An intelligent cluster optimization algorithm based on Whale Optimization Algorithm for VANETs (WOACNET)

Abstract

Vehicular Ad hoc Networks (VANETs) an important category in networking focuses on many applications, such as safety and intelligent traffic management systems. The high node mobility and sparse vehicle distribution (on the road) compromise VANETs network scalability and rapid topology, hence creating major challenges, such as network physical layout formation, unstable links to enable robust, reliable, and scalable vehicle communication, especially in a dense traffic network. This study discusses a novel optimization approach considering transmission range, node density, speed, direction, and grid size during clustering. Whale Optimization Algorithm for Clustering in Vehicular Ad hoc Networks (WOACNET) was introduced to select an optimum cluster head (CH) and was calculated and evaluated based on intelligence and capability. Initially, simulations were performed, Subsequently, rigorous experimentations were conducted on WOACNET. The model was compared and evaluated with state-of-the-art well-established other methods, such as Gray Wolf Optimization (GWO) and Ant Lion Optimization (ALO) employing various performance metrics. The results demonstrate that the developed method performance is well ahead compared to other methods in VANET in terms of cluster head, varying transmission ranges, grid size, and nodes. The developed method results in achieving an overall 46% enhancement in cluster optimization and an F-value of 31.64 compared to other established methods (11.95 and 22.50) consequently, increase in cluster lifetime.

Performance Evaluation of Hospital Economic Management with the Clustering Algorithm Oriented towards Electronic Health Management

“…If the dense grid cell had not received new data samples for a long time, it might degenerate into a sparse grid. On the contrary, if the sparse grid cell received a batch of new data samples in a certain period of time, it might be upgraded to a dense grid [ 24 – 26 ]. Therefore, it was necessary to monitor the network density regularly and the clusters that had been generated should also be adjusted.…”

An Intelligent Machine Learning Based Routing Scheme for VANET

“…Different intelligent methods are used in [13], [14], [19]- [21] in order to provide intelligent clustering for VANET, such as Whale-inspired optimization, PSO algorithm, Grasshopper Optimization, Bio-inspired metaheuristic framework and Global Affinity Propagation Clustering.…”

“…But, the parameter of density, which is very important to introduce an effective clustering algorithm, has not been evoked in this scheme [22]. In [21], Husnain and Anwar have proposed a new whale-inspired optimization approach for clustering in VANET. This method integrates several parameters such as transmission area, density, speed and direction in the clustering process.…”

Dynamic Vehicular Communication using Gaussian Interpolation of Cluster Head Selection (GI-CHS)

“…The chosen CH takes on the responsibility for managing the CMs and for intra-cluster communication, which reduces Basic Safety Messages (BSMs) delivery times. A good clustering approach for selecting a CH is to choose the member with highest metrics in terms of ability to lead the temporary network for the longest time interval with high storage and processing capacities [26], [27], [28].…”