Expansion of Cluster Head Stability Using Fuzzy in Cognitive Radio CR-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 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.

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“…Te authors in [17,18] used the fuzzy logic scheme to improve the network's reliability and stability.…”

Section: Related Workmentioning

confidence: 99%

EtHgSC: Eigen Trick-Based Hypergraph Stable Clustering Algorithm in VANET

Jabbar

Trabelsi

2023

Journal of Electrical and Computer Engineering

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“…In vehicular networks due to high-speed vehicles, environmental fluctuations are inevitable. Therefore, some studies have investigated bringing stability using fuzzy logic [28]- [30]. Fan et al [28] with the aim of solving the environment dynamics problem, identify a mapped fuzzy space where the CSI is interpreted as fuzzy numbers.…”

Section: A Related Workmentioning

confidence: 99%

Power Control and Fuzzy Pairing in V2X Communications

Parizi

Pourmoslemi

Rajabi

et al. 2023

IEEE Systems Journal

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In this paper, a joint power control, and pairingbased resource allocation problem in the vehicle-to-everything (V2X) network is studied. This V2X network consists of a vehicleto-infrastructure (V2I) link and a vehicle-to-vehicle (V2V) link. Therefore, the resource allocation problem has been formulated with the objective function of maximizing the V2I link's rate with the constraints of quality of service (QoS). Due to its NP hard nature, the original resource allocation problem is divided into two sub-problems of power control and pairing. The power control sub-problem is transformed into a convex problem using an exponential variable change. A fuzzy decision-making method is exploited to solve the pairing sub-problem to have a highly reliable and stable V2V link. Then, the proposed joint power control and pairing based resource allocation technique is evaluated with a simulated highway and compared with similar pairing algorithms. These performance comparisons reveal that the stability of the proposed resource allocation technique outperforms the other related methods.

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“…e threshold ε can be defined using the central limit theorem. e detection probability can be defined as [33]…”

Section: Data Modelingmentioning

confidence: 99%

Deep Learning-Based Dynamic Stable Cluster Head Selection in VANET

Saleem

Zhou

Sarwar

et al. 2021

Journal of Advanced Transportation

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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.

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Expansion of Cluster Head Stability Using Fuzzy in Cognitive Radio CR-VANET

Cited by 36 publications

References 33 publications

EtHgSC: Eigen Trick-Based Hypergraph Stable Clustering Algorithm in VANET

EtHgSC: Eigen Trick-Based Hypergraph Stable Clustering Algorithm in VANET

Power Control and Fuzzy Pairing in V2X Communications

Deep Learning-Based Dynamic Stable Cluster Head Selection in VANET

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