Optimal path for data dissemination in Vehicular Ad Hoc Networks using meta-heuristic

Chahal, Manisha; Harit, Sandeep

doi:10.1016/j.compeleceng.2019.03.006

Cited by 35 publications

(28 citation statements)

References 14 publications

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“…The uniform cross over and swapping functions were adopted with results demonstrating better performances by the bat, firefly, and PSO algorithms. Similarly, the PSO algorithm was also discretized in [26], termed the DPSO, in order to determine optimal communication paths in VANET. Here, authors modelled the link stability upon the computed Euclidean distance in polar coordinates, and the fitness function was based on the probability of occurrence of obstacles along the communication path.…”

Section: A Evidence Prior To the Present Studymentioning

confidence: 99%

“…Essentially, most MOAs are designed to solve continuous optimization problems, thus, it is pertinent to accurately describe the adaptation as well as the codification process of these continuous-based MOAs to their discrete versions, which is the form required to solve combinatorial problems in VANETs. However, with an exception to the approaches in [25], [26], most articles mentioned above lack the required details needed to understand the discretization processes used in their respective implementations, which limits their use and stifles the replication of these methods for further investigations.…”

Section: B Gaps In Prior Studiesmentioning

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%

Section: B Gaps In Prior Studiesmentioning

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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“…The simulation experiments used to test and propose our new algorithm for an efficient routing and dissemination in VANETs. [37].…”

Section: B Experiments Performed To Test Our Mhgu Algorithmmentioning

confidence: 99%

Swarm Intelligence Based Algorithm for Efficient Routing in VANET

Singh¹,

Prateek²,

Sastry³

2020

IJITEE

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Many recent researchers are working to optimize solutions in the field of Vehicular Adhoc Network. However, none of them has yet claimed that it will fulfill all the challenges of such a dynamic region. VANET in itself is a complete area of study, research and improvements. Most of the researchers and industry consortiums has given their hypothesis and solution that depends on their predefined scenarios but no complete solution has designed until yet. Through this research work, the authors concluded that bioinspired solutions can be used to integrate along with VANET for a much accurate and optimized solution. The performance of VANET depends on various scenarios and due to the unpredictable behavior of the vehicle movement, no concrete solution can be claimed as of now. We incorporated Swarm Intelligence in VANET through the Ant Colony Optimization algorithm and found that the performance of VANET has enhanced by avoiding the entire congested path as it senses the pheromone trail. We have implemented and tested the results using open source software like Instant Veins, Simulation of Urban MObility (SUMO) and MObility model generator for VEhicular networks (MOVE). SUMO has used for testing the traffic simulation and MOVE is used to design model. Python for the script. The OSM used to take a map of Dehradun city. When we performed the experimental setup and found that the result confirms in reducing the traveling time of the nodes, which makes nodes faster and managed even it helps in saving the hydrocarbon fuels. During our approach, we have devised our own algorithm that has improvised the present Ant Colony Optimization algorithm and has concluded that the average traveling time of the nodes minimized through our approach.

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“…7 The exchanged safety messages between V2V and V2R created by distinguished vehicular networking services help to benefit from such network in the direction of a wide variety of mobility-centric applications. [8][9][10] Safety messages consist of decentralized environmental notification messages (DENMs) and cooperative awareness messages (CAMs). DENM is an event-driven message divided into high-priority DENM and regular DENM.…”

Section: Introductionmentioning

confidence: 99%

A traffic‐centric fuzzy approach for solving the starvation problem of cooperative awareness messages in vehicular ad hoc networks

Abdollah

Zarei

2021

Int J Communication

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Congestion problem in vehicular ad hoc networks (VANETs) is one of the main factors that negatively affect the inter-vehicle communication (IVC) efficiency of such emerging networks. Decentralized congestion control (DCC) is an effective method for overcoming the congestion problem. However, lower priority packets such as cooperative awareness messages (CAMs) may be encountered with the starvation problem in the DCC approach. This paper proposes FDCC: a traffic-centric Fuzzy DCC approach for improving CAMs delivery ratio, especially in congested traffic flows. FDDC architecture consists of four fuzzy inference systems (FISs). The inter-vehicle distance (IVD) and the speed variation are the spatiotemporal fuzzy inputs, which are customized for both single direction and multi-direction scenarios. Based on the FIS outputs, the FDCC control system, unlike the DCC, provides a way for processing starved CAMs by ignoring unnecessary even-driven messages. The obtained simulation results through OMNET++ in both single and multi-direction scenarios show the efficiency of FDCC in terms of the end-to-end delay, network throughput, and packet delivery ratio for event-driven messages as well as essential CAMs.

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Optimal path for data dissemination in Vehicular Ad Hoc Networks using meta-heuristic

Cited by 35 publications

References 14 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

Swarm Intelligence Based Algorithm for Efficient Routing in VANET

A traffic‐centric fuzzy approach for solving the starvation problem of cooperative awareness messages in vehicular ad hoc networks

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