Abstract
Vehicular ad hoc networks consist of access points for communication, transmission, and collecting information of nodes and environment for managing traffic loads. Clustering can be performed in the vehicular ad hoc networks for achieving the desired goals. Due to the random range of vehicular ad hoc networks, stability is the major issue on which major research is still in progress. In this article, a moth flame optimization–driven clustering algorithm is presented for vehicular ad hoc networks, replicating the social behavior of moth flames in creating efficient clusters. The proposed framework is extracted from the living routine of moth flames. The proposed framework allows efficient communication by creating the augmented number of clusters due to which it is termed as intelligent algorithm. Besides this, the use of unsupervised clustering technique emphasizes to call it as an intelligent clustering algorithm. The recommended intelligent clustering using moth flame optimization framework is executed for resolving and optimizing the clustering problem in vehicular ad hoc networks, the primary focus of the proposed scheme is to improve the stability in vehicular ad hoc networks. This proposed method can also be used for the transmission of data in vehicular networks. Intelligent clustering using moth flame optimization is then proved by relative study with two variants of particle swarm optimization: multiple-objective particle swarm optimization and comprehensive learning particle swarm optimization and a variant of ant colony optimization: ant colony optimization–based clustering algorithm for vehicular ad hoc network. The simulation demonstrates that the intelligent clustering using moth flame optimization is provisioning optimal outcomes in contrast to widely known metaheuristics. Furthermore, it provides a robust routing mechanism based on the clustering. It is suitable for large highways for the productivity of quality communication, reliable delivery for each vehicle and can be widely applicant.
Highlights
The simplest definition of network is a collection of devices for the communication
The results showed that the projected ant colony optimization (ACO) procedure achieved enhanced performance as equated to other approaches
The graphs illustrate that proposed solution/framework (ICMFO) is more optimized
Summary
The simplest definition of network is a collection of devices for the communication. Basically, different devices are connected to each other so that these devices can communicate or transmit data to each other. In VANETs, there are further streams such as, vehicle-to-vehicle (V2V), vehicle-to-infrastructure (I2V or V2I), and the last approach is hybrid which is the combination of both mentioned algorithms (V2X) Their applications lead to the facilitation in the handling of traffic jam and many other aspects.[5,6] The potential core of ITS is to provide the efficient communication mechanism during the transportation. Real-life experiments outdoor are among the major challenges leftover in this domain.[2] Clustering is one of the solutions to solve the issue of scalability It is imperative for effective utilization of resources and load balancing on scheduling data access with cooperative load balancing VANETs. In clustering, we group together the nodes that lie in the same geographical neighborhood which helps to make the network more scaleable.[2,3] We will use evolutionary algorithms (EA) in our research. The simulation results are compared with the well-known existing algorithms to show the comprehensive analysis
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