Connectivity analysis of V2V communication with discretionary lane changing approach

Naskath, J.; Paramasivan, B.; Mustafa, Zaid; Aldabbas, Hamza

doi:10.1007/s11227-021-04086-8

Cited by 10 publications

(6 citation statements)

References 25 publications

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“…In received signal strength‐based decision algorithms, handover decisions are made based on the comparison between the RSS of the present and previous networks. Many studies have been conducted in this area in order to provide seamless network connectivity 22 …”

Section: Literature Surveymentioning

confidence: 99%

Fast multicriteria network selection scheme using hybrid of cat swarm optimization and TOPSIS algorithm for optimal handover in VANET

Jahangeer¹

2023

Trans Emerging Tel Tech

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Future generation vehicular networks will grant the technique for efficient data dissemination over dense heterogeneous radio technologies by considering existing communication technology to attain higher spectral competence for successful data rates. Due to higher mobility schemes, a vehicle frequently switches among different heterogeneous networks, which leads to unnecessary handovers. In this scenario, an amateurish handover algorithm delivers data with heavy packet loss, caused by frequent and ping‐pong handover. Hence, this article presents the model for. A network simulator is used to prove the performance of the algorithm. The performance of the algorithm is analyzed in terms of vehicle density, velocity, throughput, packet loss, and jitter.

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Section: Literature Surveymentioning

confidence: 99%

Fast multicriteria network selection scheme using hybrid of cat swarm optimization and TOPSIS algorithm for optimal handover in VANET

Jahangeer¹

2023

Trans Emerging Tel Tech

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“…Perceptrons learn by adjusting the connection weights to reduce the output error relative to the predicted outcome for each training vector. Authors [19] propose secure routing on the Internet of Vehicles based on V2V (Vehicle to Vehicle) communication [20] using multilayer perceptron (MLP) to detect intruders or attackers on the network [21]. MLP algorithm-generated classification report based on Denial of service attacks.…”

Section: Application Of Mlpmentioning

confidence: 99%

A Study on Different Deep Learning Algorithms Used in Deep Neural Nets: MLP SOM and DBN

Naskath¹,

Sivakamasundari²,

Begum

2022

Wireless Pers Commun

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Deep learning is a wildly popular topic in machine learning and is structured as a series of nonlinear layers that learns various levels of data representations. Deep learning employs numerous layers to represent data abstractions to implement various computer models. Deep learning approaches like generative, discriminative models and model transfer have transformed information processing. This article proposes a comprehensive review of various deep learning algorithms Multi layer perception, Self-organizing map and deep belief networks algorithms. It first briefly introduces historical and recent state-of-the-art reviews with suitable architectures and implementation steps. Moreover, the various applications of those algorithms in various fields such as wireless networks, Adhoc networks, Mobile ad-hoc and vehicular ad-hoc networks, speech recognition engineering, medical applications, natural language processing, material science and remote sensing applications, etc. are classified.

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“…This process continues until the message reaches the destination. Note here that the most efficient node among the potential candidates acts as a next‐hop relay and excludes other candidates from forwarding the message 14 . Therefore, the proposed system selects the best relay and thus reduces a large number of packets retransmission.…”

Section: Introductionmentioning

confidence: 99%

“…Note here that the most efficient node among the potential candidates acts as a next-hop relay and excludes other candidates from forwarding the message. 14 Therefore, the proposed system selects the best relay and thus reduces a large number of packets retransmission. The Proposed work contains the following modules The system model assumes an opportunistic network with N nodes and 'R' transmission range.…”

Section: Introductionmentioning

confidence: 99%

Regular routine aware routing in opportunistic mobile social networks

Syed Rabiya,

Ramalakshmi,

Naskath

2023

Trans Emerging Tel Tech

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Due to intermittent connectivity, routing protocols in opportunistic mobile social networks (OMSN) forward messages through opportunistic contacts. Previous opportunistic routing protocols focus on the past contact history of nodes to make a forwarding decision. It was found that predicting temporally evolving contacts and communities according to different times of the day would improve the accuracy of the prediction. In this paper, a routing paradigm called RRoutine is proposed that allows message holders to exchange multiple copies of the message based on the predicted number of communities. This distribution continues until the remaining copies of the message become one. Each message holder applies either inter or intra‐community forwarding and selects a set of potential forwarders based on the prediction of future encounters in the community and an expected remaining duration between two nodes (ERIMD). The estimation of ERIMD takes into account the sequence of daily activities, the time scale of current activities, and the return time to regular meetings of the nodes. Also, unlike traditional routing, where a single forwarder is selected, a set of potential candidates is selected and prioritized according to their contact prediction, with the most likely candidate receiving the highest priority. Simulation results obtained from synthetic and real mobility data show that the proposed paper improves the performance of the system in terms of message delivery rate, throughput, and latency.

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Connectivity analysis of V2V communication with discretionary lane changing approach

Cited by 10 publications

References 25 publications

Fast multicriteria network selection scheme using hybrid of cat swarm optimization and TOPSIS algorithm for optimal handover in VANET

Fast multicriteria network selection scheme using hybrid of cat swarm optimization and TOPSIS algorithm for optimal handover in VANET

A Study on Different Deep Learning Algorithms Used in Deep Neural Nets: MLP SOM and DBN

Regular routine aware routing in opportunistic mobile social networks

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