Design and performance evaluation of Improved DFACO protocol based on dynamic clustering in VANETs

Abstract

This study focuses on Vehicular Ad-hoc Networks (VANETs) stability in an environment that is dynamic which often leads to major challenges in VANETs, such as dynamic topology changes, shortest routing paths and also scalability. One of the best solutions for such challenges is clustering. In this study, we present five novel routing protocols based on Dynamic Flying Ant Colony Optimization (DFACO) algorithm to achieve minimum number of clusters, high accuracy, minimum time and solution cost by selecting the best cluster-head which is obtained from a new mechanism of dynamic metaheuristic-based clustering. In this regard, major improvements are applied on classical DFACO by adjusting the procedure for updating the pheromone and tuning the evaporation rate that has a major role in DFACO. In this research two individual phases of experiments are conducted for performance evaluation of proposed routing protocols. The presented solution is verified and compared to classic Ant Colony Optimization (ACO), DFACO and ACO Based Clustering Algorithm for VANET (CACONET) algorithms in phase one; and compared to clustering algorithms such as Center Position and Mobility CPM), Highest-Degree algorithm (HD), Angle-based Clustering Algorithm (ACA) in phase two through NS-2 and SUMO simulation tools. Simulation results have confirmed the expected behaviour and show that our proposed protocols achieve better node connectivity and cluster stability than the former.

Beetle colony optimization algorithm‐based node clustering scheme for efficient data dissemination in vehicular ad hoc networks

“…Symmetric Lévy steady distribution is typically produced using the Mantegna algorithm, where symmetry shows that "d t " can take positive or negative values. Step size is determined at random based on Equation (18):…”

“…It is seen that this algorithm offers improved performance based on network lifetime. Ebadinezhad 18 presented new routing protocols using the dynamic flying ant colony optimization (DFACO) algorithm that involves a reduced number of clusters, increased accuracy, reduced time, and cost for choosing the best CH by employing dynamic metaheuristic‐based clustering. Traditional DFACO is modified by varying the method involved in modifying pheromones and adjusting the evaporation rate.…”

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

“…1) High latency 2) Data security 3) Routing and routes length 4) Channel congestion www.ijacsa.thesai.org To enable quality of service and better resource management, hierarchical structure is proposed by researchers [21], [22]. Such approach describes the process of close to each other vehicles, with shared features, to join a group that is termed a cluster.…”