Clustering algorithm for internet of vehicles (IoV) based on dragonfly optimizer (CAVDO)

Internet of vehicles (IoV) is a branch of the internet of things (IoT) which is used for communication among vehicles. As vehicular nodes are considered always in motion, hence it causes the frequent changes in the topology. These changes cause major issues in IoV like scalability, dynamic topology...

Full description

Saved in:
Bibliographic Details
Published in:The Journal of supercomputing 2018-09, Vol.74 (9), p.4542-4567
Main Authors: Aadil, Farhan, Ahsan, Waleed, Rehman, Zahoor Ur, Shah, Peer Azmat, Rho, Seungmin, Mehmood, Irfan
Format: Article
Language:English
Subjects:
Algorithms
Ant colony optimization
Clustering
Compilers
Computer Science
Dynamic stability
Heuristic methods
Internet of Things
Internet of Vehicles
Interpreters
Nodes
Particle swarm optimization
Process parameters
Processor Architectures
Programming Languages
Shortest-path problems
Topology
Traffic volume
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Internet of vehicles (IoV) is a branch of the internet of things (IoT) which is used for communication among vehicles. As vehicular nodes are considered always in motion, hence it causes the frequent changes in the topology. These changes cause major issues in IoV like scalability, dynamic topology changes, and shortest path for routing. Clustering is among one of the solutions for such type of issues. In this paper, the stability of IoV topology in a dynamic environment is focused. The proposed metaheuristic dragonfly-based clustering algorithm CAVDO is used for cluster-based packet route optimization to make stable topology, and mobility aware dynamic transmission range algorithm (MA-DTR) is used with CAVDO for transmission range adaptation on the basis of traffic density. The proposed CAVDO with MA-DTR is compared with the progressive baseline techniques ant colony optimization (ACO) and comprehensive learning particle swarm optimization (CLPSO). Numerous experiments were performed keeping in view the transmission dynamics for stable topology. CAVDO performed better in many cases providing minimum number of clusters according to current channel condition. Considerable important parameters involved in clustering process are: number of un-clustered nodes as a re-clustering criterion, clustering time, re-clustering delay, dynamic transmission range, direction, and speed. According to these parameters, results indicate that CAVDO outperformed ACO-based clustering and CLPSO in various network settings. Additionally, to improve the network availability and to incorporate the functionalities of next-generation network infrastructure, 5G-enabled architecture is also utilized.
ISSN:0920-8542
1573-0484
DOI:10.1007/s11227-018-2305-x