Design and Analysis of Linear Tapered Slot Antenna for Super High Frequency Applications

doi:10.35940/ijitee.a1055.1191s19

Cited by 3 publications

References 5 publications

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A novel clustering and optimal resource scheduling in vehicular cloud networks using MKMA and the CM‐CSO algorithm

Kouser

Manikandan²

2023

Int J Communication

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Summary Various opportunities aimed at the employment of delay‐sensitive applications in the vehicular environment are presented by the vehicular cloud (VC). Contrary to diverse wireless networks, VC networks possess exceptional features among others, namely, shorter transmission time along with a higher dynamic topology. Although integration with the cloud offers higher storage along with computation capabilities, it as well entails restricted resource availability. The restrictions on the number of resources serve as a challenge in servicing the applications with their necessary quality of service (QoS) guarantees as the number of service requests for applications keeps on augmenting with diverse circumstances. Thus, the need for an effective scheduling methodology arises to decide the sequence of servicing application requests and successful utilization of a broadcast medium, along with data transmission. To do efficient resource scheduling on VC networks, an optimization algorithm, namely, the crossover and mutation (CM)‐centered chicken swarm optimization (CSO) is proposed and implemented with the help of a publicly available dataset. Initially, the VC infrastructure is initialized and some vehicle information is extracted as features. Next, the Brownian motion‐centered bacteria foraging optimization (BM‐BFO) algorithm chooses the essential features. Centered on the chosen features, the vehicles are clustered using the modified K‐means algorithm. Next, as for the cloud server's virtual machines (VMs), the resource information is extracted. Lastly, the CM‐CSO algorithm carries out the optimal scheduling in the VC by means of the clustered features of vehicles and features of the VM. The proposed techniques' findings are scrutinized and analogized to the other prevailing methodologies to confirm that the proposed work performs effectively and gives optimal resource allocation (RA) to the VC.

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A novel clustering and optimal resource scheduling in vehicular cloud networks using MKMA and the CM‐CSO algorithm

Kouser

Manikandan²

2023

Int J Communication

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A Robust Vehicular Networking Management System for the Traffic Control Using PK-DCNN Classification And L-SSA Based Rerouting

Kouser¹,

Manikandan²

2023

2023 Fifth International Conference on Electrical, Computer and Communication Technologies (ICECCT)

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A hybrid Square Shaped Adaptive Neuro-Fuzzy Interference with M-PeSOA model for optimal path selection

Kouser

Manikandan²

2023

IFS

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In VCC, at the time of congestion, to deal with traffic, the vehicles’ underutilized resources are shared; subsequently, these resources aren’t constrained to computing power, storage, along with internet connectivity. Nevertheless, owing to the vehicular network characteristics, attaining the QoS requirements search together with the allocation of resources in the Vehicular Cloud (VC) has turned into a complicated task. An intelligent Square Shaped (SS)-Adaptive Neuro-Fuzzy Interference (SS-ANFIS) methodology for Resource Scheduling (RS) in addition to Mean-centered Penguins Search Optimization Algorithm (M-PeSOA) for Optimal Path Selection (OPS) in the VC is proposed here for efficient resource allocation. (a) Feature extraction, (b) Vehicles clustering, (c) OPS, (d) Resource information extraction, and (e) RS included in the proposed methodology. First, the vehicular network is initialized following that the vehicle features are extracted. Next, Cluster Heads (CHs) are generated regarding which vehicles are clustered; subsequently, the multi-paths are generated. After that, by employing the M-PeSOA, the OPS procedure is conducted; thus, the VC’s resource information is extracted aimed at scheduling the resources efficiently. Lastly, by employing the SS-ANFIS, vehicles are scheduled in the optimal paths. The proposed resource allocation system’s performance is assessed, and the experiential outcomes are analogized using the sumo tool and java platform.

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Design and Analysis of Linear Tapered Slot Antenna for Super High Frequency Applications

Cited by 3 publications

References 5 publications

A novel clustering and optimal resource scheduling in vehicular cloud networks using MKMA and the CM‐CSO algorithm

A novel clustering and optimal resource scheduling in vehicular cloud networks using MKMA and the CM‐CSO algorithm

A Robust Vehicular Networking Management System for the Traffic Control Using PK-DCNN Classification And L-SSA Based Rerouting

A hybrid Square Shaped Adaptive Neuro-Fuzzy Interference with M-PeSOA model for optimal path selection

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