricity that are used to select the CH, the vehicle time to leave metric is introduced to increase the CH stability. The grey relational analysis model is used to find each vehicle’s score, and the CH is selected based on the maximum vehicle’s score. The results show the supremacy of our proposed scheme in terms of CH lifetime, cluster member (CM) lifetime, and the change rate of CH. Also, the proposed scheme achieves a considerable reduction in terms of packet delay. |
| first_indexed | 2024-04-09T22:16:08Z |
| format | Article |
| id | doaj.art-618ac62786324ffa9acff01b6d00425a |
| institution | Directory Open Access Journal |
| issn | 2090-0155 |
| language | English |
| last_indexed | 2024-04-09T22:16:08Z |
| publishDate | 2023-01-01 |
| publisher | Hindawi Limited |
| record_format | Article |
| series | Journal of Electrical and Computer Engineering |
| spelling | doaj.art-618ac62786324ffa9acff01b6d00425a2023-03-23T00:00:21ZengHindawi LimitedJournal of Electrical and Computer Engineering2090-01552023-01-01202310.1155/2023/6327247EtHgSC: Eigen Trick-Based Hypergraph Stable Clustering Algorithm in VANETMays Kareem Jabbar0Hafedh Trabelsi1CES_LabCES_LabA smart city’s vehicular communication strategy is important. A significant problem with vehicular communication is scalability. Clustering can help with vehicular ad hoc network (VANET) problems; however, clustering in VANET faces stability problems because of the rapid mobility of the vehicles. To achieve high stability for the VANET, this paper presents a new efficient Eigen-trick-based hypergraph stable clustering algorithm (EtHgSC). This algorithm has a twofold scheme for stable CH selection. In the first part of the proposed scheme, the cluster generation is handled using an improved hypergraph-based spectral clustering algorithm using the Eigen-trick method. The “Eigen-trick” method is used to partition both vertices and hyperedges, which provides an approach for reducing the computational complexity of the clustering. The cluster head (CH) is chosen in the second part, taking into account the requirements for keeping a stable connection with most neighbors. In addition to relative speed, neighboring degree, and eccentricity that are used to select the CH, the vehicle time to leave metric is introduced to increase the CH stability. The grey relational analysis model is used to find each vehicle’s score, and the CH is selected based on the maximum vehicle’s score. The results show the supremacy of our proposed scheme in terms of CH lifetime, cluster member (CM) lifetime, and the change rate of CH. Also, the proposed scheme achieves a considerable reduction in terms of packet delay.http://dx.doi.org/10.1155/2023/6327247 |
| spellingShingle | Mays Kareem Jabbar Hafedh Trabelsi EtHgSC: Eigen Trick-Based Hypergraph Stable Clustering Algorithm in VANET Journal of Electrical and Computer Engineering |
| title | EtHgSC: Eigen Trick-Based Hypergraph Stable Clustering Algorithm in VANET |
| title_full | EtHgSC: Eigen Trick-Based Hypergraph Stable Clustering Algorithm in VANET |
| title_fullStr | EtHgSC: Eigen Trick-Based Hypergraph Stable Clustering Algorithm in VANET |
| title_full_unstemmed | EtHgSC: Eigen Trick-Based Hypergraph Stable Clustering Algorithm in VANET |
| title_short | EtHgSC: Eigen Trick-Based Hypergraph Stable Clustering Algorithm in VANET |
| title_sort | ethgsc eigen trick based hypergraph stable clustering algorithm in vanet |
| url | http://dx.doi.org/10.1155/2023/6327247 |
| work_keys_str_mv | AT mayskareemjabbar ethgsceigentrickbasedhypergraphstableclusteringalgorithminvanet AT hafedhtrabelsi ethgsceigentrickbasedhypergraphstableclusteringalgorithminvanet |