Multi Objective Glow Swarm Based Situation and Quality Aware Routing in VANET

Kumar, N. M. Saravana; Pagadala, Pavan Kumar; Vijayakumar, V.; A., None Kavinya

doi:10.1007/s11277-022-09582-5

Cited by 9 publications

(2 citation statements)

References 23 publications

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“…In 2022, Kumar et al 24 employed the “Geographic Source Routing (GSR) protocol and the Road‐Based using Vehicular Traffic‐Reactive (RBVT‐R) routing protocol” for VANET. Further, the performance was enhanced by tuning the ideal path using the optimization algorithm within diverse paths based on the QoS requirements, where constraints like hop count, packet delivery, and average delay were considered under the fitness function.…”

Section: Literature Surveymentioning

confidence: 99%

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OHRDBN: An optimized hybrid model of RBF and DBN for obstacle‐aware routing with optimal path selection in VANET sector

Dhanapal,

Thenmozhi

2024

Int J Communication

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SummaryVehicular ad hoc networks (VANETs) are increasingly essential for communication architecture utilized in daily human life. The presence of obstacles in the terrain area, as well as the vehicular nodes present in the network, is considered an essential constraint since it reduces the efficacy of the mobile ad hoc network (MANET). To attain the VANET requirements, a novel routing scheme is designed to identify the obstacle. The main aspect of the proposed work is to detect the obstacle and determine the path availability. Initially, the characteristics of vehicle nodes are gathered, which include the packet loss, received signal strength indicator (RSSI), connectivity, energy level, and so on. In the process of data transmission, the proposed model is intended to detect the obstacles and determine the available path by using an optimized hybrid model (OHL) combining radial basis function (RBF) and deep belief network (DBN). The parameter optimization in the hybrid classifier model is done with the help of the adaptive chimp optimization algorithm (ACOA). Once it checks the path availability, then the path optimization is accomplished by using the same ACOA. Finally, the objective function for the proposed work is constructed by considering various constraints like distance, energy, link availability, life span, link duration, and mobility. The performance of the implemented system is analyzed, and its results are validated through different measures. The outcome of the suggested model demonstrates that the implemented system tends to exploit effective routing with obstacle detection when compared to various other existing approaches.

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Section: Literature Surveymentioning

confidence: 99%

“…However, the link failure can occur since the radio range is high. RBVT‐R 24 reduces the delay and evades the hop count complexity. Due to high dimensional space, the routing algorithm faces issues regarding convergence.…”

Section: Literature Surveymentioning

confidence: 99%