QoS-Aware Routing in Vehicular Ad Hoc Networks Using Ant Colony Optimization and Bee Colony Optimization

2018 ◽  
pp. 251-260 ◽  
Author(s):  
Sumandeep Kaur ◽  
Trilok C. Aseri ◽  
Sudesh Rani
Keyword(s):  
Ad Hoc Networks ◽  
Ant Colony Optimization ◽  
Vehicular Ad Hoc Networks ◽  
Ad Hoc ◽  
Ant Colony ◽  
Bee Colony ◽  
Bee Colony Optimization ◽  
Hoc Networks

An effect of big data technology with ant colony optimization based routing in vehicular ad hoc networks: Towards smart cities

2019 ◽  
Vol 217 ◽  
pp. 584-593 ◽  
Author(s):  
S.K. Lakshmanaprabu ◽  
K. Shankar ◽  
S. Sheeba Rani ◽  
Enas Abdulhay ◽  
N. Arunkumar ◽  
...  
Keyword(s):  
Big Data ◽  
Ad Hoc Networks ◽  
Ant Colony Optimization ◽  
Vehicular Ad Hoc Networks ◽  
Ad Hoc ◽  
Smart Cities ◽  
Ant Colony ◽  
Hoc Networks ◽  
Big Data Technology

scholarly journals Intelligent clustering using moth flame optimizer for vehicular ad hoc networks

2019 ◽  
Vol 15 (1) ◽  
pp. 155014771882446 ◽  
Author(s):  
Atif Ishtiaq ◽  
Sheeraz Ahmed ◽  
Muhammad Fahad Khan ◽  
Farhan Aadil ◽  
Muazzam Maqsood ◽  
...  
Keyword(s):  
Particle Swarm Optimization ◽  
Ad Hoc Networks ◽  
Ant Colony Optimization ◽  
Clustering Algorithm ◽  
Vehicular Ad Hoc Networks ◽  
Ad Hoc ◽  
Particle Swarm ◽  
Ant Colony ◽  
Swarm Optimization ◽  
Hoc Networks

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.

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