An Intelligent Machine Learning Based Routing Scheme for VANET

Kandali, Khalid; Bennis, Lamyae; Bannay, Omar El; Bennis, Hamid

doi:10.1109/access.2022.3190964

Cited by 27 publications

(7 citation statements)

References 38 publications

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“…Routing protocols' performance issues related to an urban scenario using a clustering approach based on density peaks and particle swarm optimization (PSO) is covered in [37]. However, the approach is limited to a basic traffic model.…”

Section: Related Workmentioning

confidence: 99%

A Novel Spider Monkey Optimization for Reliable Data Dissemination in VANETs Based on Machine Learning

Gupta,

Rathi

2024

Sensors

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The growth in linked and autonomous vehicles has led to the emergence of vehicular ad hoc networks (VANETs) as a means to enhance road safety, traffic efficiency, and passenger comfort. However, VANETs face challenges in facilitating trustworthiness and high-quality services due to communication delays caused by traffic, dynamic topology changes, variable speeds, and other influencing factors. Hence, there is a need for a reliable data dissemination scheme capable of reducing communication delays among hops by identifying effective forwarder nodes. In this paper, we propose a novel, weighted, estimated, spider monkey-based, nature-inspired optimization (w-SMNO) method to generate a set of efficient relays. Additionally, we introduce a dynamic weight assignment and configuration model to enhance system accuracy using a neural network based on backpropagation with gradient descent optimization techniques to minimize errors in the machine learning model. The w-SMNO also incorporates a distinct algorithm for effective relay selection among multiple monkey spider groups. The simulation results demonstrate substantial improvements in w-SMNO, with a 35.7% increase in coverage, a 41.2% reduction in the end-to-end delay, a 36.4% improvement in the message delivery rate, and a 38.4% decrease in the collision rate compared to the state-of-the-art approaches.

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Section: Related Workmentioning

confidence: 99%

A Novel Spider Monkey Optimization for Reliable Data Dissemination in VANETs Based on Machine Learning

Gupta,

Rathi

2024

Sensors

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“…The simulation gives the provision to choose sender, receiver, intermediate nodes and their communication technology. Ambulances are chosen as the sender for this simulation study along with a VANET-based telemedicine architecture, with IEEE 802.11 as the radio access technology (RAT) [54][55][56]. The mobile ambulances are simulated to traverse through different types of traffic scenarios experienced in rural, suburban, and urban areas, totalling a 6km road stretch.…”

Section: Simulation Scenariosmentioning

confidence: 99%

A QoS-Aware IoT Edge Network for Mobile Telemedicine Enabling In-Transit Monitoring of Emergency Patients

Mukhopadhyay,

Remanidevi Devidas,

Rangan

et al. 2024

Future Internet

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Addressing the inadequacy of medical facilities in rural communities and the high number of patients affected by ailments that need to be treated immediately is of prime importance for all countries. The various recent healthcare emergency situations bring out the importance of telemedicine and demand rapid transportation of patients to nearby hospitals with available resources to provide the required medical care. Many current healthcare facilities and ambulances are not equipped to provide real-time risk assessment for each patient and dynamically provide the required medical interventions. This work proposes an IoT-based mobile medical edge (IM2E) node to be integrated with wearable and portable devices for the continuous monitoring of emergency patients transported via ambulances and it delves deeper into the existing challenges, such as (a) a lack of a simplified patient risk scoring system, (b) the need for architecture that enables seamless communication for dynamically varying QoS requirements, and (c)the need for context-aware knowledge regarding the effect of end-to-end delay and the packet loss ratio (PLR) on the real-time monitoring of health risks in emergency patients. The proposed work builds a data path selection model to identify the most effective path through which to route the data packets in an effective manner. The signal-to-noise interference ratio and the fading in the path are chosen to analyze the suitable path for data transmission.

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“…RSUs are responsible for the optimization procedure. Authors in [32] proposed an effective clustering routing scheme for VANETs based on Density Peaks Clustering (DPC) and PSO. This method makes use of a clustering algorithm that combines the benefits of the PSO and DPC algorithms.…”

Section: Related Workmentioning

confidence: 99%

SOMACA: A New Swarm Optimization-Based and Mobility-Aware Clustering Approach for the Internet of Vehicles

et al. 2023

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The Internet of Vehicles (IoV) has evolved from the classic Vehicular Ad-hoc NETworks (VANETs) as a result of the emergence of the Internet of Things (IoT). IoV is used for communication among vehicles in real-time with their drivers, other vehicles, pedestrians, fleet management systems, and roadside infrastructure. High vehicular speeds and frequent network topology changes make vehicle communication extremely difficult on the IoV network. More constraints are imposed on IoV communication performance in a huge network environment due to the difficult road conditions and the enormous quantity of vehicles. A promising approach to improve the IoV communication performance is through vehicle clustering. Minimizing the number of clusters and identifying a reliable Cluster head (CH) are some of the most challenging tasks. In this paper, we propose a Swarm optimization-based and mobility-aware clustering method termed SOMACA. SOMACA consists of two phases clustering phase and the routing phase. During the clustering phase, we combine mobility measures and cluster distance to generate the minimum number of clusters having stable CHs and employ the Sparrow Search algorithm (SSA) for CH selection. The routing phase consists of two steps (1) Route Formation and (2) Route Upkeep. The main target for route formation step is to build a secure routing path between IoV nodes and base station (BS) by establishing an optimal list of links that are ordered from high to low, and in each round, it selects the best one. Moreover, the Upkeep step aims to update and maintain the existing connection. The performance of SOMACA is assessed using simulation experiments with various metrics including average cluster lifetime, transmission range, and network grid size. The simulation results show that SOMACA reduces the average number of clusters by 42%, 48%, 47%, 9%, 22%,31%, 16%, and 43% less than CAVDO, GOA, GWOCNET, MFCA-IOV, MOGA-AWCP, HHOCNET, AMONE, and p-WOA algorithms respectively when the transmission ranges vary between 200 and 500. Moreover, SOMACA increases the average network lifetime by 11%, 12%, 9%, 6%, 7%, 9%, 3%, and 13% longer than the mentioned above compared algorithms, respectively.INDEX TERMS Clustering, the Internet of Things, the Internet of Vehicles, swarm intelligence, sparrow search algorithm, routing data, VANET.The associate editor coordinating the review of this manuscript and approving it for publication was Chan Hwang See.

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An Intelligent Machine Learning Based Routing Scheme for VANET

Cited by 27 publications

References 38 publications

A Novel Spider Monkey Optimization for Reliable Data Dissemination in VANETs Based on Machine Learning

A Novel Spider Monkey Optimization for Reliable Data Dissemination in VANETs Based on Machine Learning

A QoS-Aware IoT Edge Network for Mobile Telemedicine Enabling In-Transit Monitoring of Emergency Patients

SOMACA: A New Swarm Optimization-Based and Mobility-Aware Clustering Approach for the Internet of Vehicles

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