Two New Clustering Algorithms for Vehicular Ad-Hoc Network Based on Ant Colony System

Fathian, Mohammad; Shiran, Gholam Reza; Jafarian-Moghaddam, Ahmad Reza

doi:10.1007/s11277-015-2404-4

Cited by 13 publications

(3 citation statements)

References 19 publications

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“…[10] Among the various optimization techniques used in sensor network, the ACO is one of the most trusted technique. The ACO technique uses the model of ant movement and the path utilized by the ant is used to predict the best path between the source and destination [2]. The data can travel to the sink node by using the substance called as pheromone in the path utilized by ant which they travel.…”

Section: Ant Colony Optimizationmentioning

confidence: 99%

Improved Clustering with Optimization and Intelligent Path Selection

Deepika*

2020

IJITEE

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Wireless sensor networks (WSN) are widely used networking paradigm in research areas and clustering is one of the trusted mechanism for storing and processing data among the nodes in the network. The clustering makes use of a single node for transferring data to the sink node with sustaining the energy of all other nodes in the network and does the procedure of storing and processing of data using an optimization technique called as Ant Colony Optimization (ACO). The ACO is proposed in accordance along clustering which is the optimization technique used for robust communication between source node and destination node. The clustering mechanism used for sending data along the best path among the source and destination in the network so that the data can reach the destination rapidly and that helps in availing the faster response between source node and destination node. The ACO will greatly influence the dropped packets and that the delay occurring in the network based on the traffic available among the nodes in the network.

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Section: Ant Colony Optimizationmentioning

confidence: 99%

Improved Clustering with Optimization and Intelligent Path Selection

Deepika*

2020

IJITEE

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“…These vehicles combine to form a network. This network is created for the data sharing between the automobiles in the network [1]. There are huge number of applications of VANETs, for instance, entertainment, safety and emergency services and much more [2].…”

Section: Introductionmentioning

confidence: 99%

An Efficient Optimization Technique for Node Clustering in VANETs Using Gray Wolf Optimization

2018

KSII TIIS

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Many methods have been developed for the vehicles to create clusters in vehicular ad hoc networks (VANETs). Usually, nodes are vehicles in the VANETs, and they are dynamic in nature. Clusters of vehicles are made for making the communication between the network nodes. Cluster Heads (CHs) are selected in each cluster for managing the whole cluster. This CH maintains the communication in the same cluster and with outside the other cluster. The lifetime of the cluster should be longer for increasing the performance of the network. Meanwhile, lesser the CH's in the network also lead to efficient communication in the VANETs. In this paper, a novel algorithm for clustering which is based on the social behavior of Gray Wolf Optimization (GWO) for VANET named as Intelligent Clustering using Gray Wolf Optimization (ICGWO) is proposed. This clustering based algorithm provides the optimized solution for smooth and robust communication in the VANETs. The key parameters of proposed algorithm are grid size, load balance factor (LBF), the speed of the nodes, directions and transmission range. The ICGWO is compared with the well-known meta-heuristics, Multi-Objective Particle Swarm Optimization (MOPSO) and Comprehensive Learning Particle Swarm Optimization (CLPSO) for clustering in VANETs. Experiments are performed by varying the key parameters of the ICGWO, for measuring the effectiveness of the proposed algorithm. These parameters include grid sizes, transmission ranges, and a number of nodes. The effectiveness of the proposed algorithm is evaluated in terms of optimization of number of cluster with respect to transmission range, grid size and number of nodes. ICGWO selects the 10% of the nodes as CHs where as CLPSO and MOPSO selects the 13% and 14% respectively.

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“…These nodes create a dispersed network of automobiles for information exchange [7]. The potential applications for VANETs include safety, comfort, and infotainment related applications [8].…”

Section: Introductionmentioning

confidence: 99%

CACONET: Ant Colony Optimization (ACO) Based Clustering Algorithm for VANET

et al. 2016

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A vehicular ad hoc network (VANET) is a wirelessly connected network of vehicular nodes. A number of techniques, such as message ferrying, data aggregation, and vehicular node clustering aim to improve communication efficiency in VANETs. Cluster heads (CHs), selected in the process of clustering, manage inter-cluster and intra-cluster communication. The lifetime of clusters and number of CHs determines the efficiency of network. In this paper a Clustering algorithm based on Ant Colony Optimization (ACO) for VANETs (CACONET) is proposed. CACONET forms optimized clusters for robust communication. CACONET is compared empirically with state-of-the-art baseline techniques like Multi-Objective Particle Swarm Optimization (MOPSO) and Comprehensive Learning Particle Swarm Optimization (CLPSO). Experiments varying the grid size of the network, the transmission range of nodes, and number of nodes in the network were performed to evaluate the comparative effectiveness of these algorithms. For optimized clustering, the parameters considered are the transmission range, direction and speed of the nodes. The results indicate that CACONET significantly outperforms MOPSO and CLPSO.

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Two New Clustering Algorithms for Vehicular Ad-Hoc Network Based on Ant Colony System

Cited by 13 publications

References 19 publications

Improved Clustering with Optimization and Intelligent Path Selection

Improved Clustering with Optimization and Intelligent Path Selection

An Efficient Optimization Technique for Node Clustering in VANETs Using Gray Wolf Optimization

CACONET: Ant Colony Optimization (ACO) Based Clustering Algorithm for VANET

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