Enhanced Stability in Vehicular Ad Hoc Networks: A Dual-Layer Approach for Cluster Head Selection Using Hypergraph Spectral Clustering and Deep Learning | Chapter 7 | Scientific Research, New Technologies and Applications Vol. 4

The present study discuss about Hypergraph Clustering Model (HGCM) for More Stable Urban Scenario in VANET. VANETs have a highly dynamic topology, with a high relative speed of vehicles and frequent discontinuity in the networks. The topology management in VANETs can be achieved through clustering. A vehicular ad hoc network is a dynamic and constantly changing topology that requires reliable clustering to prevent connection failure. Packet delay (PD) is avoided via a stable cluster head (CH), which also keeps network throughput high. This chapter introduces a revolutionary two-step method for stable CH selection. The vehicle network is regarded in the first section of the suggested scheme as a one-to-many connection network, which is close to a realistic situation. A recently proposed spectral clustering methodology based on vehicle hypergraphs is used to handle the cluster creation. In the second part, the CH is selected considering the criteria for maintaining a stable connection with the maximum number of neighbours. The new rewarding/penalising relative speed and neighbourhood degree fulfil the condition. Eccentricity assesses that the vehicle should be at the centre of the cluster. Another metric with deep learning spectrum sensing is introduced for CH selection. Trust calculation is performed using deep learning-trained spectrum sensing as a model. The primary vehicle in noisy and noiseless environments is recognised using layers of long short-term memory. A high trust score is awarded to the vehicle that vacates the spectrum in the sensing of the primary vehicle. The stable CH selected by these metrics reduces the overhead that occurs due to the frequent shifting of the CH from one vehicle to another. The stable CH improves the routing parameters, such as PD and throughput. These parameters are distance-dependent. The minimum distance travelled by the packet leads to low PD and high throughput.  This has been validated by the improved CH stability; increased cluster member (CM) lifetime and reduced rate of change of CH. The proposed scheme also demonstrates a considerable improvement in PD and throughput. The analysis of the algorithm’s computational complexity will also be a benchmark of study in the next part of this work. CH stability decreases with the increase in vehicle density in the network.

 

Author (s) Details

Mays Kareem Jabbar
Faculty of Engineering, University of Misan, Al Amarah City, Misan Province, 62001, Iraq and CES Lab, Ecole National d’Ingénieurs de Sfax (ENIS), Sfax University, Sfax, Tunisia.

 

Hafedh Trabelsi
CES Lab, Ecole National d’Ingénieurs de Sfax (ENIS), Sfax University, Sfax, Tunisia.

 

Please see the book here:- https://doi.org/10.9734/bpi/srnta/v4/2103

 

An Overview on Eigen Trick-based Hypergraph Stable Clustering (EtHgSC) Algorithm in VANET | Chapter 1 | Theory and Applications of Engineering Research Vol. 3

Thic chapter presents the Eigen trick-based Hypergraph Stable Clustering algorithm (EtHgSC), which has a two-fold scheme for stable clustering. 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. This work introduces a novel efficient Eigen trick-based Hypergraph Stable Clustering algorithm (EtHgSC) to achieve high stability for the VANET. There are two schemes in this algorithm for steady CH selection. The cluster generation is handled by us in the first section of the suggested system. 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. Compared to the most common clustering algorithms in the literature, the JCV method follows our proposed EtHgSC method in terms of stability, because the two methods solve the problem of CH stability at junctions by preventing the frequent cluster breakage. 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.  Every vehicle is given a score using the gray relational analysis model, and the CH is chosen based on the vehicle with the highest score. The outcomes demonstrate the superiority of our suggested system with respect to CH lifetime, CM lifetime, and CH change rate. Furthermore, the suggested plan accomplishes a significant decrease in packet latency.

Author(s) Details:

Mays Kareem Jabbar,
Faculty of Engineering, University of Misan, Al Amarah City, Misan Province, 62001, Iraq and CES_Lab, Ecole National d’Ingénieurs de Sfax (ENIS), Sfax University, Tunisia.

Hafedh Trabelsi,
CES_Lab, Ecole National d’Ingénieurs de Sfax (ENIS), Sfax University, Tunisia.

Please see the link here: https://stm.bookpi.org/TAER-V3/article/view/13032

Determination of Text Summary Using Morphological Filtering of Intuitionistic Fuzzy Hypergraph | Chapter 5 | Current Topics on Mathematics and Computer Science Vol. 11

For many years, text summarization has piqued people's interest. It refers to condensing a document's text without losing any information. Many abstractive and extractive strategies for constructing summaries have been developed by researchers in the field of natural language processing. Extractive summaries select relevant sentences, but abstractive summaries modify the sentences and create a modified compact form. This work employs a unique extraction method that models the document as an Intuitionistic Fuzzy Hypergraph (IFHG). The work's major goals are to transform a document into an IFHG, perform morphological operations on it, and build a summary filter. This is the first study to use morphological procedures on an IFHG modelled on a text. When compared to previous machine-generated summaries, the approach produced a summary that is practically identical to a human-generated summary and exhibited higher accuracy. An attempt is also made to apply the skelton procedure to a text hypergraph.

Author (S) Details

Dhanya Prabhasadanam Mohanan

Department of Computer Science, RSET, Kochi, India.

Sreekumar Ananda Rao

Department of Computer Applications, CUSAT, Kochi, India.

Jathavedan Madambi

Department of Computer Applications, CUSAT, Kochi, India.

Ramkumar Padinjarepizharath Balakrishna

Department of Basic Sciences and Humanities, RSET, Kochi, India.

View Book :- https://stm.bookpi.org/CTMCS-V11/article/view/3979