Routing Algorithm for Vehicular Ad Hoc Network Based on Dynamic Ant Colony Optimization

Oranj, Alireza Mohammadnia; Alguliyev, Rasim; Yusifov, Farhad; Jamali, S.

doi:10.18178/ijeee.4.1.79-83

Cited by 33 publications

(8 citation statements)

References 19 publications

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“…Different routing algorithms have been proposed in order to determine the shortest and fastest path in V2V and V2I networks. An example can be found in [13], wherein a dynamic ad-hoc on-demand routing protocol based on the ant colony optimization (ACO) algorithm was proposed for VANETs. Experimental results show that the proposed protocol successfully withstood episodes of node disconnections during the communication process.…”

Section: A Evidence Prior To the Present Studymentioning

confidence: 99%

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New Discrete Cuckoo Search Optimization Algorithms for Effective Route Discovery in IoT-Based Vehicular Ad-Hoc Networks

et al. 2020

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Recently, the Internet of Things (IoT) is widely considered in vehicular ad-hoc networks (VANETs) for use in intelligent transportation systems. In particular, the pervasive deployment of different sensors in modern vehicles has unlocked interesting possibilities for improving routing performance in VANETs. Nevertheless, the discovery of short single loop-free routes for effective and efficient information dissemination in VANETs remains a challenge. This challenge proves more difficult to solve since it reduces to the case of finding the shortest Hamiltonian path for effective routing in VANETs. Consequently, in this paper, we propose two discretized variants of the cuckoo search optimization (CSO) algorithm, namely, the Lévy flight-based discrete CSO (LF-DCSO) and the random walk-based discrete CSO (RW-DCSO) for effective route discovery in VANETs. In addition, we investigated the inverse mutation operator gleaned from genetic algorithm (GA) in order to improve the exploration properties of our DCSO variants. We describe a new objective function that effectively models the reliability of individual links between nodes that comprise a single route. A detailed report of the routing protocol that controls the routing process is presented. Our proposed methods were compared against the roulette wheel-based GA and the improved kmeans-based GA termed IGAROT. Specifically, our findings reveal that there was no significant difference in the performance of the different methods in the low vehicle density scenario, however, in the medium vehicle density scenario, the RW-DCSO algorithm achieved 2.56%, 100%, and 128.57% percentage increment in its route reliability score over the LF-DCSO, RW-GA, and IGAROT algorithms, respectively. Whereas in the high vehicle density scenario, the LF-DCSO algorithm achieved a percentage increment of 42.85%, 525%, and 733.33% in the route reliability score obtained over the RW-DCSO, IGAROT, and RW-GA algorithms, respectively. Such results suggest that our methods are able to guarantee effective routing in VANETs.

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Section: A Evidence Prior To the Present Studymentioning

confidence: 99%

“…Apply the inversion mutation operation for the RW-DCSO algorithm to the best route V * using (13) as Compute the new reliability score R (k) of this new route using 39:…”

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confidence: 99%

New Discrete Cuckoo Search Optimization Algorithms for Effective Route Discovery in IoT-Based Vehicular Ad-Hoc Networks

et al. 2020

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“…A. M. Oranj et al [8] presented a Dynamic MANET Ondemand (DYMO) protocol and ant colony optimization algorithm which was capable of managing environmental changes. Graphs were used in ant colony optimization algorithm which was based on probabilities' method to find routes.…”

Section: Literature Reviewmentioning

confidence: 99%

Design of Power Efficient Routing Protocol for Smart Livestock Farm Applications

Emira¹,

Youssef²,

Abouelatta³

2020

Adv. sci. technol. eng. syst. j.

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“…For optimization the number-of-clusters in VANETs to obtain the more optimize solution there is a gap of improvement. Oranj et al [64] disagreed over the fact that even if these parameters can be affected by environmental parameters, they usually get ignored, affecting the performance due to throughput. The idea about the identity of each node was proposed by Baker and Ephrimides [65] in which, each node contains the unique ID.…”

Section: Evolutionary Algorithmsmentioning

confidence: 99%

Moth Flame Clustering Algorithm for Internet of Vehicle (MFCA-IoV)

et al. 2019

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A network of wirelessly connected vehicles by using any mean of connectivity is termed as the Internet of Vehicle (IoV). Managing this type of network is a challenging task. Clustering is a technique to efficiently manage resources in this type of network. In a cluster, all inter/intra cluster communication is managed by a cluster head (CH). Load on each CH, the lifetime of the cluster and the total number of clusters in a network are some parameters to measure the efficiency of the network. In this paper, a novel technique based on moth flame clustering algorithm for IoV (MFCA-IoV) is proposed. Moth flame optimizer is a nature-inspired algorithm. MFCA-IoV generates optimized clusters for robust transmission and is evaluated experimentally with renowned techniques. These techniques are Grey-Wolf-optimizationbased method used for the clustering called as GWOCNETs, multi-objective particle-swarm-optimization (MOPSO), clustering algorithm based on Ant colony optimization for vehicular ad-hoc networks termed as CACONET and comprehensive learning particle-swarm-optimization (CLPSO). To assess the comparative efficiency of these algorithms, numerous experiments are performed. The parameters like network gridsize, number of nodes, speed, direction, and transmission-range of the nodes are considered for optimized clustering. The results indicate, MFCA-IoV is showing 73% nodes, which are not selected as a cluster head while existing techniques are providing 57%, 50%, 51%, and 58% for GWOCNETs, CLPSO, MOPSO, and CACONET, respectively. Hence, lesser the nodes are selected as CH, the more optimal result will be considered. INDEX TERMS Internet of Vehicle (IoV), vehicular ad-hoc networks (VANETs), intelligent transportation system (ITS), Ant-colony-optimization (ACO), particle swarm optimization (PSO), MFO; clustering, meta-heuristic algorithms, population-based algorithm.

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Routing Algorithm for Vehicular Ad Hoc Network Based on Dynamic Ant Colony Optimization

Cited by 33 publications

References 19 publications

New Discrete Cuckoo Search Optimization Algorithms for Effective Route Discovery in IoT-Based Vehicular Ad-Hoc Networks

New Discrete Cuckoo Search Optimization Algorithms for Effective Route Discovery in IoT-Based Vehicular Ad-Hoc Networks

Design of Power Efficient Routing Protocol for Smart Livestock Farm Applications

Moth Flame Clustering Algorithm for Internet of Vehicle (MFCA-IoV)

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