Extensible Particle Swarm Optimization Technique Using Route Set Choosing Algorithm in MANET

Sindhuja, S.; Vadivel, R.

doi:10.1109/ic-etite47903.2020.385

Cited by 4 publications

(1 citation statement)

References 14 publications

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“…Sindhuja et al [13]described that the ongoing issues in a versatile organization are load awkwardness, more correspondence delay, discovering the best way is troublesome and energy utilization for the particular directing way. Terminal dependability doesn't consider this multitude of improvement measures, by using diverse pointless ways between the source and the objective, which restricts the authority of this issue.…”

Section: Related Workmentioning

confidence: 99%

Fuzzified Swarm Intelligence Framework Using Fpsor Algorithm for High-Speed Manet

Gopalan¹

2021

Preprint

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Ad-hoc routing protocols may be dispersed and involve every node in the route discovery process by making routing data more reliable. Mobile-Adhoc-Networks (MANETs) consist of many portable nodes that can commune directly with each other or through intermediate nodes. Repeatedly, nodes in MANETs operate with batteries and can roam freely, and thus, a node may exhaust its energy or move away without providing any notice to its cooperative nodes. In MANETs, a route consists of numerous links in sequence, and so, its lifetime is based on the lifetime of every node and also the wireless links among adjacent nodes. In this research work Fuzzified Particle Swarm Optimization oriented Routing (FPSOR) algorithm is planned to minimize data loss and computational overhead, which in turn maximizes the lifetime of MANETs. Particle Swarm Optimization oriented Routing protocol (PSOR) has taken energy competence as a significant criterion for processing routing and driving optimised path for the data accelerating and processing to the source node. The PSOR produces a fresh route for routing by considering the fitness value of energy to evaluate diverse paths and to choose the best-optimised path whose energy consumption is less as compared to ant colony optimization routing paths. This algorithm utilizes a good approach focusing energy levels/status of the nodes through fuzzification and the lengths of the routed ways. NS2 simulator is cast for performance evaluation.

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Section: Related Workmentioning

confidence: 99%