Reliable Priority Based QoS Real-Time Traffic Routing in VANET: Open Issues &amp; Parameter

Nisar, Kashif; Mu’azu, Abubakar Aminu; Lawal, Ibrahim A.; Khan, Sohrab; Memon, Shuaib K.

doi:10.1109/aict50176.2020.9368689

Cited by 14 publications

(2 citation statements)

References 19 publications

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“…The proposed methodology was compared with other state of the art machine learning classifiers like SVM, RF, NN, and auto-encoders, and was shown to provide more accurate prediction results. Other research has discussed the application of technologies such as the Internet of Things (IoT), networks, software-defined networking (SDN), and wireless sensor networks (WSN) [32][33][34][35][36][37][38][39] Boyu Lyu et al [22] proposed an approach converting RNA-Seq data into 2D images which were then classified by CNN. This technique was applied to 32 types of tumors for classification.…”

Section: Related Workmentioning

confidence: 99%

Analyzing RNA-Seq Gene Expression Data Using Deep Learning Approaches for Cancer Classification

et al. 2022

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Ribonucleic acid Sequencing (RNA-Seq) analysis is particularly useful for obtaining insights into differentially expressed genes. However, it is challenging because of its high-dimensional data. Such analysis is a tool with which to find underlying patterns in data, e.g., for cancer specific biomarkers. In the past, analyses were performed on RNA-Seq data pertaining to the same cancer class as positive and negative samples, i.e., without samples of other cancer types. To perform multiple cancer type classification and to find differentially expressed genes, data for multiple cancer types need to be analyzed. Several repositories offer RNA-Seq data for various cancer types. In this paper, data from the Mendeley data repository for five cancer types are analyzed. As a first step, RNA-Seq values are converted to 2D images using normalization and zero padding. In the next step, relevant features are extracted and selected using Deep Learning (DL). In the last phase, classification is performed, and eight DL algorithms are used. Results and discussion are based on four different splitting strategies and k-fold cross validation for each DL classifier. Furthermore, a comparative analysis is performed with state of the art techniques discussed in literature. The results demonstrated that classifiers performed best at 70–30 split, and that Convolutional Neural Network (CNN) achieved the best overall results. Hence, CNN is the best DL model for classification among the eight studied DL models, and is easy to implement and simple to understand.

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Section: Related Workmentioning

confidence: 99%

Analyzing RNA-Seq Gene Expression Data Using Deep Learning Approaches for Cancer Classification

et al. 2022

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“…The research by Nisar et al, (2020) [42] projected priority based messages dissemination mechanism based on IEEE 802.11e MAC protocol in order to improve the real-time traffic packet distribution ratio (PDR). To determine the priority message (PM) is considered direction, real-time packet timestamp, velocity and packet receiving vehicle node.…”

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confidence: 99%

Enhanced Congestion Control Model Based on Message Prioritization and Scheduling Mechanism in Vehicle-to-Infrastructure (V2I)

Ismail

Hossain²,

Md.Noor³

et al. 2022

J. Phys.: Conf. Ser.

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Vehicular Ad Hoc Networks (VANETs) is an origination of Mobile Ad-Hoc Network (MANET), where road vehicles will distribute messages and provide safety alerts to notice hazardous circumstances to the drivers. One of its greatest challenges is ensuring Quality of Service (QoS) owing to channel sharing, high traffic and topology changes in VANETs. These challenges cause of degradation of network performance. Congestion control should be properly considered to improve the network performance and transmission message over VANETs. However, there seem significant limitations to most of the other current congestion control mechanisms. In this paper, an enhanced congestion control model based on prioritization and scheduling-based strategy is proposed. This strategy assigns priorities of the safety messages and non-safety messages based of the message type and network condition. Further, safety message can be classified into event-driven messages and beacon messages. At that point, all messages transmit into the appropriate queue based on their priority and schedule all messages in each queue. However, when new message enqueue, reschedule in each queue may cause additional unwanted delay to be processed cause of inefficient scheduling technique. Thus, an improved dynamic scheduling algorithm also proposed to schedule the messages in this circumstance.

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F-SDNGR: Fog with SDN-Based Geographical Routing Enhances the Reliable Routing in Vehicular Ad Hoc Network (VANET)

Karthikeyini,

Rupa,

Athira

et al. 2023

Algorithms for Intelligent Systems

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Reliable Priority Based QoS Real-Time Traffic Routing in VANET: Open Issues & Parameter

Cited by 14 publications

References 19 publications

Analyzing RNA-Seq Gene Expression Data Using Deep Learning Approaches for Cancer Classification

Analyzing RNA-Seq Gene Expression Data Using Deep Learning Approaches for Cancer Classification

Enhanced Congestion Control Model Based on Message Prioritization and Scheduling Mechanism in Vehicle-to-Infrastructure (V2I)

F-SDNGR: Fog with SDN-Based Geographical Routing Enhances the Reliable Routing in Vehicular Ad Hoc Network (VANET)

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